Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract)

Sampath Kalutharage, Chathuranga; Liu, Xiaodong; Chrysoulas, Christos

Abstract

Over the past few decades, Machine Learning (ML)-based intrusion detection systems (IDS) have become increasingly popular and continue to show remarkable performance in detecting attacks. However, the lack of transparency in their decision-making process and the scarcity of attack data for training purposes pose a major challenge for the development of ML-based IDS systems for Internet of Things (IoT). Therefore, employing anomaly detection methods and interpreting predicted results in terms of feature contribution or performing feature-based impact analysis can increase stakeholders confidence. To this end, this paper presents a novel framework for IoT security monitoring, combining deep autoencoder models with Explainable Artificial Intelligence (XAI), to verify the credibility and certainty of attack detection by ML-based IDSs. Our proposed approach reduces the number of black boxes in the ML decision-making process in IoT security monitoring by explaining why a prediction is made, providing quantifiable data on which features influence the prediction and to what extent, which are generated from SHaply Adaptive values exPlanations (SHAP) linking optimal credit allocation to local explanations. This was tested using the USB-IDS benchmark dataset and a detection accuracy of 84% (benign) and 100% (attack) was achieved. Our experimental results show that integrating XAI with the autoencoder model obviates the need of malicious data for training purposes, but can provide attack certainty for detected anomalies, proving the validity of the proposed methodology.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 27th European Symposium on Research in Computer Security (ESORICS) 2022
• Start Date: Sep 26, 2022
• End Date: Sep 30, 2022
• Acceptance Date: Aug 27, 2022
• Online Publication Date: Dec 11, 2022
• Publication Date: 2022
• Deposit Date: Oct 27, 2022
• Publicly Available Date: Dec 12, 2023
• Publisher: Springer
• Pages: 41-50
• Series Title: Lecture Notes in Computer Science
• Series Number: 13745
• Series ISSN: 1611-3349
• Book Title: Attacks and Defenses for the Internet-of-Things: 5th International Workshop, ADIoT 2022
• ISBN: 978-3-031-21310-6
• DOI: https://doi.org/10.1007/978-3-031-21311-3_8
• Keywords: IoT Security, Anomaly Detection, Explainable AI
• Public URL: http://researchrepository.napier.ac.uk/Output/2940046