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Full-Text Articles in Physical Sciences and Mathematics
Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen
Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen
Research Collection School Of Computing and Information Systems
The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers (or invariant checkers). In this work, we …
Enhancing Project Based Learning With Unsupervised Learning Of Project Reflections, Hua Leong Fwa
Enhancing Project Based Learning With Unsupervised Learning Of Project Reflections, Hua Leong Fwa
Research Collection School Of Computing and Information Systems
Natural Language Processing (NLP) is an area of research and application that uses computers to analyze human text. It has seen wide adoption within several industries but few studies have investigated it for use in evaluating the effectiveness of educational interventions and pedagogies. Pedagogies such as Project based learning (PBL) centers on learners solving an authentic problem or challenge which leads to knowledge creation and higher engagement. PBL also lends itself well in plugging the gap between what is taught in classrooms and applying the knowledge gained to the real working environment. In this study, we seek to investigate how …
Code Integrity Attestation For Plcs Using Black Box Neural Network Predictions, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Code Integrity Attestation For Plcs Using Black Box Neural Network Predictions, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Research Collection School Of Computing and Information Systems
Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs). Unfortunately, traditional techniques for attesting code integrity (i.e. verifying that it has not been modified) rely on firmware access or roots-of-trust, neither of which proprietary or legacy PLCs are likely to provide. In this paper, we propose a practical code integrity checking solution based on privacy-preserving black box models that instead attest the input/output behaviour of PLC programs. Using faithful offline copies of the PLC programs, we identify their most important …
Attack As Defense: Characterizing Adversarial Examples Using Robustness, Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, Jun Sun
Attack As Defense: Characterizing Adversarial Examples Using Robustness, Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, Jun Sun
Research Collection School Of Computing and Information Systems
As a new programming paradigm, deep learning has expanded its application to many real-world problems. At the same time, deep learning based software are found to be vulnerable to adversarial attacks. Though various defense mechanisms have been proposed to improve robustness of deep learning software, many of them are ineffective against adaptive attacks. In this work, we propose a novel characterization to distinguish adversarial examples from benign ones based on the observation that adversarial examples are significantly less robust than benign ones. As existing robustness measurement does not scale to large networks, we propose a novel defense framework, named attack …
Efficient White-Box Fairness Testing Through Gradient Search, Lingfeng Zhang, Yueling Zhang, Min Zhang
Efficient White-Box Fairness Testing Through Gradient Search, Lingfeng Zhang, Yueling Zhang, Min Zhang
Research Collection School Of Computing and Information Systems
Deep learning (DL) systems are increasingly deployed for autonomous decision-making in a wide range of applications. Apart from the robustness and safety, fairness is also an important property that a well-designed DL system should have. To evaluate and improve individual fairness of a model, systematic test case generation for identifying individual discriminatory instances in the input space is essential. In this paper, we propose a framework EIDIG for efficiently discovering individual fairness violation. Our technique combines a global generation phase for rapidly generating a set of diverse discriminatory seeds with a local generation phase for generating as many individual discriminatory …