A Collaborative Anomaly Detection System for Network Intrusion Detection
Keywords:
Anomaly detection, intrusion, supervised, unsupervised machine learning.Abstract
Anomaly detection plays a critical role in identifying malicious enterprise network traffic, but it has limitations when applied to modern complex networks. In this system, we recommended a collaborative framework for anomaly detection in network intrusion detection by combining supervised and unsupervised machine learning approaches. A Collaborative Anomaly Detection (CAD) for Network Intrusion Detection is a system specially designed to identify and detect any unusual or abnormal behavior in computer networks that might lead to a possible security breach. The system leverages the power of collaborative machine learning algorithms to identify network anomalies beyond the capabilities of a single machine learning model. The proposed system reduces false positives and improves the accuracy of anomaly detection by integrating multiple data sources. Our experiment results show that the proposed system detects anomalies more effectively than existing methods, demonstrating its effectiveness and scalability. The recommended approach has the potential to be implemented in real-world environments to improve the efficiency and accuracy of network intrusion detection.
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