Kemal Akkaya

Muhammad A. Hakim, Hidayet Aksu, A. Selcuk Uluagac et al.

The ubiquitous nature of the IoT devices has brought serious security implications to its users. A lot of consumer IoT devices have little to no security implementation at all, thus risking user's privacy and making them target of mass cyber-attacks. Indeed, recent outbreak of Mirai botnet and its variants have already proved the lack of security on the IoT world. Hence, it is important to understand the security issues and attack vectors in the IoT domain. Though significant research has been done to secure traditional computing systems, little focus was given to the IoT realm. In this work, we reduce this gap by developing a honeypot framework for IoT devices. Specifically, we introduce U-PoT: a novel honeypot framework for capturing attacks on IoT devices that use Universal Plug and Play (UPnP) protocol. A myriad of smart home devices including smart switches, smart bulbs, surveillance cameras, smart hubs, etc. uses the UPnP protocol. Indeed, a simple search on Shodan IoT search engine lists 1,676,591 UPnP devices that are exposed to public network. The popularity and ubiquitous nature of UPnP-based IoT device necessitates a full-fledged IoT honeypot system for UPnP devices. Our novel framework automatically creates a honeypot from UPnP device description documents and is extendable to any device types or vendors that use UPnP for communication. To the best of our knowledge, this is the first work towards a flexible and configurable honeypot framework for UPnP-based IoT devices. We released U-PoT under an open source license for further research and created a database of UPnP device descriptions. We also evaluated our framework on two emulated deices. Our experiments show that the emulated devices are able to mimic the behavior of a real IoT device and trick vendor-provided device management applications or popular IoT search engines while having minimal performance ovherhead.

Andrew Quijano, Spyros T. Halkidis, Kevin Gallagher et al.

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

Ahmet Kurt, Enes Erdin, Kemal Akkaya et al.

While various covert botnets were proposed in the past, they still lack complete anonymization for their servers/botmasters or suffer from slow communication between the botmaster and the bots. In this paper, we first propose a new generation hybrid botnet that covertly and efficiently communicates over Bitcoin Lightning Network (LN), called LNBot. Exploiting various anonymity features of LN, we show the feasibility of a scalable two-layer botnet which completely anonymizes the identity of the botmaster. In the first layer, the botmaster anonymously sends the commands to the command and control (C&C) servers through regular LN payments. Specifically, LNBot allows botmaster's commands to be sent in the form of surreptitious multi-hop LN payments, where the commands are either encoded with the payments or attached to the payments to provide covert communications. In the second layer, C&C servers further relay those commands to the bots in their mini-botnets to launch any type of attacks to victim machines. We further improve on this design by introducing D-LNBot; a distributed version of LNBot that generates its C&C servers by infecting users on the Internet and forms the C&C connections by opening channels to the existing nodes on LN. In contrary to the LNBot, the whole botnet formation phase is distributed and the botmaster is never involved in the process. By utilizing Bitcoin's Testnet and the new message attachment feature of LN, we show that D-LNBot can be run for free and commands are propagated faster to all the C&C servers compared to LNBot. We presented proof-of-concept implementations for both LNBot and D-LNBot on the actual LN and extensively analyzed their delay and cost performance. Finally, we also provide and discuss a list of potential countermeasures to detect LNBot and D-LNBot activities and minimize their impacts.

Ahmet Kurt, Suat Mercan, Enes Erdin et al.

Bitcoin's success as a cryptocurrency enabled it to penetrate into many daily life transactions. Its problems regarding the transaction fees and long validation times are addressed through an innovative concept called the Lightning Network (LN) which works on top of Bitcoin by leveraging off-chain transactions. This made Bitcoin an attractive micro-payment solution that can also be used within certain IoT applications (e.g., toll payments) since it eliminates the need for traditional centralized payment systems. Nevertheless, it is not possible to run LN and Bitcoin on resource-constrained IoT devices due to their storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN's functions through a gateway LN node even if it is not trusted. The idea is to involve the IoT device only in signing operations, which is possible by replacing LN's original 2-of-2 multisignature channels with 3-of-3 multisignature channels. Once the gateway is delegated to open a channel for the IoT device in a secure manner, our protocol enforces the gateway to request the IoT device's cryptographic signature for all further operations on the channel such as sending payments or closing the channel. LN's Bitcoin transactions are revised to incorporate the 3-of-3 multisignature channels. In addition, we propose other changes to protect the IoT device's funds from getting stolen in possible revoked state broadcast attempts. We evaluated the proposed protocol using a Raspberry Pi considering a toll payment scenario. Our results show that timely payments can be sent and the computational and communication delays associated with the protocol are negligible.

