Explainable Artificial Intelligence for Resilient Security Applications in the Internet of Things

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Abstract
The performance of Artificial Intelligence (AI) systems reaches or even exceeds that of humans in an increasing number of complicated tasks. Highly effective non-linear AI models are generally employed in a black-box form nested in their complex structures, which means that no information as to what precisely helps them reach appropriate predictions is provided. The lack of transparency and interpretability in existing Artificial Intelligence techniques would reduce human users’ trust in the models used for cyber defence, especially in current scenarios where cyber resilience is becoming increasingly diverse and challenging. Explainable AI (XAI) should be incorporated into developing cybersecurity models to deliver explainable models with high accuracy that human users can understand, trust, and manage. This paper explores the following concepts related to XAI. A summary of current literature on XAI is discussed. Recent taxonomies that help explain different machine learning algorithms are discussed. These include deep learning techniques developed and studied extensively in other IoT taxonomies. The outputs of AI models are crucial for cybersecurity, as experts require more than simple binary outputs for examination to enable the cyber resilience of IoT systems. Examining the available XAI applications and safety-related threat models to explain resilience towards IoT systems also summarises the difficulties and gaps in XAI concerning cybersecurity. Finally, various technical issues and trends are explained, and future studies on technology, applications, security, and privacy are presented, emphasizing the ideas of explainable AI models.
Funding Information
  • University of New South Wales Canberra
  • U.S. Department of the Army/ARO Broad Agency Announcement (BAA) for Fundamental Research (FA520924C0006)

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