Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals

The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods, including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub

Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability

In recent years, the topic of explainable machine learning (ML) has been extensively researched. Up until now, this research focused on regular ML users use-cases such as debugging a ML model. This paper takes a different posture and show that adversaries can leverage explainable ML to bypass multi-feature types malware classifiers. Previous adversarial attacks against such classifiers only add new features and not modify existing ones to avoid harming the modified malware executable's functionality. Current attacks use a single algorithm that both selects which features to modify and modifies them blindly, treating all features the same. In this paper, we present a different approach. We split the adversarial example generation task into two parts: First we find the importance of all features for a specific sample using explainability algorithms, and then we conduct a feature-specific modification, feature-by-feature. In order to apply our attack in black-box scenarios, we introduce the concept of transferability of explainability, that is, applying explainability algorithms to different classifiers using different features subsets and trained on different datasets still result in a similar subset of important features. We conclude that explainability algorithms can be leveraged by adversaries and thus the advocates of training more interpretable classifiers should consider the trade-off of higher vulnerability of those classifiers to adversarial attacks.Comment: Accepted as a conference paper at IJCNN 202

Anomalous behaviour detection for cyber defence in modern industrial control systems

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The fusion of pervasive internet connectivity and emerging technologies in smart cities creates fragile cyber-physical-natural ecosystems. Industrial Control Systems (ICS) are intrinsic parts of smart cities and critical to modern societies. Not designed for interconnectivity or security, disruptor technologies enable ubiquitous computing in modern ICS. Aided by artificial intelligence and the industrial internet of things they transform the ICS environment towards better automation, process control and monitoring. However, investigations reveal that leveraging disruptive technologies in ICS creates security challenges exposing critical infrastructure to sophisticated threat actors including increasingly hostile, well-organised cybercrimes and Advanced Persistent Threats. Besides external factors, the prevalence of insider threats includes malicious intent, accidental hazards and professional errors. The sensing capabilities create opportunities to capture various data types. Apart from operational use, this data combined with artificial intelligence can be innovatively utilised to model anomalous behaviour as part of defence-in-depth strategies. As such, this research aims to investigate and develop a security mechanism to improve cyber defence in ICS. Firstly, this thesis contributes a Systematic Literature Review (SLR), which helps analyse frameworks and systems that address CPS’ cyber resilience and digital forensic incident response in smart cities. The SLR uncovers emerging themes and concludes several key findings. For example, the chronological analysis reveals key influencing factors, whereas the data source analysis points to a lack of real CPS datasets with prevalent utilisation of software and infrastructure-based simulations. Further in-depth analysis shows that cross-sector proposals or applications to improve digital forensics focusing on cyber resilience are addressed by a small number of research studies in some smart sectors. Next, this research introduces a novel super learner ensemble anomaly detection and cyber risk quantification framework to profile anomalous behaviour in ICS and derive a cyber risk score. The proposed framework and associated learning models are experimentally validated. The produced results are promising and achieve an overall F1-score of 99.13%, and an anomalous recall score of 99% detecting anomalies lasting only 17 seconds ranging from 0.5% to 89% of the dataset. Further, a one-class classification model is developed, leveraging stream rebalancing followed by adaptive machine learning algorithms and drift detection methods. The model is experimentally validated producing promising results including an overall Matthews Correlation Coefficient (MCC) score of 0.999 and the Cohen’s Kappa (K) score of 0.9986 on limited variable single-type anomalous behaviour per data stream. Wide data streams achieve an MCC score of 0.981 and a K score of 0.9808 in the prevalence of multiple types of anomalous instances. Additionally, the thesis scrutinises the applicability of the learning models to support digital forensic readiness. The research study presents the concept of digital witness and digital chain of custody in ICS. Following that, a use case integrating blockchain technologies into the design of ICS to support digital forensic readiness is discussed. In conclusion, the contributions of this research thesis help towards developing the next generation of state-of-the-art methods for anomalous behaviour detection in ICS defence-in-depth