Ahmet Kurt, Suat Mercan, Omer Shlomovits et al.

Bitcoin has emerged as a revolutionary payment system with its decentralized ledger concept however it has significant problems such as high transaction fees and long confirmation times. Lightning Network (LN), which was introduced much later, solves most of these problems with an innovative concept called off-chain payments. With this advancement, Bitcoin has become an attractive venue to perform micro-payments which can also be adopted in many IoT applications (e.g. toll payments). Nevertheless, it is not feasible to host LN and Bitcoin on IoT devices due to the storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN through an untrusted gateway node. The gateway hosts LN and Bitcoin nodes and can open & close LN channels, send LN payments on behalf of the IoT device. This delegation approach is powered by a (2,2)-threshold scheme that requires the IoT device and the LN gateway to jointly perform all LN operations which in turn secures both parties' funds. Specifically, we propose to thresholdize LN's Bitcoin public and private keys as well as its commitment points. With these and several other protocol level changes, IoT device is protected against revoked state broadcast, collusion, and ransom attacks. We implemented the proposed protocol by changing LN's source code and thoroughly evaluated its performance using a Raspberry Pi. Our evaluation results show that computational and communication delays associated with the protocol are negligible. To the best of our knowledge, this is the first work that implemented threshold cryptography in LN.

Luis Puche Rondon, Leonardo Babun, Ahmet Aris et al.

As technology becomes more widely available, millions of users worldwide have installed some form of smart device in their homes or workplaces. These devices are often off-the-shelf commodity systems, such as Google Home or Samsung SmartThings, that are installed by end-users looking to automate a small deployment. In contrast to these "plug-and-play" systems, purpose-built Enterprise Internet-of-Things (E-IoT) systems such as Crestron, Control4, RTI, Savant offer a smart solution for more sophisticated applications (e.g., complete lighting control, A/V management, security). In contrast to commodity systems, E-IoT systems are usually closed source, costly, require certified installers, and are overall more robust for their use cases. Due to this, E-IoT systems are often found in expensive smart homes, government and academic conference rooms, yachts, and smart private offices. However, while there has been plenty of research on the topic of commodity systems, no current study exists that provides a complete picture of E-IoT systems, their components, and relevant threats. As such, lack of knowledge of E-IoT system threats, coupled with the cost of E-IoT systems has led many to assume that E-IoT systems are secure. To address this research gap, raise awareness on E-IoT security, and motivate further research, this work emphasizes E-IoT system components, E-IoT vulnerabilities, solutions, and their security implications. In order to systematically analyze the security of E-IoT systems, we divide E-IoT systems into four layers: E-IoT Devices Layer, Communications Layer, Monitoring and Applications Layer, and Business Layer. We survey attacks and defense mechanisms, considering the E-IoT components at each layer and the associated threats. In addition, we present key observations in state-of-the-art E-IoT security and provide a list of open research problems that need further research.

