Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model
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Abstract
Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting abnormal network behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, such as denial of service, spam, phishing, and other types of attacks, are reported daily, and the number of attacks is growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposes an anomaly intrusion detection system method based on a new RNA encoding method and the ResNet50 Model, where encoding is performed by splitting the training records into different groups. These groups are protocol, service, flag, and digit, and each group is represented by the number of RNA characters that can represent the group's values. The RNA encoding phase converts network traffic records into RNA sequences, allowing for a comprehensive representation of the dataset. The detection model, which uses the ResNet architecture, effectively addresses training challenges and achieves high detection rates for different attack types. The KDD-Cup99 dataset is used for both training and testing. The testing dataset includes new attacks that do not appear in the training dataset, which means that the system can detect new attacks in the future. The efficiency of the suggested anomaly intrusion detection system is determined by calculating the detection rate (DR), false alarm rate (FAR), and accuracy. The achieved DR, FAR, and accuracy are 96.24%, 6.133%, and 95.99%, respectively. The experimental results revealed that the RNA encoding method can improve intrusion detection.
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