Raja Jurdak

Dimity Miller, Peyman Moghadam, Mark Cox et al.

In object detection, false negatives arise when a detector fails to detect a target object. To understand why object detectors produce false negatives, we identify five 'false negative mechanisms', where each mechanism describes how a specific component inside the detector architecture failed. Focusing on two-stage and one-stage anchor-box object detector architectures, we introduce a framework for quantifying these false negative mechanisms. Using this framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects in benchmark vision datasets and robotics datasets. We show that a detector's false negative mechanisms differ significantly between computer vision benchmark datasets and robotics deployment scenarios. This has implications for the translation of object detectors developed for benchmark datasets to robotics applications. Code is publicly available at https://github.com/csiro-robotics/fn_mechanisms

Baofu Han, Bing Li, Yining Qi et al.

Privacy-Preserving Federated Learning (PPFL) has emerged as a secure distributed Machine Learning (ML) paradigm that aggregates locally trained gradients without exposing raw data. To defend against model poisoning threats, several robustness-enhanced PPFL schemes have been proposed by integrating anomaly detection. Nevertheless, they still face two major challenges: (1) the reliance on heavyweight encryption techniques results in substantial communication and computation overhead; and (2) single-strategy defense mechanisms often fail to provide sufficient robustness against adaptive adversaries. To overcome these challenges, we propose DP2Guard, a lightweight PPFL framework that enhances both privacy and robustness. DP2Guard leverages a lightweight gradient masking mechanism to replace costly cryptographic operations while ensuring the privacy of local gradients. A hybrid defense strategy is proposed, which extracts gradient features using singular value decomposition and cosine similarity, and applies a clustering algorithm to effectively identify malicious gradients. Additionally, DP2Guard adopts a trust score-based adaptive aggregation scheme that adjusts client weights according to historical behavior, while blockchain records aggregated results and trust scores to ensure tamper-proof and auditable training. Extensive experiments conducted on two public datasets demonstrate that DP2Guard effectively defends against four advanced poisoning attacks while ensuring privacy with reduced communication and computation costs.

Kealan Dunnett, Reza Arablouei, Dimity Miller et al.

Backdoor attacks can implant malicious behaviours into deep models while preserving performance on clean data, posing a serious threat to safety-critical vision systems. Although backdoor mitigation has been studied extensively for image classification, defenses for object detection remain comparatively underdeveloped. Adversarial fine-tuning is a common backdoor mitigation approach in classification, but adapting it to detection is nontrivial as classification-oriented adversarial generation does not match the detection attack space, where attacks may cause object misclassification or disappearance, and standard detection losses can dilute the repair signal across many predictions. We address these challenges through a detection-aware adversarial fine-tuning framework for mitigating object-detection backdoors when the defender has access only to a compromised detector and a small clean dataset, without knowing the attack objective. For adversarial generation that does not require knowledge of the attack objective, we introduce soft-branch minimisation, which uses a soft gate to combine objectives aligned with misclassification and disappearance attacks, together with a detection-aware classification-loss maximisation. For targeted repair, we introduce a dual-objective fine-tuning loss applied to target-matched predictions, concentrating the defensive update on predictions most relevant to the backdoor behaviour. Experiments across CNN- and Transformer-based detectors show that our approach more effectively reduces attack success while preserving true detections, compared with classification-oriented baselines, and maintains competitive clean detection performance.

Sk Tanzir Mehedi, Raja Jurdak, Chadni Islam et al.

The security of open-source software repositories is increasingly threatened by next-gen software supply chain attacks. These attacks include multiphase malware execution, remote access activation, and dynamic payload generation. Traditional Machine Learning (ML) detectors struggle to detect these attacks due to the high-dimensional and sparse nature of dynamic behavioral data, including system calls, network traffic, directory access patterns, and dependency logs. As a result, these data characteristics degrade the performance, stability, and explainability of ML models. These challenges have made Deep Learning (DL) a promising alternative, given its success across various domains and its potential for modeling complex patterns. This paper presents eDySec, a DL-based efficient, stable, and explainable framework for dynamic behavioral analysis to detect malicious packages. Using the QUT-DV25 dataset, which captures both install-time and post-installation behaviors of packages, we evaluate DL models and investigate feature sets to identify the most discriminative attributes for enabling efficient malicious package detection. Additionally, model stability analysis and explainable AI techniques are incorporated into the detection pipeline to enable stable, and transparent interpretations of model decisions. Experimental results demonstrate that eDySec significantly outperforms the state-of-the-art frameworks. Specifically, it halves feature dimensionality while lowering false positives by 82% and false negatives by 79%. It also improves accuracy by 3%, achieves near-perfect stability, and maintains an inference latency of 170ms per package. Further analysis reveals that feature and model selection play a critical role, as certain combinations degrade performance. Ultimately, this study advances the understanding of the strengths and limitations of dynamic analysis against next-gen attacks.

Md Mahfujur Rahmana, Alistair Barros, Raja Jurdak et al.

Peer-to-peer energy trading among electric vehicles (EVs) has been increasingly studied as a promising solution for improving supply-side resilience under growing charging demand and constrained charging infrastructure. While prior studies on EV-EV energy trading and related EV research have largely focused on transaction management or isolated mobility prediction tasks, the problem of identifying which charging nodes are more suitable for EV-EV trading in journey contexts remains open. We address this gap by formulating next charging nodes recommendation as a learning-to-rank problem, where each EV decision event is associated with a set of candidate charging locations. We propose a supervised ranking framework applied to a large-scale urban EV mobility dataset comprising millions of journey records and multidimensional EV trading-related features, including EV energy level, trading role, distance to charging locations, charging speed, and temporal station popularity. To account for uncertainty arising from the mobility of both energy providers and consumers, as well as the presence of multiple viable charging nodes at a decision point, we employ probabilistic relevance refinement to generate graded labels for ranking. We evaluate gradient-boosted learning-to-rank models, including LightGBM, XGBoost, and CatBoost, on EV journey records enriched with candidate charging nodes. Experimental results show that LightGBM consistently achieves the strongest ranking performance across standard metrics, including NDCG@k, Recall@k, and MRR, with particularly strong early-ranking quality, reflected in the highest NDCG@1 (0.9795) and MRR (0.9990). These results highlight the effectiveness of uncertainty-aware learning-to-rank for charging node recommendation and support improved coordination and matching in decentralized EV-EV energy trading systems.