Enes Erdin, Suat Mercan, Kemal Akkaya

Cryptocurrencies redefined how money can be stored and transferred among users. However, independent of the amount being sent, public blockchain-based cryptocurrencies suffer from high transaction waiting times and fees. These drawbacks hinder the wide use of cryptocurrencies by masses. To address these challenges, payment channel network concept is touted as the most viable solution to be used for micro-payments. The idea is exchanging the ownership of money by keeping the state of the accounts locally. The users inform the blockchain rarely, which decreases the load on the blockchain. Specifically, payment channel networks can provide transaction approvals in seconds by charging a nominal fee proportional to the payment amount. Such attraction on payment channel networks inspired many recent studies which focus on how to design them and allocate channels such that the transactions will be secure and efficient. However, as payment channel networks are emerging and reaching large number of users, privacy issues are becoming more relevant that raise concerns about exposing not only individual habits but also businesses' revenues. In this paper, we first propose a categorization of the existing payment networks formed on top of blockchain-backed cryptocurrencies. After discussing several emerging attacks on user/business privacy in these payment channel networks, we qualitatively evaluate them based on a number of privacy metrics that relate to our case. Based on the discussions on the strengths and weaknesses of the approaches, we offer possible directions for research for the future of privacy based payment channel networks.

Suat Mercan, Ahmet Kurt, Enes Erdin et al.

The successful amalgamation of cryptocurrency and consumer Internet of Things (IoT) devices can pave the way for novel applications in machine-to-machine economy. However, the lack of scalability and heavy resource requirements of initial blockchain designs hinders the integration as they prioritized decentralization and security. Numerous solutions have been proposed since the emergence of Bitcoin to achieve this goal. However, none of them seem to dominate and thus it is unclear how consumer devices will be adopting these approaches. Therefore, in this paper, we critically review the existing integration approaches and cryptocurrency designs that strive to enable micro-payments among consumer devices. We identify and discuss solutions under three main categories; direct integration, payment channel network and new cryptocurrency design. The first approach utilizes a full node to interact with the payment system. Offline channel payment is suggested as a second layer solution to solve the scalability issue and enable instant payment with low fee. New designs converge to semi-centralized scheme and focuson lightweight consensus protocol that does not require highcomputation power which might mean loosening the initial designchoices in favor of scalability. We evaluate the pros and cons ofeach of these approaches and then point out future researchchallenges. Our goal is to help researchers and practitioners tobetter focus their efforts to facilitate micro-payment adoptions.

Ahmet Kurt, Suat Mercan, Enes Erdin et al.

Lightning Network (LN) addresses the scalability problem of Bitcoin by leveraging off-chain transactions. Nevertheless, it is not possible to run LN on resource-constrained IoT devices due to its storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN's functions through a gateway LN node. The idea is to involve the IoT device in LN operations with its digital signature by replacing original 2-of-2 multisignature channels with 3-of-3 multisignature channels. Our protocol enforces the LN gateway to request the IoT device's cryptographic signature for all operations on the channel. We evaluated the proposed protocol by implementing it on a Raspberry Pi for a toll payment scenario and demonstrated its feasibility and security.

Luis Puche Rondon, Leonardo Babun, Ahmet Aris et al.

The rise of IoT devices has led to the proliferation of smart buildings, offices, and homes worldwide. Although commodity IoT devices are employed by ordinary end-users, complex environments such as smart buildings, smart offices, conference rooms, or hospitality require customized and highly reliable solutions. Those systems called Enterprise Internet of Things (EIoT) connect such environments to the Internet and are professionally managed solutions usually offered by dedicated vendors. As EIoT systems require specialized training, software, and equipment to deploy, this has led to very little research investigating the security of EIoT systems and their components. In effect, EIoT systems in smart settings such as smart buildings present an unprecedented and unexplored threat vector for an attacker. In this work, we explore EIoT system vulnerabilities and insecure development practices. Specifically, focus on the usage of drivers as an attack mechanism, and introduce PoisonIvy, a number of novel attacks that demonstrate an attacker can easily compromise EIoT system controllers using malicious drivers. Specifically, we show how drivers used to integrate third-party devices to EIoT systems can be misused in a systematic fashion. To demonstrate the capabilities of attackers, we implement and evaluate PoisonIvy using a testbed of real EIoT devices. We show that an attacker can perform DoS attacks, gain remote control, and maliciously abuse system resources of EIoT systems. To the best of our knowledge, this is the first work to analyze the (in)securities of EIoT deployment practices and demonstrate the associated vulnerabilities in this ecosystem. With this work, we raise awareness on the (in)secure development practices used for EIoT systems, the consequences of which can largely impact the security, privacy, reliability, and performance of millions of EIoT systems worldwide.

