SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection

• URL: http://arxiv.org/abs/2502.07119v1
• Date: Mon, 10 Feb 2025 23:20:59 GMT
• Title: SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection
• Authors: Elvin Li, Zhengli Shang, Onat Gungor, Tajana Rosing,
• Abstract summary: This paper introduces SAFE, a novel framework that transforms network intrusion data into an image-like format. It is shown that SAFE outperforms the state-of-the-art anomaly detection method, Scale Learning-based Deep Anomaly Detection method (SLAD), by up to 26.2%. It also surpasses the state-of-the-art SSL-based network intrusion detection approach, Anomal-E, by up to 23.5% in F1-score.
• Score: 6.587970321208976
• License:
• Abstract: The proliferation of IoT devices has significantly increased network vulnerabilities, creating an urgent need for effective Intrusion Detection Systems (IDS). Machine Learning-based IDS (ML-IDS) offer advanced detection capabilities but rely on labeled attack data, which limits their ability to identify unknown threats. Self-Supervised Learning (SSL) presents a promising solution by using only normal data to detect patterns and anomalies. This paper introduces SAFE, a novel framework that transforms tabular network intrusion data into an image-like format, enabling Masked Autoencoders (MAEs) to learn robust representations of network behavior. The features extracted by the MAEs are then incorporated into a lightweight novelty detector, enhancing the effectiveness of anomaly detection. Experimental results demonstrate that SAFE outperforms the state-of-the-art anomaly detection method, Scale Learning-based Deep Anomaly Detection method (SLAD), by up to 26.2% and surpasses the state-of-the-art SSL-based network intrusion detection approach, Anomal-E, by up to 23.5% in F1-score.

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