Robert Marlin, Raja Jurdak, Alsharif Abuadbba et al.

By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy demands, leaving sensitive mobility data vulnerable. To address this, we developed a Federated Learning Transformer Network (FLTN) to predict EVs' next charge location with enhanced privacy measures. Each EV operates as a client, training an onboard FLTN model that shares only model weights, not raw data with a community-based Distributed Energy Resource Management System (DERMS), which aggregates them into a community global model. To further enhance privacy, non-transitory EVs use peer-to-peer weight sharing and augmentation within their community, obfuscating individual contributions and improving model accuracy. Community DERMS global model weights are then redistributed to EVs for continuous training. Our FLTN approach achieved up to 92% accuracy while preserving data privacy, compared to our baseline centralised model, which achieved 98% accuracy with no data privacy. Simulations conducted across diverse charge levels confirm the FLTN's ability to forecast energy demands over extended periods. We present a privacy-focused solution for forecasting EV charge location prediction, effectively mitigating data leakage risks.

Kealan Dunnett, Reza Arablouei, Dimity Miller et al.

The widespread adoption of deep learning across various industries has introduced substantial challenges, particularly in terms of model explainability and security. The inherent complexity of deep learning models, while contributing to their effectiveness, also renders them susceptible to adversarial attacks. Among these, backdoor attacks are especially concerning, as they involve surreptitiously embedding specific triggers within training data, causing the model to exhibit aberrant behavior when presented with input containing the triggers. Such attacks often exploit vulnerabilities in outsourced processes, compromising model integrity without affecting performance on clean (trigger-free) input data. In this paper, we present a comprehensive review of existing mitigation strategies designed to counter backdoor attacks in image recognition. We provide an in-depth analysis of the theoretical foundations, practical efficacy, and limitations of these approaches. In addition, we conduct an extensive benchmarking of sixteen state-of-the-art approaches against eight distinct backdoor attacks, utilizing three datasets, four model architectures, and three poisoning ratios. Our results, derived from 122,236 individual experiments, indicate that while many approaches provide some level of protection, their performance can vary considerably. Furthermore, when compared to two seminal approaches, most newer approaches do not demonstrate substantial improvements in overall performance or consistency across diverse settings. Drawing from these findings, we propose potential directions for developing more effective and generalizable defensive mechanisms in the future.

Kealan Dunnett, Reza Arablouei, Dimity Miller et al.

In the era of increasing concerns over cybersecurity threats, defending against backdoor attacks is paramount in ensuring the integrity and reliability of machine learning models. However, many existing approaches require substantial amounts of data for effective mitigation, posing significant challenges in practical deployment. To address this, we propose a novel approach to counter backdoor attacks by treating their mitigation as an unlearning task. We tackle this challenge through a targeted model pruning strategy, leveraging unlearning loss gradients to identify and eliminate backdoor elements within the model. Built on solid theoretical insights, our approach offers simplicity and effectiveness, rendering it well-suited for scenarios with limited data availability. Our methodology includes formulating a suitable unlearning loss and devising a model-pruning technique tailored for convolutional neural networks. Comprehensive evaluations demonstrate the efficacy of our proposed approach compared to state-of-the-art approaches, particularly in realistic data settings.

Kealan Dunnett, Reza Arablouei, Dimity Miller et al.

Backdoor attacks pose a severe threat to deep learning, yet their impact on object detection remains poorly understood compared to image classification. While attacks have been proposed, we identify critical weaknesses in existing detection-based methods, specifically their reliance on unrealistic assumptions and a lack of physical validation. To bridge this gap, we introduce BadDet+, a penalty-based framework that unifies Region Misclassification Attacks (RMA) and Object Disappearance Attacks (ODA). The core mechanism utilizes a log-barrier penalty to suppress true-class predictions for triggered inputs, resulting in (i) position and scale invariance, and (ii) enhanced physical robustness. On real-world benchmarks, BadDet+ achieves superior synthetic-to-physical transfer compared to existing RMA and ODA baselines while preserving clean performance. Theoretical analysis confirms the proposed penalty acts within a trigger-specific feature subspace, reliably inducing attacks without degrading standard inference. These results highlight significant vulnerabilities in object detection and the necessity for specialized defenses.

Kealan Dunnett, Reza Arablouei, Dimity Miller et al.

Model pruning has gained traction as a promising defense strategy against backdoor attacks in deep learning. However, existing pruning-based approaches often fall short in accurately identifying and removing the specific parameters responsible for inducing backdoor behaviors. Despite the dominance of fine-tuning-based defenses in recent literature, largely due to their superior performance, pruning remains a compelling alternative, offering greater interpretability and improved robustness in low-data regimes. In this paper, we propose a novel pruning approach featuring a learned \emph{selection} mechanism to identify parameters critical to both main and backdoor tasks, along with an \emph{invertible} pruning mask designed to simultaneously achieve two complementary goals: eliminating the backdoor task while preserving it through the inverse mask. We formulate this as a bi-level optimization problem that jointly learns selection variables, a sparse invertible mask, and sample-specific backdoor perturbations derived from clean data. The inner problem synthesizes candidate triggers using the inverse mask, while the outer problem refines the mask to suppress backdoor behavior without impairing clean-task accuracy. Extensive experiments demonstrate that our approach outperforms existing pruning-based backdoor mitigation approaches, maintains strong performance under limited data conditions, and achieves competitive results compared to state-of-the-art fine-tuning approaches. Notably, the proposed approach is particularly effective in restoring correct predictions for compromised samples after successful backdoor mitigation.