Suat Mercan, Kemal Akkaya, Lisa Cain et al.

The Internet of Things (IoT) has been on the rise in the last decade as it finds applications in various domains. Hospitality is one of the pioneer sectors that has adopted this technology to create novel services such as smart hotel rooms, personalized services etc. Hotels, restaurants, theme parks, and cruise ships are some specific application areas to improve customer satisfaction by creating an intense interactive environment and data collection with the use of appropriate sensors and actuators. However, applying IoT solutions in the hospitality environment has some unique challenges such as easy physical access to devices. In addition, due to the very nature of these domains, the customers are at the epicenter of these IoT technologies that result in a massive amount of data collection from them. Such data and its management along with business purposes also raises new concerns regarding privacy and ethical considerations. Therefore, this paper surveys and analyzes security, privacy and ethical issues regarding the utilization of IoT devices by focusing on the hospitality industry specifically. We explore some exemplary uses, cases, potential problems and solutions in order to contribute to better understanding and guiding the business operators in this sector.

Andrew Quijano, Kemal Akkaya

GPS signals, the main origin of navigation, are not functional in indoor environments. Therefore, Wi-Fi access points have started to be increasingly used for localization and tracking inside the buildings by relying on a fingerprint-based approach. However, with these types of approaches, several concerns regarding the privacy of the users have arisen. Malicious individuals can determine a client's daily habits and activities by simply analyzing their wireless signals. While there are already efforts to incorporate privacy into the existing fingerprint-based approaches, they are limited to the characteristics of the homomorphic cryptographic schemes they employed. In this paper, we propose to enhance the performance of these approaches by exploiting another homomorphic algorithm, namely DGK, with its unique encrypted sorting capability and thus pushing most of the computations to the server side. We developed an Android app and tested our system within a Columbia University dormitory. Compared to existing systems, the results indicated that more power savings can be achieved at the client side and DGK can be a viable option with more powerful server computation capabilities.

Suat Mercan, Mumin Cebe, Ege Tekiner et al.

IoT devices have been adopted widely in the last decade which enabled collection of various data from different environments. The collected data is crucial in certain applications where IoT devices generate data for critical infrastructure or systems whose failure may result in catastrophic results. Specifically, for such critical applications, data storage poses challenges since the data may be compromised during the storage and the integrity might be violated without being noticed. In such cases, integrity and data provenance are required in order to be able to detect the source of any incident and prove it in legal cases if there is a dispute with the involved parties. To address these issues, blockchain provides excellent opportunities since it can protect the integrity of the data thanks to its distributed structure. However, it comes with certain costs as storing huge amount of data in a public blockchain will come with significant transaction fees. In this paper, we propose a highly cost effective and reliable digital forensics framework by exploiting multiple inexpensive blockchain networks as a temporary storage before the data is committed to Ethereum. To reduce Ethereum costs,we utilize Merkle trees which hierarchically stores hashes of the collected event data from IoT devices. We evaluated the approach on popular blockchains such as EOS, Stellar, and Ethereum by presenting a cost and security analysis. The results indicate that we can achieve significant cost savings without compromising the integrity of the data.

Suat Mercan, Enes Erdin, Kemal Akkaya

The last decade has experienced a vast interest in Blockchain-based cryptocurrencies with a specific focus on the applications of this technology. However, slow confirmation times of transactions and unforeseeable high fees hamper their wide adoption for micro-payments. The idea of establishing payment channel networks is one of the many proposed solutions to address this scalability issue where nodes, by utilizing smart contracting, establish payment channels between each other and perform off-chain transactions. However, due to the way these channels are created, both sides have a certain one-way capacity for making transactions. Consequently, if one sides exceeds this one-way capacity, the channel becomes useless in that particular direction, which causes failures of payments and eventually creates an imbalance in the overall network. To keep the payment channel network sustainable, in this paper, we aim to increase the overall success rate of payments by effectively exploiting the fact that end-users are usually connected to the network at multiple points (i.e., gateways) any of which can be used to initiate the payment. We propose an efficient method for selection of the gateway for a user by considering the gateway's inbound and outbound payment traffic ratio. We then augment this proposed method with split payment capability to further increase success rate especially for large transactions. The evaluation of the proposed method shows that compared to greedy and maxflow-based approaches, we can achieve much higher success rates, which are further improved with split payments.

