A Hybrid Nested Genetic-Fuzzy Algorithm Framework for Intrusion Detection and Attacks

Abstract

Intrusion Detection System (IDS) plays a very important role in security systems. Among its different types, Network Intrusion Detection System (NIDS) has an effective role in monitoring computer networks systems for malicious and illegal activities. In the literature, the detection of DoS and Probe attacks were with reasonable accuracy in most of the NIDS researches. However, the detection accuracy of other categories of attacks is still low, such as the R2L and U2R in KDDCUP99 dataset along with the Backdoors and Worms in UNSW-NB15 dataset. Computational Intelligence (CI) techniques have the characteristics to address such imprecision problem. In this research, a Hybrid Nested Genetic-Fuzzy Algorithm (HNGFA) framework has been developed to produce highly optimized outputs for security experts in classifying both major and minor categories of attacks. The adaptive model is evolved using two-nested Genetic-Fuzzy Algorithms (GFA). Each GFA consists of two-nested Genetic Algorithms (GA). The outer is to evolve fuzzy sets and the inner is to evolve fuzzy rules. The outer GFA assists the inner GFA in training phase, where the best individual in outer GFA interacts with the weak individual in inner GFA to generate new solutions that enhance the prediction of mutated attacks. Both GFA interact together to evolve the best rules for normal, major and minor categories of attacks through the optimization process. Several experiments have been conducted with different settings over different datasets. The obtained results show that the developed model has good accuracy and is more efficient compared with several state-of-the-art techniques.

Intrusion Detection Method Based on Adaptive Clonal Genetic Algorithm and Backpropagation Neural Network

“…[2]. Confronted with a lot of network intrusions and attacks, data and privacy on the Internet are seriously threatened, which also poses greater challenges to IDS [3].…”

Effective intrusion detection system for IoT using optimized capsule auto encoder model

“…The developed model's performance is compared with the recently published papers of BoT‐IoT dataset like HKCAE‐salp swarm algorithm (SSA), 32 HKCAE‐grasshopper optimization algorithm (GOA), 33 HKCAE‐atom search optimization (ASO), 34 RNN, LSTM, SVM, 35 ID3, and RF 36 . It is also compared with recently published papers of BoT‐IoT dataset like random over sampler (ROS)‐DNN, 21 synthetic minority over‐sampling technique (SMOTE)‐DNN, 21 adaptive synthetic sampling (ADASYN)‐DNN, 21 ICVAE‐DNN, 21 imbalanced generative adversarial network IGAN‐IDS, 37 network anomaly detection scheme based on feature representation and data augmentation (NADS‐RA), 38 GA + SVM, 39 and HNGFA 27 …”

“…Elhefnawy et al 27 developed a hybrid nested genetic‐Fuzzy algorithm (HNGFA) for producing highly optimized results in classifying minor and major attacks. Initially, the data was pre‐processed to obtain the relevant features and classified by HNGFA.…”

Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

“…The overall fitness calculation can be computed by taking the average CC of all chromosomes in the population. For example, x and y indicates the individual feature sets and the CC is computed by using the following equation [27].…”