Real-time Contextual Intrusion Detection
Design science approach
Abstract—Cybersecurity is a growing problem. Cyber-threats are now affecting society on personal, organizational and national levels. Current threat detection and prediction models are far from being operational in realistic scenarios. Suggested threat detection approaches in current research are merely prototypes trained and tested with similar data sets, relying on well-known attacks and attack signatures steps. Attack prediction detection models suffer from false positives. In this paper, we present context-based threat detection model using design science. Our proposed detection model decreases false positives with greater detection of malicious activities. The proposed detection system aligns with organizational information security policies to provide better detection of insider threats.
Copyright (c) 2019 Abdulrahim Charif
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