Suat Mercan, Enes Erdin, Kemal Akkaya

Blockchain-based cryptocurrencies received a lot of attention recently for their applications in many domains. IoT domain is one of such applications, which can utilize cryptocur-rencies for micro payments without compromising their payment privacy. However, long confirmation times of transactions and relatively high fees hinder the adoption of cryptoccurency based micro-payments. The payment channel networks is one of the proposed solutions to address these issue where nodes establish payment channels among themselves without writing on blockchain. IoT devices can benefit from such payment networks as long as they are capable of sustaining their overhead. Payment channel networks pose unique characteristics as far as the routing problem is concerned. Specifically, they should stay balanced to have a sustainable network for maintaining payments for longer times, which is crucial for IoT devices once they are deployed.In this paper, we present a payment channel network design that aims to keep the channels balanced by using a common weight policy across the network. We additionally propose using multi-point connections to nodes for each IoT device for unbalanced payment scenarios. The experiment results show that we can keep the channels in the network more equally balanced compared to the minimal fee approach. In addition, multiple connections from IoT devices to nodes increase the success ratio significantly.

Mohamed Baza, Andrew Salazar, Mohamed Mahmoud et al.

Electronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach. The idea is first to train a model on the original sensitive data, called the teacher model. Then, using this model, we can transfer its knowledge to another model, called the student model, without the need to learn the original data used in training the teacher model. The student model is then shared to the public and can be used to make accurate predictions. To assess the mimic learning approach, we have evaluated our scheme using different medical datasets. The results indicate that the student model mimics the teacher model performance in terms of prediction accuracy without the need to access to the patients' original data records.

Ahmet Kurt, Enes Erdin, Mumin Cebe et al.

While various covert botnets were proposed in the past, they still lack complete anonymization for their servers/botmasters or suffer from slow communication between the botmaster and the bots. In this paper, we propose a new generation hybrid botnet that covertly and efficiently communicates over Bitcoin Lightning Network (LN), called LNBot. LN is a payment channel network operating on top of Bitcoin network for faster Bitcoin transactions with negligible fees. Exploiting various anonymity features of LN, we designed a scalable two-layer botnet which completely anonymize the identity of the botmaster. In the first layer, the botmaster sends commands anonymously to the C&C servers through LN transactions. Specifically, LNBot allows botmaster's commands to be sent in the form of surreptitious multihop LN payments, where the commands are encoded with ASCII or Huffman encoding to provide covert communications. In the second layer, C&C servers further relay those commands to the bots they control in their mini-botnets to launch any type of attacks to victim machines. We implemented a proof-of-concept on the actual LN and extensively analyzed the delay and cost performance of LNBot. Our analysis show that LNBot achieves better scalibility compared to the other similar blockchain botnets with negligible costs. Finally, we also provide and discuss a list of potential countermeasures to detect LNBot activities and minimize its impacts.

Abhaykumar Kumbhar, Hamidullah Binol, Simran Singh et al.

UAV enabled communications and networking can enhance wireless connectivity and support emerging services. However, this would require system-level understanding to modify and extend the existing terrestrial network infrastructure. In this paper, we integrate UAVs both as user equipment and base stations into existing LTE-Advanced heterogeneous network (HetNet) and provide system-level insights of this three-tier LTE-Advanced air-ground HetNet (AG-HetNet). This AG-HetNet leverages cell range expansion (CRE), ICIC, 3D beamforming, and enhanced support for UAVs. Using system-level understanding and through brute-force technique and heuristics algorithms, we evaluate the performance of AG-HetNet in terms of fifth percentile spectral efficiency (5pSE) and coverage probability. We compare 5pSE and coverage probability, when aerial base-stations (UABS) are deployed on a fixed hexagonal grid and when their locations are optimized using genetic algorithm (GA) and elitist harmony search algorithm based on genetic algorithm (eHSGA). Our simulation results show the heuristic algorithms outperform the brute-force technique and achieve better peak values of coverage probability and 5pSE. Simulation results also show that trade-off exists between peak values and computation time when using heuristic algorithms. Furthermore, the three-tier hierarchical structuring of FeICIC provides considerably better 5pSE and coverage probability than eICIC.

