Detecting Mixed-Type Intrusion in High Adaptability Using Artificial Immune System and Parallelized Automata

Abstract

This study applies artificial immune system and parallelized finite-state machines to construct an intrusion detection algorithm for spotting hidden threats in massive number of packets. Existing intrusion detections are mostly not focused on adaptability for mixed and changing attacks, which results in low detection rate in new and mixed-type attacks. Using the characteristics of artificial immune and state transition can address the attacks in evolutionary patterns and track the anomalies in nonconsecutive packets. The proposed immune algorithm in this study is highly efficient based on a selection step in multi-island migration. Result shows that the algorithm can effectively detect mixed-type attacks and obtains an overall accuracy of 95.9% in testing data.

An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System

“…Wu et al (2020) proposed a network intrusion detection method based on semantic re-encoding 2 (SR) and deep learning to improve the detection speed. Chou et al (2020) adopted an incremental approach to choose the minimal Redundancy-Maximal Relevance (mRMR) criterion, which is used to calculate the mean value of redundant attributes to reduce the effects of β. The advantage of the mRMR criterion is that with lower computational resources, we can get the best features; the drawback is that there are more differences in information entropy.…”

“…Ehsan et al (2021) proposed a new complex mixed artificial immune intrusion detection system; the system integrated the negative selection algorithm (NSA) and the DCA for detectors. Chou et al (2020) used AI and the parallel automaton (PA) method to design a high adaptive hybrid intrusion detection algorithm; the state automaton theory was used to define the different data states; the artificial immune algorithm was used to convert the states. Xi et al (2021) introduced immune adaptive and feedback mechanism to build a multi-source neighborhood immune detector adaptive model (MS-NIDAM).…”