Robert Marlin, Raja Jurdak, Alsharif Abuadbba

The widespread adoption of Electric Vehicles (EVs) poses critical challenges for energy providers, particularly in predicting charging time (temporal prediction), ensuring user privacy, and managing resources efficiently in mobility-driven networks. This paper introduces the Hierarchical Federated Learning Transformer Network (H-FLTN) framework to address these challenges. H-FLTN employs a three-tier hierarchical architecture comprising EVs, community Distributed Energy Resource Management Systems (DERMS), and the Energy Provider Data Centre (EPDC) to enable accurate spatio-temporal predictions of EV charging needs while preserving privacy. Temporal prediction is enhanced using Transformer-based learning, capturing complex dependencies in charging behavior. Privacy is ensured through Secure Aggregation, Additive Secret Sharing, and Peer-to-Peer (P2P) Sharing with Augmentation, which allow only secret shares of model weights to be exchanged while securing all transmissions. To improve training efficiency and resource management, H-FLTN integrates Dynamic Client Capping Mechanism (DCCM) and Client Rotation Management (CRM), ensuring that training remains both computationally and temporally efficient as the number of participating EVs increases. DCCM optimises client participation by limiting excessive computational loads, while CRM balances training contributions across epochs, preventing imbalanced participation. Our simulation results based on large-scale empirical vehicle mobility data reveal that DCCM and CRM reduce the training time complexity with increasing EVs from linear to constant. Its integration into real-world smart city infrastructure enhances energy demand forecasting, resource allocation, and grid stability, ensuring reliability and sustainability in future mobility ecosystems.

Sabah Suhail, Raja Jurdak, Rasheed Hussain

Digital twins, being the virtual replicas of their physical counterparts, share valuable knowledge of the underlying system. Therefore, they might become a potential source of data breaches and a playground for attackers to launch covert attacks. It is imperative to investigate necessary countermeasures to mitigate such attacks.

Ali Dorri, Clemence Roulin, Shantanu Pal et al.

In recent years blockchain technology has received tremendous attention. Blockchain users are known by a changeable Public Key (PK) that introduces a level of anonymity, however, studies have shown that anonymized transactions can be linked to deanonymize the users. Most of the existing studies on user de-anonymization focus on monetary applications, however, blockchain has received extensive attention in non-monetary applications like IoT. In this paper we study the impact of de-anonymization on IoT-based blockchain. We populate a blockchain with data of smart home devices and then apply machine learning algorithms in an attempt to classify transactions to a particular device that in turn risks the privacy of the users. Two types of attack models are defined: (i) informed attacks: where attackers know the type of devices installed in a smart home, and (ii) blind attacks: where attackers do not have this information. We show that machine learning algorithms can successfully classify the transactions with 90% accuracy. To enhance the anonymity of the users, we introduce multiple obfuscation methods which include combining multiple packets into a transaction, merging ledgers of multiple devices, and delaying transactions. The implementation results show that these obfuscation methods significantly reduce the attack success rates to 20% to 30% and thus enhance user privacy.

Sabah Suhail, Saif Ur Rehman Malik, Raja Jurdak et al.

The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate critical infrastructures' operational behaviour and security without affecting the operation of live systems. In this regard, Digital Twins (DTs) provide actionable insights through monitoring, simulating, predicting, and optimizing the state of CPSs. Through the use cases, including system testing and training, detecting system misconfigurations, and security testing, DTs strengthen the security of CPSs throughout the product lifecycle. However, such benefits of DTs depend on an assumption about data integrity and security. Data trustworthiness becomes more critical while integrating multiple components among different DTs owned by various stakeholders to provide an aggregated view of the complex physical system. This article envisions a blockchain-based DT framework as Trusted Twins for Securing Cyber-Physical Systems (TTS-CPS). With the automotive industry as a CPS use case, we demonstrate the viability of the TTS-CPS framework in a proof of concept. To utilize reliable system specification data for building the process knowledge of DTs, we ensure the trustworthiness of data-generating sources through integrity checking mechanisms. Additionally, the safety and security rules evaluated during simulation are stored and retrieved from the blockchain, thereby establishing more understanding and confidence in the decisions made by the underlying systems. Finally, we perform formal verification of the TTS-CPS.

Rodrigo Dutra Garcia, Gowri Sankar Ramachandran, Raja Jurdak et al.

Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, and consent management. We introduce a decentralized data governance framework based on blockchain technology and proxy re-encryption to let data owners control and track their data through privacy-enhancing and consent management mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We have used Digital e-prescription as the use case since it has multiple stakeholders and sensitive data while enabling the medical fraternity to manage patients' prescription data, involving patients as data owners, doctors and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Ethereum, and pyUmbral PRE show that the proposed decentralized system guarantees transparency, privacy, and trust with minimal overhead.

Kathy Nguyen, Shantanu Pal, Zahra Jadidi et al.

In recent years Industrial Control Systems (ICS) have been targeted increasingly by sophisticated cyberattacks. Improving ICS security has drawn significant attention in the literature that emphasises the importance of Cyber Threat Intelligence (CTI) sharing in accelerating detection, mitigation, and prevention of cyberattacks. However, organisations are reluctant to exchange CTI due to fear of exposure, reputational damage, and lack of incentives. Furthermore, there has been limited discussion about the factors influencing participation in sharing CTI about ICS. The existing CTI-sharing platforms rely on centralised trusted architectures that suffer from a single point of failure and risk companies' privacy as the central node maintains CTI details. In this paper, we address the needs of organisations involved in the management and protection of ICS and present a novel framework that facilitates secure, private, and incentivised exchange of CTI related to ICS using blockchain. We propose a new blockchain-enabled framework that facilitates the secure dissemination of CTI data among multiple stakeholders in ICS. We provide the framework design, technical development and evaluate the framework's feasibility in a real-world application environment using practical use-case scenarios. Our proposed design shows a more practical and efficient framework for a CTI sharing network for ICS, including the bestowal and acknowledgment of data privacy, trust barriers, and security issues ingrained in this domain.

Guntur Dharma Putra, Volkan Dedeoglu, Abhinav Pathak et al.

