Browsing by Author "Zahid, Farzana"
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- Actively detecting multiscale flooding attacks & attack volumes in resource-constrained ICPSPublication . Zahid, Farzana; Kuo, Matthew M.Y.; Sinha, Roopak; Funchal, Gustavo Silva; Pedrosa, Tiago; Leitão, PauloThe significant growth in modern communication technologies has led to an increase in zero-day vulnerabilities that degrade the performance of cyber-physical systems (ICPS). Distributed denial of service (DDoS) attacks are one such threat that overwhelms a target with floods of packets, posing a severe risk to the normal operations of the ICPS. Current solutions to detect DDoS attacks are unsuitable for resource-constrained ICPS. This study proposes actively detecting multiscale flooding DDoS attacks in resource-constrained ICPS by analyzing network traffic in the frequency domain. A two-phased technique detects attack presence and attack volume. Both phases use a novel combination of light-weight and theoretically sound statistical methods. The effectiveness of the proposed technique is evaluated using mainstream metrics like true and false positive rates, accuracy, and precision using BOUN DDoS 2020 and CICDDoS 2019 datasets. An implementation of the proposed approach on a programmable logic controllers-based ICPS demonstrated improvements in resource usage and detection time compared to the existing state-of-the-art.
- An intrusion detection system dataset for a multi-agent cyber-physical conveyor systemPublication . Funchal, Gustavo Silva; Zahid, Farzana; Melo, Victoria; Kuo, Matthew M.Y.; Pedrosa, Tiago; Sinha, Roopak; Prieta Pintado, Fernando De la; Leitão, PauloIndustry 4.0 is built upon the foundation of connecting devices and systems via Internet of Things (IoT) technologies, with Cyber-Physical Systems (CPS) serving as the backbone infrastructure. Although this approach brings numerous benefits like improved performance, responsiveness and reconfigurability, it also introduces security concerns, making devices and systems vulnerable to cyber attacks. There is a need for effective techniques to protect these systems, and the availability of datasets becomes essential to support the development of such techniques. This paper presents a dataset based on the collection of traffic information exchanged in a self-organizing conveyor system using the multi-agent systems (MAS) architecture and containing various intelligent conveyor modules. The dataset comprises data collected at the network and agent levels under normal system operation, denial of service (DoS) attacks, and malicious agent attacks. An intrusion detection system that integrates Fast Fourier Transform (FFT) and Machine Learning (ML) analysis is developed to demonstrate the utility of this dataset.
- DDoS attacks on smart manufacturing systems: a cross-domain taxonomy and attack vectorsPublication . Zahid, Farzana; Funchal, Gustavo Silva; Melo, Victória; Kuo, Matthew M.Y.; Leitão, Paulo; Sinha, RoopakDenial of Service is a significant availability threat in Industrial Cyber-Physical systems and smart manufacturing is not an exception. The types, methods, and duration of these attacks have been evolving rapidly and their number has increased dramatically, reaching a new record in history. In particular, digitisation of the manufacturing process and increased connectivity have created a battleground between product quality of service and threats associated with cross-domains and multi-vector attacks that affect the manufacturing system performance. The existing research on cyber-threats related to smart manufacturing system does not consider the comprehensive landscape of denial of service attacks. In this study, we classify well-accepted (distributed) denial of service attacks according to a proposed taxonomy, focusing on both the multi-vector attacks and cross-domain attacks. Utilising the taxonomy, more than fifty different denial of service attacks on smart manufacturing system were classified in terms of Endpoint and Network (distributed) denial of service attacks. As an example, a Cyber-Physical Conveyor System was used to examine the proposed taxonomy.