<p>Emerging Cyber threats with an increased dependency on vulnerable cyber-networks have jeopardized all stakeholders, making Intrusion Detection Systems (IDS) the essential network security requirement. Several IDS have been proposed in the past decade for preventing systems from cyber-attacks. Machine learning (ML) based IDS have shown remarkable performance on conventional cyber threats. However, the introduction of adversarial attacks in the cyber domain highlights the need to upgrade these IDS because conventional ML-based approaches are vulnerable to adversarial attacks. Therefore, the proposed IDS framework leverages the performance of conventional ML-based IDS and integrates it with Explainable AI (XAI) to deal with adversarial attacks. Global Explanation of AI model, extracted by SHAP (Shapley additive explanation) during the training phase of Primary Random Forest Classifier (RFC), is used to reassess the credibility of predicted outcomes. In other words, an outcome with low credibility is reassessed by secondary classifiers. This SHAP-based approach helps in filtering out all disguised malicious network traffic and can also enhance user trust by adding transparency to the decision-making process. Adversarial robustness of the proposed IDS was assessed by Hop Skip Jump Attack and CICIDS dataset, where IDS showed 98.5% and 100% accuracy, respectively. Furthermore, the performance of the proposed IDS is compared with conventional algorithms using recall, precision, accuracy, and F1-score as evaluation metrics. This comparative analysis and series of experiments endorse the credibility of the proposed scheme, depicting that the integration of XAI with conventional IDS can ensure credibility, integrity, and availability of cyber-networks.</p>

No abstract

Recent surveys about autonomous vehicles show that the public is concerned about the safety consequences of system or equipment failures and the vehicles' reactions to unexpected situations. We believe that informing about the technology and quality, e.g., safety and reliability, of autonomous vehicles is paramount to improving public expectations, perception and acceptance. In this paper, we report on the design of an interactive exhibit to illustrate (1) basic technologies employed in autonomous vehicles, i.e., sensors and object classification; and(2) basic principles for ensuring their quality, i.e., employing software testing and simulations. We subsequently report on the delivery of this exhibit titled "Trusted Autonomous Vehicles" at the Royal Society Summer Science Exhibition 2019. We describe the process of designing and developing the artefacts used in our exhibit, the theoretical background associated to them, the design of our stand, and the lessons learned. The activities and findings of this study can be used by other educators and researchers interested in promoting trust in autonomous vehicles among the general public.

<p>Emerging Cyber threats with an increased dependency on vulnerable cyber-networks have jeopardized all stakeholders, making Intrusion Detection Systems (IDS) the essential network security requirement. Several IDS have been proposed in the past decade for preventing systems from cyber-attacks. Machine learning (ML) based IDS have shown remarkable performance on conventional cyber threats. However, the introduction of adversarial attacks in the cyber domain highlights the need to upgrade these IDS because conventional ML-based approaches are vulnerable to adversarial attacks. Therefore, the proposed IDS framework leverages the performance of conventional ML-based IDS and integrates it with Explainable AI (XAI) to deal with adversarial attacks. Global Explanation of AI model, extracted by SHAP (Shapley additive explanation) during the training phase of Primary Random Forest Classifier (RFC), is used to reassess the credibility of predicted outcomes. In other words, an outcome with low credibility is reassessed by secondary classifiers. This SHAP-based approach helps in filtering out all disguised malicious network traffic and can also enhance user trust by adding transparency to the decision-making process. Adversarial robustness of the proposed IDS was assessed by Hop Skip Jump Attack and CICIDS dataset, where IDS showed 98.5% and 100% accuracy, respectively. Furthermore, the performance of the proposed IDS is compared with conventional algorithms using recall, precision, accuracy, and F1-score as evaluation metrics. This comparative analysis and series of experiments endorse the credibility of the proposed scheme, depicting that the integration of XAI with conventional IDS can ensure credibility, integrity, and availability of cyber-networks.</p>

No abstract