Luis Puche Rondon, Leonardo Babun, Kemal Akkaya et al.

The High Definition Multimedia Interface (HDMI) is the de-facto standard for Audio/Video interfacing between video-enabled devices. Today, almost tens of billions of HDMI devices exist worldwide and are widely used to distribute A/V signals in smart homes, offices, concert halls, and sporting events making HDMI one of the most highly deployed systems in the world. An important component in HDMI is the Consumer Electronics Control (CEC) protocol, which allows for the interaction between devices within an HDMI distribution network. Nonetheless, existing network security mechanisms only protect traditional networking components, leaving CEC outside of their scope. In this work, we identify and tap into CEC protocol vulnerabilities, using them to implement realistic proof-of-work attacks on HDMI distribution networks. We study, how current insecure CEC protocol practices and HDMI distributions may grant an adversary a novel attack surface for HDMI devices otherwise thought to be unreachable. To introduce this novel attack surface, we present HDMI-Walk, which opens a realm of remote and local CEC attacks to HDMI devices. Specifically, with HDMI-Walk, an attacker can perform malicious analysis of devices, eavesdropping, Denial of Service attacks, targeted device attacks, and even facilitate well-known existing attacks through HDMI. With HDMI-Walk, we prove it is feasible for an attacker to gain arbitrary control of HDMI devices. We demonstrate the implementations of both local and remote attacks with commodity HDMI devices. Finally, we discuss security mechanisms to provide impactful and comprehensive security evaluation to these real-world systems while guaranteeing deployability and providing minimal overhead considering the current limitations of the CEC protocol. To the best of our knowledge, this is the first work solely investigating the security of HDMI device distribution networks.

Wesam Al Amiri, Mohamed Baza, Karim Banawan et al.

Searching for available parking spaces is a major problem for drivers especially in big crowded cities, causing traffic congestion and air pollution, and wasting drivers' time. Smart parking systems are a novel solution to enable drivers to have real-time parking information for pre-booking. However, current smart parking requires drivers to disclose their private information, such as desired destinations. Moreover, the existing schemes are centralized and vulnerable to the bottleneck of the single point of failure and data breaches. In this paper, we propose a distributed privacy-preserving smart parking system using blockchain. A consortium blockchain created by different parking lot owners to ensure security, transparency, and availability is proposed to store their parking offers on the blockchain. To preserve drivers' location privacy, we adopt a private information retrieval (PIR) technique to enable drivers to retrieve parking offers from blockchain nodes privately, without revealing which parking offers are retrieved. Furthermore, a short randomizable signature is used to enable drivers to reserve available parking slots in an anonymous manner. Besides, we introduce an anonymous payment system that cannot link drivers' to specific parking locations. Finally, our performance evaluations demonstrate that the proposed scheme can preserve drivers' privacy with low communication and computation overhead.

Mumin Cebe, Kemal Akkaya

Advanced Metering Infrastructure forms a communication network for the collection of power data from smart meters in Smart Grid. As the communication between smart meters could be secured utilizing public-key cryptography, however, public-key cryptography still has certain challenges in terms of certificate revocation and management particularly related distribution and storage overhead of revoked certificates. To address this challenge, in this paper, we propose a novel revocation management approach by utilizing cryptographic accumulators which reduces the space requirements for revocation information significantly and thus enables efficient distribution of such information to all smart meters. We implemented the proposed approach on both ns-3 network simulator and a testbed. We demonstrated its superior performance with respect to traditional methods for revocation management.