Intrusion Detection Systems (IDS) have been the industry standard for securing IoT networks against known attacks. To increase the capability of an IDS, researchers proposed the concept of blockchain-based Collaborative-IDS (CIDS), wherein blockchain acts as a decentralised platform allowing collaboration between CIDS nodes to share intrusion related information, such as intrusion alarms and detection rules. However, proposals in blockchain-based CIDS overlook the importance of continuous evaluation of the trustworthiness of each node and generally work based on the assumption that the nodes are always honest. In this paper, we propose a decentralised CIDS that emphasises the importance of building trust between CIDS nodes. In our proposed solution, each CIDS node exchanges detection rules to help other nodes detect new types of intrusion. Our architecture offloads the trust computation to the blockchain and utilises a decentralised storage to host the shared trustworthy detection rules, ensuring scalability. Our implementation in a lab-scale testbed shows that the our solution is feasible and performs within the expected benchmarks of the Ethereum platform.

Seyedehfaezeh Hosseininoorbin, Siamak Layeghy, Brano Kusy et al.

This paper explores the performance of Google's Edge TPU on feed forward neural networks. We consider Edge TPU as a hardware platform and explore different architectures of deep neural network classifiers, which traditionally has been a challenge to run on resource constrained edge devices. Based on the use of a joint-time-frequency data representation, also known as spectrogram, we explore the trade-off between classification performance and the energy consumed for inference. The energy efficiency of Edge TPU is compared with that of widely-used embedded CPU ARM Cortex-A53. Our results quantify the impact of neural network architectural specifications on the Edge TPU's performance, guiding decisions on the TPU's optimal operating point, where it can provide high classification accuracy with minimal energy consumption. Also, our evaluations highlight the crossover in performance between the Edge TPU and Cortex-A53, depending on the neural network specifications. Based on our analysis, we provide a decision chart to guide decisions on platform selection based on the model parameters and context.

Guntur Dharma Putra, Volkan Dedeoglu, Salil S Kanhere et al.

The salient features of blockchain, such as decentralisation and transparency, have allowed the development of Decentralised Trust and Reputation Management Systems (DTRMS), which mainly aim to quantitatively assess the trustworthiness of the network participants and help to protect the network from adversaries. In the literature, proposals of DTRMS have been applied to various Cyber-physical Systems (CPS) applications, including supply chains, smart cities and distributed energy trading. In this chapter, we outline the building blocks of a generic DTRMS and discuss how it can benefit from blockchain. To highlight the significance of DTRMS, we present the state-of-the-art of DTRMS in various field of CPS applications. In addition, we also outline challenges and future directions in developing DTRMS for CPS.

Gowri Sankar Ramachandran, Sidra Malik, Shantanu Pal et al.

Supply chain applications operate in a multi-stakeholder setting, demanding trust, provenance, and transparency. Blockchain technology provides mechanisms to establish a decentralized infrastructure involving multiple stakeholders. Such mechanisms make the blockchain technology ideal for multi-stakeholder supply chain applications. This chapter introduces the characteristics and requirements of the supply chain and explains how blockchain technology can meet the demands of supply chain applications. In particular, this chapter discusses how data and trust management can be established using blockchain technology. The importance of scalability and interoperability in a blockchain-based supply chain is highlighted to help the stakeholders make an informed decision. The chapter concludes by underscoring the design challenges and open opportunities in the blockchain-based supply chain domain.

Shantanu Pal, Ali Dorri, Raja Jurdak

With the rapid development of wireless sensor networks, smart devices, and traditional information and communication technologies, there is tremendous growth in the use of Internet of Things (IoT) applications and services in our everyday life. IoT systems deal with high volumes of data. This data can be particularly sensitive, as it may include health, financial, location, and other highly personal information. Fine-grained security management in IoT demands effective access control. Several proposals discuss access control for the IoT, however, a limited focus is given to the emerging blockchain-based solutions for IoT access control. In this paper, we review the recent trends and critical needs for blockchain-based solutions for IoT access control. We identify several important aspects of blockchain, including decentralised control, secure storage and sharing information in a trustless manner, for IoT access control including their benefits and limitations. Finally, we note some future research directions on how to converge blockchain in IoT access control efficiently and effectively.

Ali Dorri, Shailesh Mishra, Raja Jurdak

Blockchain has received tremendous attention as a secure, distributed, and anonymous framework for the Internet of Things (IoT). As a distributed system, blockchain trades off scalability for distribution, which limits the technologys adaptation for large scale networks such as IoT. All transactions and blocks must be broadcast and verified by all participants which limits scalability and incurs computational and communication overheads. The existing solutions to scale blockchains have so far led to partial recentralization, limiting the technologys original appeal. In this paper, we introduce a distributed yet scalable Verification and Communication architecture for blockchain referred to as Vericom. Vericom concurrently achieves high scalability and distribution using hash function outputs to shift blockchains from broadcast to multicast communication. Unlike conventional blockchains where all nodes must verify new transactions/blocks, Vericom uses the hash of IoT traffic to randomly select a set of nodes to verify transactions/blocks which in turn reduces the processing overhead. Vericom incorporates two layers: i) transmission layer where a randomized multicasting method is introduced along with a backbone network to route traffic, i.e., transactions and blocks, from the source to the destination, and ii) verification layer where a set of randomly selected nodes are allocated to verify each transaction or block. The performance evaluation shows that Vericom reduces the packet and processing overhead as compared with conventional blockchains. In the worst case, packet overhead in Vericom scales linearly with the number of nodes while the processing overhead remains scale-independent.

Sidra Malik, Naman Gupta, Volkan Dedeoglu et al.

In this work, we propose a privacy-preservation framework, TradeChain, which decouples the trade events of participants using decentralised identities. TradeChain adopts the Self-Sovereign Identity (SSI) principles and makes the following novel contributions: a) it incorporates two separate ledgers: a public permissioned blockchain for maintaining identities and the permissioned blockchain for recording trade flows, b) it uses Zero Knowledge Proofs (ZKPs) on traders' private credentials to prove multiple identities on trade ledger and c) allows data owners to define dynamic access rules for verifying traceability information from the trade ledger using access tokens and Ciphertext Policy Attribute-Based Encryption (CP-ABE). A proof of concept implementation of TradeChain is presented on Hyperledger Indy and Fabric and an extensive evaluation of execution time, latency and throughput reveals minimal overheads.

