Amr Youssef

Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic et al.

Line Current Differential Relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-Masking Attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this paper, we propose a two-module framework to detect FMAs. The first module is a Mismatch Index (MI) developed from the protected transmission line's equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR's local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT's real-time simulator confirm the proposed solution's real-time performance capability.

Ahmad Mohammad Saber, Alaa Selim, Mohamed M. Hammad et al.

With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset.

Shakib Khan, A. Ben Hamza, Amr Youssef

Adversarial attacks present a significant risk to the integrity and performance of graph neural networks, particularly in tasks where graph structure and node features are vulnerable to manipulation. In this paper, we present a novel model, called fixed-point iterative graph convolutional network (Fix-GCN), which achieves robustness against adversarial perturbations by effectively capturing higher-order node neighborhood information in the graph without additional memory or computational complexity. Specifically, we introduce a versatile spectral modulation filter and derive the feature propagation rule of our model using fixed-point iteration. Unlike traditional defense mechanisms that rely on additional design elements to counteract attacks, the proposed graph filter provides a flexible-pass filtering approach, allowing it to selectively attenuate high-frequency components while preserving low-frequency structural information in the graph signal. By iteratively updating node representations, our model offers a flexible and efficient framework for preserving essential graph information while mitigating the impact of adversarial manipulation. We demonstrate the effectiveness of the proposed model through extensive experiments on various benchmark graph datasets, showcasing its resilience against adversarial attacks.

Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic et al.

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%$-$91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.

Ahmad Mohammad Saber, Max Mauro Dias Santos, Mohammad Al Janaideh et al.

The increasing adoption of Electric Vehicles (EVs) and the expansion of charging infrastructure and their reliance on communication expose Electric Vehicle Supply Equipment (EVSE) to cyberattacks. This paper presents a novel Kolmogorov-Arnold Network (KAN)-based framework for detecting cyberattacks on EV chargers using only power consumption measurements. Leveraging the KAN's capability to model nonlinear, high-dimensional functions and its inherently interpretable architecture, the framework effectively differentiates between normal and malicious charging scenarios. The model is trained offline on a comprehensive dataset containing over 100,000 cyberattack cases generated through an experimental setup. Once trained, the KAN model can be deployed within individual chargers for real-time detection of abnormal charging behaviors indicative of cyberattacks. Our results demonstrate that the proposed KAN-based approach can accurately detect cyberattacks on EV chargers with Precision and F1-score of 99% and 92%, respectively, outperforming existing detection methods. Additionally, the proposed KANs's enable the extraction of mathematical formulas representing KAN's detection decisions, addressing interpretability, a key challenge in deep learning-based cybersecurity frameworks. This work marks a significant step toward building secure and explainable EV charging infrastructure.

Yan Zhang, Ahmad Mohammad Saber, Amr Youssef et al.

Modern power grids face unprecedented complexity from Distributed Energy Resources (DERs), Electric Vehicles (EVs), and extreme weather, while also being increasingly exposed to cyberattacks that can trigger grid violations. This paper introduces Grid-Agent, an autonomous AI-driven framework that leverages Large Language Models (LLMs) within a multi-agent system to detect and remediate violations. Grid-Agent integrates semantic reasoning with numerical precision through modular agents: a planning agent generates coordinated action sequences using power flow solvers, while a validation agent ensures stability and safety through sandboxed execution with rollback mechanisms. To enhance scalability, the framework employs an adaptive multi-scale network representation that dynamically adjusts encoding schemes based on system size and complexity. Violation resolution is achieved through optimizing switch configurations, battery deployment, and load curtailment. Our experiments on IEEE and CIGRE benchmark networks, including the IEEE 69-bus, CIGRE MV, IEEE 30-bus test systems, demonstrate superior mitigation performance, highlighting Grid-Agent's suitability for modern smart grids requiring rapid, adaptive response.

