Leveraging Interpretable Machine Learning Approaches to Detect Anomaly Within UK Cloud Service Sector
Open Access DepositedThe increasing reliance on cloud-based platforms has saved time, money, and resources but also has lured criminals in cyber-attacks. With financial losses averaging £3.4 million per data breach, the need to identify cyber-attacks is critical. Anomaly detection models are designed to identify unusual activities including data breaches. However, anomaly detection models suffer from over-sensitivity despite their proved successes in various domains. This praxis addresses the problem by enhancing the trustworthiness of anomaly detection models. This praxis study employs a validation model that integrates Explainable Artificial Intelligence (XAI) with machine learning models. The methodology acquires and processes a dataset, trains supervised, semi-supervised and unsupervised anomaly detection models, and applies XAI technologies including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to generate interpretable results. The main findings reveal that XAI models can effectively validate machine learning predictions. XAI global explanations can validate whether dataset features are employed properly. Furthermore, XAI local explanations can validate whether the prediction result (true positive, false negative and etc.) is properly formed by the right dataset features. Utilizing these attributes, XAI models effectively explain the machine learning model predictions, provide transparency, show correlation between features and anomalous activities, and increase trust among stakeholders.
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