Sabah Suhail, Raja Jurdak

The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate the operational behaviour and security without negatively affecting the operation of live systems. In this regard, Digital Twins (DTs) are revolutionizing the CPSs. DTs strengthen the security of CPSs throughout the product lifecycle, while assuming that the DT data is trusted, providing agility to predict and respond to real-time changes. However, existing DTs solutions in CPS are constrained with untrustworthy data dissemination among multiple stakeholders and timely course correction. Such limitations reinforce the significance of designing trustworthy distributed solutions with the ability to create actionable insights in real-time. To do so, we propose a framework that focuses on trusted and intelligent DT by integrating blockchain and Artificial Intelligence (AI). Following a hybrid approach, the proposed framework not only acquires process knowledge from the specifications of the CPS, but also relies on AI to learn security threats based on sensor data. Furthermore, we integrate blockchain to safeguard product lifecycle data. We discuss the applicability of the proposed framework for the automotive industry as a CPS use case. Finally, we identify the open challenges that impede the implementation of intelligence-driven architectures in CPSs.

Sidra Malik, Volkan Dedeoglu, Salil Kanhere et al.

Blockchain offers traceability and transparency to supply chain event data and hence can help overcome many challenges in supply chain management such as: data integrity, provenance and traceability. However, data privacy concerns such as the protection of trade secrets have hindered adoption of blockchain technology. Although consortium blockchains only allow authorised supply chain entities to read/write to the ledger, privacy preservation of trade secrets cannot be ascertained. In this work, we propose a privacy-preservation framework, PrivChain, to protect sensitive data on blockchain using zero knowledge proofs. PrivChain provides provenance and traceability without revealing any sensitive information to end-consumers or supply chain entities. Its novelty stems from: a) its ability to allow data owners to protect trade related information and instead provide proofs on the data, and b) an integrated incentive mechanism for entities providing valid proofs over provenance data. In particular, PrivChain uses Zero Knowledge Range Proofs (ZKRPs), an efficient variant of ZKPs, to provide origin information without disclosing the exact location of a supply chain product. Furthermore, the framework allows to compute proofs and commitments off-line, decoupling the computational overhead from blockchain. The proof verification process and incentive payment initiation are automated using blockchain transactions, smart contracts, and events. A proof of concept implementation on Hyperledger Fabric reveals a minimal overhead of using PrivChain for blockchain enabled supply chains.

Khondokar Fida Hasan, Anthony Overall, Keyvan Ansari et al.

The recent advancement of cloud technology offers unparallel strength to support intelligent computations and advanced services to assist with automated decisions to improve road transportation safety and comfort. Besides, the rise of machine intelligence propels the technological evolution of transportation systems one step further and leads to a new framework known as Cognitive Internet of Vehicles (C-IoV). The redefined cognitive technology in this framework promises significant enhancements and optimized network capacities compared with its predecessor framework, the Internet of Vehicles (IoV). CIoV offers additional security measures and introduces security and privacy concerns, such as evasion attacks, additional threats of data poisoning, and learning errors, which may likely lead to system failure and road user fatalities. Similar to many other public enterprise systems, transportation has a significant impact on the population. Therefore, it is crucial to understand the evolution and equally essential to identify potential security vulnerabilities and issues to offer mitigation towards success. This chapter offers discussions framing answers to the following two questions, 1) how and in what ways the penetration of the latest technologies are reshaping the transportation system? 2) whether the evolved system is capable of addressing the concerns of cybersecurity? This chapter, therefore, starts presenting the evolution of the transportation system followed by a quick overview of the evolved CIoV, highlighting the evolved cognitive design. Later it presents how a cognitive engine can overcome legacy security concerns and also be subjected to further potential security, privacy, and trust issues that this cloud-based evolved transportation system may encounter.

Guntur Dharma Putra, Volkan Dedeoglu, Salil S Kanhere et al.

Authorization or access control limits the actions a user may perform on a computer system, based on predetermined access control policies, thus preventing access by illegitimate actors. Access control for the Internet of Things (IoT) should be tailored to take inherent IoT network scale and device resource constraints into consideration. However, common authorization systems in IoT employ conventional schemes, which suffer from overheads and centralization. Recent research trends suggest that blockchain has the potential to tackle the issues of access control in IoT. However, proposed solutions overlook the importance of building dynamic and flexible access control mechanisms. In this paper, we design a decentralized attribute-based access control mechanism with an auxiliary Trust and Reputation System (TRS) for IoT authorization. Our system progressively quantifies the trust and reputation scores of each node in the network and incorporates the scores into the access control mechanism to achieve dynamic and flexible access control. We design our system to run on a public blockchain, but we separate the storage of sensitive information, such as user's attributes, to private sidechains for privacy preservation. We implement our solution in a public Rinkeby Ethereum test-network interconnected with a lab-scale testbed. Our evaluations consider various performance metrics to highlight the applicability of our solution for IoT contexts.

Seyedehfaezeh Hosseininoorbin, Siamak Layeghy, Mohanad Sarhan et al.

This paper explores Google's Edge TPU for implementing a practical network intrusion detection system (NIDS) at the edge of IoT, based on a deep learning approach. While there are a significant number of related works that explore machine learning based NIDS for the IoT edge, they generally do not consider the issue of the required computational and energy resources. The focus of this paper is the exploration of deep learning-based NIDS at the edge of IoT, and in particular the computational and energy efficiency. In particular, the paper studies Google's Edge TPU as a hardware platform, and considers the following three key metrics: computation (inference) time, energy efficiency and the traffic classification performance. Various scaled model sizes of two major deep neural network architectures are used to investigate these three metrics. The performance of the Edge TPU-based implementation is compared with that of an energy efficient embedded CPU (ARM Cortex A53). Our experimental evaluation shows some unexpected results, such as the fact that the CPU significantly outperforms the Edge TPU for small model sizes.

