Hybrid-Ids: An Approach for Intrusion Detection System with Hybrid Feature Extraction Technique Using Supervised Machine Learning - Kishor P. Jadhav; Tripti Arjariya; Mohit Gangwar (International Journal of Intelligent Systems and Applications in Engineering, 2023)
At a breakneck pace, the IoT (i.e. Internet of Things) and networking technology, security has become a significant issue, such as data security, virtual machine hacking and various internal and external attacks. Conventional Intrusion Detection Systems (IDS) have a lot of limitations due to resource dependency and their complexity. Multiple researchers have implemented IDS systems with network logs or real-time network audit datasets. The KDDCUP99 and NSLKDD are the most popular datasets that existing authors use, but challenges persist in detecting unknown, active, passive, and others. In this paper, we proposed a heterogeneous extraction of attribute or feature and selection method for IDS, by using machine learning methodologies for the recognition of network intrusion as well as host intrusion. The numerous network log dataset has been used to detect the intruder in a vulnerable environment. The various heterogeneous feature extraction methods have been carried out for building a robust module. In the testing module, entire training rules validates the input packet data with training rules and executes the weight using a majority voting algorithm. Finally, it detects whether the current packet is normal or intruder based on majority voting values and eliminates that connection. In an extensive experimental analysis, three classifiers have been used for validation, such as ANN, SVM and RNN of different network log datasets. In observation, RNN produces the highest detection and classification accuracy over the SVM and ANN. It also reduces the time complexity and error rate with al datasets