An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

Abstract

Intrusion detection and classification have gained significant attention recently due to the increased utilization of networks. For this purpose, there are different types of Network Intrusion Detection System (NIDS) approaches developed in the conventional works, which mainly focus on identifying the intrusions from the datasets with the help of classification techniques. Still, it is limited by the significant problems of inefficiency in handling large dimensional datasets, high computational complexity, false detection, and more time consumption for training the models. To solve these problems, this research intends to develop an innovative clustering-based classification methodology to precisely detect intrusions from the different types of IDS datasets. Here, the most recent and extensively used IDS datasets such as NSL-KDD, CICIDS, and Bot-IoT have been employed for detecting intrusions. Data preprocessing has been performed to normalize the dataset to eliminate irrelevant attributes and organize the features. Then, the data separation is applied by forming the clusters by using an intelligent Anticipated Distance-based Clustering (ADC) incorporated with the Density-Based Spatial clustering of applications with noise (DBScan) algorithm. It helps to find the distance and density measures for grouping the attributes into the clusters, which increases the efficiency of classification. Here, the most suitable optimal parameters are selected using the Perpetual Pigeon Galvanized Optimization (PPGO) technique. The extracted features are used for training and testing the dataset samples.Consequently, the Likelihood Naïve Bayes (LNB) classification approach is implemented to accurately predict the classified label as to whether normal or attack. During the evaluation, the performance of the proposed IDS framework is validated and compared using various evaluation metrics. Theresults show that the proposed ADC-DBScan-LNB model outperforms the other techniques with improved performance outcomes. INDEX TERMSNetwork IntrusionDetection System (NIDS), Density-based spatial clustering of applications with noise (DBSCAN), Anticipated Distance-based Clustering (ADC), Data Preprocessing, Likelihood Naïve Bayes (LNB), and IDS Datasets.

Development of a Machine-Learning Intrusion Detection System and Testing of Its Performance Using a Generative Adversarial Network

“…Another point is that the usage of artificial neural networks seems promising, since even with a simple ANN the results were similar to the other algorithms tested. Compared with the results presented in [ 9 ], we obtained better results with the testing dataset for classes that were better represented in terms of the number of samples in the dataset. However, the authors of [ 9 ] obtained better performance using PPGO, a bio-inspired optimization technique, for the U2R class, which had few samples in the dataset.…”

“…For the purposes of this paper, we used the NSL-KDD dataset, which is a refined version of its predecessor KDD’99, a well-known benchmark in the research on intrusion detection techniques [ 6 , 9 , 10 , 11 , 12 , 13 ]. This labeled dataset is split into training and testing files and can be downloaded from [ 12 ].…”

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

“…The research on the IDS system in this paper is to distinguish whether it is a malicious attack, or to distinguish the specific form of the attack. Different from the CNN algorithm proposed in this paper, the mainstream algorithms include decision tree [29], Naive Bayes [28], Logist regression [27], ensemble algorithm [30] and so on.…”

“…An intrusion detection model based on network detection data learning mechanism is proposed. In 2022, Shitharth et al [28] proposed an innovative cluster-based classification method to accurately detect intrusions from different types of IDS datasets. First, data preprocessing is performed to normalize the dataset to eliminate irrelevant attributes and organize features.…”

Artificial Intelligence in Cyber Security

“…Therefore, machine learning techniques, specifically deep learning that does not generally require prior experience or dependence on previous expert classifications may be particularly important as an implementation of cyber security AI approaches. The study [71][72][73][74][75][76][77] analysis to the effectiveness for cyber security purposes of machine learning approaches. This research included the implementation of methods of machine learning to identify intrusions, spam and malware.…”