Sabah Suhail, Rasheed Hussain, Raja Jurdak et al.

Industrial processes rely on sensory data for decision-making processes, risk assessment, and performance evaluation. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the dissemination of trustworthy data. For the physical data to be trustworthy, it needs to be cross-validated through multiple sensor sources with overlapping fields of view. Cross-validated data can then be stored on the blockchain, to maintain its integrity and trustworthiness. Once trustworthy data is recorded on the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, Digital Twins (DTs) can be leveraged to draw intelligent conclusions from data by identifying the faults and recommending precautionary measures ahead of critical events. Empowering DTs with blockchain in industrial use-cases targets key challenges of disparate data repositories, untrustworthy data dissemination, and the need for predictive maintenance. In this survey, while highlighting the key benefits of using blockchain-based DTs, we present a comprehensive review of the state-of-the-art research results for blockchain-based DTs. Based on the current research trends, we discuss a trustworthy blockchain-based DTs framework. We highlight the role of Artificial Intelligence (AI) in blockchain-based DTs. Furthermore, we discuss current and future research and deployment challenges of blockchain-supported DTs that require further investigation.

Seyedeh Faezeh Hosseini Noorbin, Siamak Layeghy, Brano Kusy et al.

Automated cattle activity classification allows herders to continuously monitor the health and well-being of livestock, resulting in increased quality and quantity of beef and dairy products. In this paper, a sequential deep neural network is used to develop a behavioural model and to classify cattle behaviour and activities. The key focus of this paper is the exploration of a joint time-frequency domain representation of the sensor data, which is provided as the input to the neural network classifier. Our exploration is based on a real-world data set with over 3 million samples, collected from sensors with a tri-axial accelerometer, magnetometer and gyroscope, attached to collar tags of 10 dairy cows and collected over a one month period. The key results of this paper is that the joint time-frequency data representation, even when used in conjunction with a relatively basic neural network classifier, can outperform the best cattle activity classifiers reported in the literature. With a more systematic exploration of neural network classifier architectures and hyper-parameters, there is potential for even further improvements. Finally, we demonstrate that the time-frequency domain data representation allows us to efficiently trade-off a large reduction of model size and computational complexity for a very minor reduction in classification accuracy. This shows the potential for our classification approach to run on resource-constrained embedded and IoT devices.

Sabah Suhail, Rasheed Hussain, Raja Jurdak et al.

Industrial processes rely on sensory data for critical decision-making processes. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the trustworthiness of data. To this end, we envision a blockchain-based framework for the Industrial Internet of Things (IIoT) to address the issues of data management and security. Once the data collected from trustworthy sources are recorded in the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, we leverage Digital Twins (DTs) that can draw intelligent conclusions from data by identifying the faults and recommending precautionary measures ahead of critical events. Furthermore, we discuss the integration of DTs and blockchain to target key challenges of disparate data repositories, untrustworthy data dissemination, and fault diagnosis. Finally, we identify outstanding challenges faced by the IIoT and future research directions while leveraging blockchain and DTs.

Zahra Jadidi, Ali Dorri, Raja Jurdak et al.

Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. Therefore, analysis of multiple security data can provide comprehensive and system-wide anomaly detection in industrial networks. In this paper, we propose an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWAT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors.

Chuka Oham, Regio Michelin, Salil S. Kanhere et al.

The ubiquity of connecting technologies in smart vehicles and the incremental automation of its functionalities promise significant benefits, including a significant decline in congestion and road fatalities. However, increasing automation and connectedness broadens the attack surface and heightens the likelihood of a malicious entity successfully executing an attack. In this paper, we propose a Blockchain based Framework for sEcuring smaRt vehicLes (B-FERL). B-FERL uses permissioned blockchain technology to tailor information access to restricted entities in the connected vehicle ecosystem. It also uses a challenge-response data exchange between the vehicles and roadside units to monitor the internal state of the vehicle to identify cases of in-vehicle network compromise. In order to enable authentic and valid communication in the vehicular network, only vehicles with a verifiable record in the blockchain can exchange messages. Through qualitative arguments, we show that B-FERL is resilient to identified attacks. Also, quantitative evaluations in an emulated scenario show that B-FERL ensures a suitable response time and required storage size compatible with realistic scenarios. Finally, we demonstrate how B-FERL achieves various important functions relevant to the automotive ecosystem such as trust management, vehicular forensics and secure vehicular networks.

Mohsen Khorasany, Ali Dorri, Reza Razzaghi et al.

Peer-to-Peer (P2P) energy trading can facilitate integration of a large number of small-scale producers and consumers into energy markets. Decentralized management of these new market participants is challenging in terms of market settlement, participant reputation and consideration of grid constraints. This paper proposes a blockchain-enabled framework for P2P energy trading among producer and consumer agents in a smart grid. A fully decentralized market settlement mechanism is designed, which does not rely on a centralized entity to settle the market and encourages producers and consumers to negotiate on energy trading with their nearby agents truthfully. To this end, the electrical distance of agents is considered in the pricing mechanism to encourage agents to trade with their neighboring agents. In addition, a reputation factor is considered for each agent, reflecting its past performance in delivering the committed energy. Before starting the negotiation, agents select their trading partners based on their preferences over the reputation and proximity of the trading partners. An Anonymous Proof of Location (A-PoL) algorithm is proposed that allows agents to prove their location without revealing their real identity. The practicality of the proposed framework is illustrated through several case studies, and its security and privacy are analyzed in detail.

Ali Dorri, Raja Jurdak

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger. Implementation results show that tree-chain is runnable on low resource devices and incurs low processing overhead, achieving near real-time transaction settlement.

Siddique Latif, Rajib Rana, Sara Khalifa et al.

Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practice and the performance of SER systems plummet against unforeseen data shifts. The design of robust models for accurate SER is challenging, which limits its use in practical applications. In this paper we propose a deeper neural network architecture wherein we fuse DenseNet, LSTM and Highway Network to learn powerful discriminative features which are robust to noise. We also propose data augmentation with our network architecture to further improve the robustness. We comprehensively evaluate the architecture coupled with data augmentation against (1) noise, (2) adversarial attacks and (3) cross-corpus settings. Our evaluations on the widely used IEMOCAP and MSP-IMPROV datasets show promising results when compared with existing studies and state-of-the-art models.

Siddique Latif, Muhammad Asim, Rajib Rana et al.

Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data. In this work, we propose a framework that utilises the mixup data augmentation scheme to augment the GAN in feature learning and generation. To show the effectiveness of the proposed framework, we present results for SER on (i) synthetic feature vectors, (ii) augmentation of the training data with synthetic features, (iii) encoded features in compressed representation. Our results show that the proposed framework can effectively learn compressed emotional representations as well as it can generate synthetic samples that help improve performance in within-corpus and cross-corpus evaluation.

Guntur Dharma Putra, Volkan Dedeoglu, Salil S Kanhere et al.

An Intrusion Detection System (IDS) aims to alert users of incoming attacks by deploying a detector that monitors network traffic continuously. As an effort to increase detection capabilities, a set of independent IDS detectors typically work collaboratively to build intelligence of holistic network representation, which is referred to as Collaborative Intrusion Detection System (CIDS). However, developing an effective CIDS, particularly for the IoT ecosystem raises several challenges. Recent trends and advances in blockchain technology, which provides assurance in distributed trust and secure immutable storage, may contribute towards the design of effective CIDS. In this poster abstract, we present our ongoing work on a decentralized CIDS for IoT, which is based on blockchain technology. We propose an architecture that provides accountable trust establishment, which promotes incentives and penalties, and scalable intrusion information storage by exchanging bloom filters. We are currently implementing a proof-of-concept of our modular architecture in a local test-bed and evaluate its effectiveness in detecting common attacks in IoT networks and the associated overhead.

Pooja Gupta, Volkan Dedeoglu, Kamran Najeebullah et al.

Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices.

Siddique Latif, Rajib Rana, Sara Khalifa et al.

Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions. There are two main drawbacks to this approach: firstly, the feature engineering being manual is cumbersome and requires human knowledge; and secondly, the designed features might not be best for the objective at hand. This has motivated the adoption of a recent trend in speech community towards utilisation of representation learning techniques, which can learn an intermediate representation of the input signal automatically that better suits the task at hand and hence lead to improved performance. The significance of representation learning has increased with advances in deep learning (DL), where the representations are more useful and less dependent on human knowledge, making it very conducive for tasks like classification, prediction, etc. The main contribution of this paper is to present an up-to-date and comprehensive survey on different techniques of speech representation learning by bringing together the scattered research across three distinct research areas including Automatic Speech Recognition (ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER). Recent reviews in speech have been conducted for ASR, SR, and SER, however, none of these has focused on the representation learning from speech -- a gap that our survey aims to bridge.

Guntur Dharma Putra, Volkan Dedeoglu, Salil S. Kanhere et al.

Heterogeneous and dynamic IoT environments require a lightweight, scalable, and trustworthy access control system for protection from unauthorized access and for automated detection of compromised nodes. Recent proposals in IoT access control systems have incorporated blockchain to overcome inherent issues in conventional access control schemes. However, the dynamic interaction of IoT networks remains uncaptured. Here, we develop a blockchain based Trust and Reputation System (TRS) for IoT access control, which progressively evaluates and calculates the trust and reputation score of each participating node to achieve a self-adaptive and trustworthy access control system. Trust and reputation are explicitly incorporated in the attribute-based access control policy, so that different nodes can be assigned to different access right levels, resulting in dynamic access control policies. We implement our proposed architecture in a private Ethereum blockchain comprised of a Docker container network. We benchmark our solution using various performance metrics to highlight its applicability for IoT contexts.

Volkan Dedeoglu, Ali Dorri, Raja Jurdak et al.

Cyberphysical Systems (CPS) are transforming the way we interact with the physical world around us. However, centralised approaches for CPS systems are not capable of addressing the unique challenges of CPS due to the complexity, constraints, and dynamic nature of the interactions. To realize the true potential of CPS, a decentralized approach that takes into account these unique features is required. Recently, blockchain-based solutions have been proposed to address CPS challenges.Yet, applying blockchain for diverse CPS domains is not straight-forward and has its own challenges. In this paper, we share our experiences in applying blockchain technology for CPS to provide insights and highlight the challenges and future opportunities.

Siddique Latif, Rajib Rana, Sara Khalifa et al.

Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art accuracy is quite low and needs improvement to make commercial applications of SER viable. A key underlying reason for the low accuracy is the scarcity of emotion datasets, which is a challenge for developing any robust machine learning model in general. In this paper, we propose a solution to this problem: a multi-task learning framework that uses auxiliary tasks for which data is abundantly available. We show that utilisation of this additional data can improve the primary task of SER for which only limited labelled data is available. In particular, we use gender identifications and speaker recognition as auxiliary tasks, which allow the use of very large datasets, e.g., speaker classification datasets. To maximise the benefit of multi-task learning, we further use an adversarial autoencoder (AAE) within our framework, which has a strong capability to learn powerful and discriminative features. Furthermore, the unsupervised AAE in combination with the supervised classification networks enables semi-supervised learning which incorporates a discriminative component in the AAE unsupervised training pipeline. This semi-supervised learning essentially helps to improve generalisation of our framework and thus leads to improvements in SER performance. The proposed model is rigorously evaluated for categorical and dimensional emotion, and cross-corpus scenarios. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on two publicly available datasets.

Volkan Dedeoglu, Raja Jurdak, Guntur D. Putra et al.

