Hybrid Ant Colony Optimization and Deep Learning for Anomaly Intrusion Detection
Keywords:
anomaly Intrusion Detection, Deep Learning, Optimization, Ant Colony, NSLKDDAbstract
Network security is now a major issue for every distributed system. A lot of risks are becoming harder to identify using antivirus and firewall software. Intrusion detection systems (IDSs) are used to find abnormalities in network traffic to increase security. Security-focused networks are known to be mostly consisting of intrusion detection systems. Determining whether incoming network traffic is abnormal or legitimate is the problem of network anomaly detection. The accuracy of intrusion detection statistics and false alarms of network intrusions resulting from a large amount of network data are among the issues with these kinds of security systems. In IDS, achieving high detection accuracy while minimizing training time is a major problem. However, they are managed inefficiently by the conventional IDS that are currently in effect. To solve these issues with network security, this study proposes IDS that depends on deep learning and hybrid optimization. The NSL-KDD dataset's optimal feature number is selected by the algorithm. Furthermore, this proposed system has combined feature selection with deep learning by using the Ant Colony Optimisation (ACO) algorithm to optimize factors for efficient dataset classification. The proposed approach performance has been evaluated on the current intrusion dataset as NSLKDD. Experimental results indicate that the proposed approach performs better than also achieves good accuracy in comparison to the other recent strategies in NSLKDD.
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