Ahmed Aly, Essam Mansour, Amr Youssef

Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these logs as connected entities and events, revealing relationships that are missed by linear log representations. Existing systems apply anomaly detection to these graphs but often suffer from high false positive rates and coarse-grained alerts. Their reliance on node attributes like file paths or IPs leads to spurious correlations, reducing detection robustness and reliability. To fully understand an attack's progression and impact, security analysts need systems that can generate accurate, human-like narratives of the entire attack. To address these challenges, we introduce OCR-APT, a system for APT detection and reconstruction of human-like attack stories. OCR-APT uses Graph Neural Networks (GNNs) for subgraph anomaly detection, learning behavior patterns around nodes rather than fragile attributes such as file paths or IPs. This approach leads to a more robust anomaly detection. It then iterates over detected subgraphs using Large Language Models (LLMs) to reconstruct multi-stage attack stories. Each stage is validated before proceeding, reducing hallucinations and ensuring an interpretable final report. Our evaluations on the DARPA TC3, OpTC, and NODLINK datasets show that OCR-APT outperforms state-of-the-art systems in both detection accuracy and alert interpretability. Moreover, OCR-APT reconstructs human-like reports that comprehensively capture the attack story.

Ahmad Mohammad Saber, Aditi Maheshwari, Amr Youssef et al.

The application of Deep Learning-based Schemes (DLSs) for detecting False Data Injection Attacks (FDIAs) in smart grids has attracted significant attention. This paper demonstrates that adversarial attacks, carefully crafted FDIAs, can evade existing DLSs used for FDIA detection in Line Current Differential Relays (LCDRs). We propose a novel adversarial attack framework, utilizing the Fast Gradient Sign Method, which exploits DLS vulnerabilities by introducing small perturbations to LCDR remote measurements, leading to misclassification of the FDIA as a legitimate fault while also triggering the LCDR to trip. We evaluate the robustness of multiple deep learning models, including multi-layer perceptrons, convolutional neural networks, long short-term memory networks, and residual networks, under adversarial conditions. Our experimental results demonstrate that while these models perform well, they exhibit high degrees of vulnerability to adversarial attacks. For some models, the adversarial attack success rate exceeds 99.7%. To address this threat, we introduce adversarial training as a proactive defense mechanism, significantly enhancing the models' ability to withstand adversarial FDIAs without compromising fault detection accuracy. Our results highlight the significant threat posed by adversarial attacks to DLS-based FDIA detection, underscore the necessity for robust cybersecurity measures in smart grids, and demonstrate the effectiveness of adversarial training in enhancing model robustness against adversarial FDIAs.

Walter Lucia, Amr Youssef

In this study, the authors consider the problem of exchanging secrete messages in cyber-physical systems (CPSs) without resorting to cryptographic solutions. In particular, they consider a CPS where the networked controller wants to send a secrete message to the plant. They show that such a problem can be solved by exploiting a Wyner wiretap-like encoding scheme taking advantage of the closed-loop operations typical of feedback control systems. Specifically, by resorting to the control concept of one-step reachable sets, they first show that a wiretap-like encoding scheme exists whenever there is an asymmetry in the plant model knowledge available to control system (the defender) and to the eavesdropper. The effectiveness of the proposed scheme is confirmed by means of a numerical example. Finally, they conclude the study by presenting open design challenges that can be addressed by the research community to improve, in different directions, the secrete message exchange problem in CPSs

Amir Mohammad Naseri, Walter Lucia, Mohammad Mannan et al.

Recently, cloud control systems have gained increasing attention from the research community as a solution to implement networked cyber-physical systems (CPSs). Such an architecture can reduce deployment and maintenance costs albeit at the expense of additional security and privacy concerns. In this paper, first, we discuss state-of-the-art security solutions for cloud control systems and their limitations. Then, we propose a novel control architecture based on Trusted Execution Environments (TEE). We show that such an approach can potentially address major security and privacy issues for cloud-hosted control systems. Finally, we present an implementation setup based on Intel Software Guard Extensions (SGX) and validate its effectiveness on a testbed system.

Suzan Ali, Tousif Osman, Mohammad Mannan et al.