Blockchain is a promising technology for establishing trust in IoT networks, where network nodes do not necessarily trust each other. Cryptographic hash links and distributed consensus mechanisms ensure that the data stored on an immutable blockchain can not be altered or deleted. However, blockchain mechanisms do not guarantee the trustworthiness of data at the origin. We propose a layered architecture for improving the end-to-end trust that can be applied to a diverse range of blockchain-based IoT applications. Our architecture evaluates the trustworthiness of sensor observations at the data layer and adapts block verification at the blockchain layer through the proposed data trust and gateway reputation modules. We present the performance evaluation of the data trust module using a simulated indoor target localization and the gateway reputation module using an end-to-end blockchain implementation, together with a qualitative security analysis for the architecture.

Sidra Malik, Volkan Dedeoglu, Salil S. Kanhere et al.

Traceability and integrity are major challenges for the increasingly complex supply chains of today's world. Although blockchain technology has the potential to address these challenges through providing a tamper-proof audit trail of supply chain events and data associated with a product life-cycle, it does not solve the trust problem associated with the data itself. Reputation systems are an effective approach to solve this trust problem. However, current reputation systems are not suited to the blockchain based supply chain applications as they are based on limited observations, they lack granularity and automation, and their overhead has not been explored. In this work, we propose TrustChain, as a three-layered trust management framework which uses a consortium blockchain to track interactions among supply chain participants and to dynamically assign trust and reputation scores based on these interactions. The novelty of TrustChain stems from: (a) the reputation model that evaluates the quality of commodities, and the trustworthiness of entities based on multiple observations of supply chain events, (b) its support for reputation scores that separate between a supply chain participant and products, enabling the assignment of product-specific reputations for the same participant, (c) the use of smart contracts for transparent, efficient, secure, and automated calculation of reputation scores, and (d) its minimal overhead in terms of latency and throughput when compared to a simple blockchain based supply chain model.

Chuka Oham, Raja Jurdak, Sanjay Jha

Fully autonomous vehicles are emerging vehicular technologies that have gained significant attention in todays research endeavours. Even though it promises to optimize road safety, the proliferation of wireless and sensor technologies makes it susceptible to cyber threats thus dawdling its adoption. The identification of threats and design of apposite security solutions is therefore pertinent to expedite its adoption. In this paper, we analyse the security risks of the communication infrastructure for the fully autonomous vehicle using a subset of the TVRA methodology by ETSI. We described the model of communication infrastructure. This model clarifies the potential communication possibilities of the vehicle. Then we defined the security objectives and identified threats. Furthermore, we classified risks and propose countermeasures to facilitate the design of security solutions. We find that all identified high impact threats emanates from a particular source and required encryption mechanisms as countermeasures. Finally, we discovered that all threats due to an interaction with humans are of serious consequences.

Siddique Latif, Rajib Rana, Sara Khalifa et al.

Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not always guaranteed to be the best in a statistical modelling framework where the end goal is for example emotion classification. This has fuelled the emerging trend of learning representations from raw speech especially using deep learning neural networks. In particular, a combination of Convolution Neural Networks (CNNs) and Long Short Term Memory (LSTM) have gained great traction for the intrinsic property of LSTM in learning contextual information crucial for emotion recognition; and CNNs been used for its ability to overcome the scalability problem of regular neural networks. In this paper, we show that there are still opportunities to improve the performance of emotion recognition from the raw speech by exploiting the properties of CNN in modelling contextual information. We propose the use of parallel convolutional layers to harness multiple temporal resolutions in the feature extraction block that is jointly trained with the LSTM based classification network for the emotion recognition task. Our results suggest that the proposed model can reach the performance of CNN trained with hand-engineered features from both IEMOCAP and MSP-IMPROV datasets.

Ali Dorri, Clemence Roulin, Raja Jurdak et al.

Security is one of the fundamental challenges in the Internet of Things (IoT) due to the heterogeneity and resource constraints of the IoT devices. Device classification methods are employed to enhance the security of IoT by detecting unregistered devices or traffic patterns. In recent years, blockchain has received tremendous attention as a distributed trustless platform to enhance the security of IoT. Conventional device identification methods are not directly applicable in blockchain-based IoT as network layer packets are not stored in the blockchain. Moreover, the transactions are broadcast and thus have no destination IP address and contain a public key as the user identity, and are stored permanently in blockchain which can be read by any entity in the network. We show that device identification in blockchain introduces privacy risks as the malicious nodes can identify users' activity pattern by analyzing the temporal pattern of their transactions in the blockchain. We study the likelihood of classifying IoT devices by analyzing their information stored in the blockchain, which to the best of our knowledge, is the first work of its kind. We use a smart home as a representative IoT scenario. First, a blockchain is populated according to a real-world smart home traffic dataset. We then apply machine learning algorithms on the data stored in the blockchain to analyze the success rate of device classification, modeling both an informed and a blind attacker. Our results demonstrate success rates over 90\% in classifying devices. We propose three timestamp obfuscation methods, namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices. The proposed timestamp obfuscation methods can reduce the classification success rates to as low as 20%.

Ali Dorri, Fengji Luo, Salil S Kanhere et al.

Security and privacy in Direct Load Control (DLC) is a fundamental challenge in smart grids. In this paper, we propose a blockchain-based framework to increase security and privacy of DLC. We propose a method whereby participating nodes share their data with the distribution company in an anonymous and secure manner. To reduce the associated overhead for data dissemination, we propose a hash-based transaction generation method. We also outline the DLC process for managing the load in consumer site. Qualitative analysis demonstrates the security and privacy of the proposed method.

Ali Dorri, Ambrose Hill, Salil S Kanhere et al.

Blockchain is increasingly being used as a distributed, anonymous, trustless framework for energy trading in smart grids. However, most of the existing solutions suffer from reliance on Trusted Third Parties (TTP), lack of privacy, and traffic and processing overheads. In our previous work, we have proposed a Secure Private Blockchain-based framework (SPB) for energy trading to address the aforementioned challenges. In this paper, we present a proof-on-concept implementation of SPB on the Ethereum private network to demonstrates SPB's applicability for energy trading. We benchmark SPB's performance against the relevant state-of-the-art. The implementation results demonstrate that SPB incurs lower overheads and monetary cost for end users to trade energy compared to existing solutions.