Umit Karabiyik, Kemal Akkaya

In the last decade, wireless sensor networks (WSNs) and Internet-of-Things (IoT) devices are proliferated in many domains including critical infrastructures such as energy, transportation and manufacturing. Consequently, most of the daily operations now rely on the data coming from wireless sensors or IoT devices and their actions. In addition, personal IoT devices are heavily used for social media applications, which connect people as well as all critical infrastructures to each other under the cyber domain. However, this connectedness also comes with the risk of increasing number of cyber attacks through WSNs and/or IoT. While a significant research has been dedicated to secure WSN/IoT, this still indicates that there needs to be forensics mechanisms to be able to conduct investigations and analysis. In particular, understanding what has happened after a failure is crucial to many businesses, which rely on WSN/IoT applications. Therefore, there is a great interest and need for understanding digital forensics applications in WSN and IoT realms. This chapter fills this gap by providing an overview and classification of digital forensics research and applications in these emerging domains in a comprehensive manner. In addition to analyzing the technical challenges, the chapter provides a survey of the existing efforts from the device level to network level while also pointing out future research opportunities.

Ahmad Alsharif, Mahmoud Nabil, Samet Tonyali et al.

In Advanced Metering Infrastructure (AMI) networks, smart meters should send fine-grained power consumption readings to electric utilities to perform real-time monitoring and energy management. However, these readings can leak sensitive information about consumers' activities. Various privacy-preserving schemes for collecting fine-grained readings have been proposed for AMI networks. These schemes aggregate individual readings and send an aggregated reading to the utility, but they extensively use asymmetric-key cryptography which involves large computation/communication overhead. Furthermore, they do not address End-to-End (E2E) data integrity, authenticity, and computing electricity bills based on dynamic prices. In this paper, we propose EPIC, an efficient and privacy-preserving data collection scheme with E2E data integrity verification for AMI networks. Using efficient cryptographic operations, each meter should send a masked reading to the utility such that all the masks are canceled after aggregating all meters' masked readings, and thus the utility can only obtain an aggregated reading to preserve consumers' privacy. The utility can verify the aggregated reading integrity without accessing the individual readings to preserve privacy. It can also identify the attackers and compute electricity bills efficiently by using the fine-grained readings without violating privacy. Furthermore, EPIC can resist collusion attacks in which the utility colludes with a relay node to extract the meters' readings. A formal proof, probabilistic analysis are used to evaluate the security of EPIC, and ns-3 is used to implement EPIC and evaluate the network performance. In addition, we compare EPIC to existing data collection schemes in terms of overhead and security/privacy features.

Abbas Acar, Hidayet Aksu, A. Selcuk Uluagac et al.

One-time login process in conventional authentication systems does not guarantee that the identified user is the actual user throughout the session. However, it is necessary to re-verify the user identity periodically throughout a login session without reducing the user convenience. Continuous authentication can address this issue. However, existing methods are either not reliable or not usable. In this paper, we introduce a usable and reliable method called Wearable Assisted Continuous Authentication (WACA). WACA relies on the sensor based keystroke dynamics, where the authentication data is acquired through the built in sensors of a wearable (e.g., smartwatch) while the user is typing. We implemented the WACA framework and evaluated its performance on real devices with real users. The empirical evaluation of WACA reveals that WACA is feasible and its error rate is as low as 1 percent with 30 seconds of processing time and 2 3% for 20 seconds. The computational overhead is minimal. Furthermore, we tested WACA against different attack scenarios. WACA is capable of identifying insider threats with very high accuracy (99.2%) and also robust against powerful adversaries such as imitation and statistical attackers.

Mumin Cebe, Enes Erdin, Kemal Akkaya et al.

Today's vehicles are becoming cyber-physical systems that do not only communicate with other vehicles but also gather various information from hundreds of sensors within them. These developments help create smart and connected (e.g., self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies and maintenance service providers for various applications. One such application that is becoming crucial with the introduction of self-driving cars is the forensic analysis for traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to find out the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information on the cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data. Specifically, we first integrate Vehicular Public Key Management (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicle such as maintenance information/history, car diagnosis reports, etc. The proposed forensic framework enables trustless, traceable and privacy-aware post-accident analysis with minimal storage and processing overhead.