Open access WiFi hotspots are widely deployed in many public places, including restaurants, parks, coffee shops, shopping malls, trains, airports, hotels, and libraries. While these hotspots provide an attractive option to stay connected, they may also track user activities and share user/device information with third-parties, through the use of trackers in their captive portal and landing websites. In this paper, we present a comprehensive privacy analysis of 67 unique public WiFi hotspots located in Montreal, Canada, and shed some light on the web tracking and data collection behaviors of these hotspots. Our study reveals the collection of a significant amount of privacy-sensitive personal data through the use of social login (e.g., Facebook and Google) and registration forms, and many instances of tracking activities, sometimes even before the user accepts the hotspot's privacy and terms of service policies. Most hotspots use persistent third-party tracking cookies within their captive portal site; these cookies can be used to follow the user's browsing behavior long after the user leaves the hotspots, e.g., up to 20 years. Additionally, several hotspots explicitly share (sometimes via HTTP) the collected personal and unique device information with many third-party tracking domains.

Hisham S. Galal, Amr M. Youssef

The wide deployment of tokens for digital assets on top of Ethereum implies the need for powerful trading platforms. Vickrey auctions have been known to determine the real market price of items as bidders are motivated to submit their own monetary valuations without leaking their information to the competitors. Recent constructions have utilized various cryptographic protocols such as ZKP and MPC, however, these approaches either are partially privacy-preserving or require complex computations with several rounds. In this paper, we overcome these limits by presenting Trustee as a Vickrey auction on Ethereum which fully preserves bids' privacy at relatively much lower fees. Trustee consists of three components: a front-end smart contract deployed on Ethereum, an Intel SGX enclave, and a relay to redirect messages between them. Initially, the enclave generates an Ethereum account and ECDH key-pair. Subsequently, the relay publishes the account's address and ECDH public key on the smart contract. As a prerequisite, bidders are encouraged to verify the authenticity and security of Trustee by using the SGX remote attestation service. To participate in the auction, bidders utilize the ECDH public key to encrypt their bids and submit them to the smart contract. Once the bidding interval is closed, the relay retrieves the encrypted bids and feeds them to the enclave that autonomously generates a signed transaction indicating the auction winner. Finally, the relay submits the transaction to the smart contract which verifies the transaction's authenticity and the parameters' consistency before accepting the claimed auction winner. As part of our contributions, we have made a prototype for Trustee available on Github for the community to review and inspect it. Additionally, we analyze the security features of Trustee and report on the transactions' gas cost incurred on Trustee smart contract.

Louis Waked, Mohammad Mannan, Amr Youssef

Network traffic inspection, including TLS traffic, in enterprise environments is widely practiced. Reasons for doing so are primarily related to improving enterprise security (e.g., malware detection) and meeting legal requirements. To analyze TLS-encrypted data, network appliances implement a Man-in-the-Middle TLS proxy, by acting as the intended web server to a requesting client (e.g., a browser), and acting as the client to the outside web server. As such, the TLS proxy must implement both a TLS client and a server, and handle a large amount of traffic, preferably, in real-time. However, as protocol and implementation layer vulnerabilities in TLS/HTTPS are quite frequent, these proxies must be, at least, as secure as a modern, up-to-date web browser, and a properly configured web server. As opposed to client-end TLS proxies (e.g., as in several anti-virus products), the proxies in network appliances may serve hundreds to thousands of clients, and any vulnerability in their TLS implementations can significantly downgrade enterprise security. To analyze TLS security of network appliances, we develop a comprehensive framework, by combining and extending tests from existing work on client-end and network-based interception studies. We analyze thirteen representative network appliances over a period of more than a year (including versions before and after notifying affected vendors, a total of 17 versions), and uncover several security issues. For instance, we found that four appliances perform no certificate validation at all, three use pre-generated certificates, and eleven accept certificates signed using MD5, exposing their clients to MITM attacks. Our goal is to highlight the risks introduced by widely-used TLS proxies in enterprise and government environments, potentially affecting many systems hosting security, privacy, and financially sensitive data.