Feature reduction scheme for anomaly‐based intrusion detection in wireless networks: Building of hybrid model

In recent years, the number of mobile transactions has skyrocketed. Because mobile payments are made on the fly, many consumers prefer the method to the traditional local payment approach. The rise in mobile payments has inspired this study into the security of mobile networks in order to instill trust in those who may be involved in the transaction in some way. This report is a precursor to explain and compare some of the most popular wireless networks that enable mobile payments, from a security standpoint, this research presents, explains, and compares some of the most common wireless networks that enable mobile payments. Threat models in 3G with connections to GSM, WLAN, and 4G networks are classified into four categories: attacks on privacy, attacks on integrity, attacks on availability, and assaults on authentication. In addition, we offer classification countermeasures which are divided into three categories: cryptographic methods, human factors, and intrusion detection methods. One of the most important aspects we analyze is the security procedures that each network employs. Since the security of these networks is paramount, it gives hope to subscribers. In summary, the study aims to verify if mobile payments offer acceptable security to the average user.

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Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms

International Journal of Machine Learning and Computing ◽

10.7763/ijmlc.2015.v5.476 ◽

2015 ◽

Vol 5 (1) ◽

pp. 17-23 ◽

Cited By ~ 12

Author(s):

Md. Enamul Haque ◽

Talal M. Alkharobi

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Hybrid Model ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Network Intrusion Detection ◽

Network Intrusion

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Extracting salient features for network intrusion detection using machine learning methods

South African Computer Journal ◽

10.18489/sacj.v52i0.200 ◽

2014 ◽

Vol 52 ◽

Cited By ~ 13

Author(s):

Ralf C. Staudemeyer ◽

Christian W. Omlin

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Information Gain ◽

Selection Process ◽

Denial Of Service ◽

Reduction Process ◽

Feature Reduction ◽

General Nature ◽

Network Intrusion ◽

Salient Features

This work presents a data preprocessing and feature selection framework to support data mining and network security experts in minimal feature set selection of intrusion detection data. This process is supported by detailed visualisation and examination of class distributions. Distribution histograms, scatter plots and information gain are presented as supportive feature reduction tools. The feature reduction process applied is based on decision tree pruning and backward elimination. This paper starts with an analysis of the KDD Cup '99 datasets and their potential for feature reduction. The dataset consists of connection records with 41 features whose relevance for intrusion detection are not clear. All traffic is either classified `normal' or into the four attack types denial-of-service, network probe, remote-to-local or user-to-root. Using our custom feature selection process, we show how we can significantly reduce the number features in the dataset to a few salient features. We conclude by presenting minimal sets with 4--8 salient features for two-class and multi-class categorisation for detecting intrusions, as well as for the detection of individual attack classes; the performance using a static classifier compares favourably to the performance using all features available. The suggested process is of general nature and can be applied to any similar dataset.

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Feature Selection Models Based on Hybrid Firefly Algorithm with Mutation Operator for Network Intrusion Detection

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2021.0228.19 ◽

2021 ◽

Vol 14 (1) ◽

pp. 192-202

Author(s):

Karrar Alwan ◽

◽

Ahmed AbuEl-Atta ◽

Hala Zayed ◽

◽

...

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Firefly Algorithm ◽

Detection System ◽

Binary Classification ◽

Feature Reduction ◽

Detection Accuracy ◽

Multi Classification ◽

Modified Firefly Algorithm

Accurate intrusion detection is necessary to preserve network security. However, developing efficient intrusion detection system is a complex problem due to the nonlinear nature of the intrusion attempts, the unpredictable behaviour of network traffic, and the large number features in the problem space. Hence, selecting the most effective and discriminating feature is highly important. Additionally, eliminating irrelevant features can improve the detection accuracy as well as reduce the learning time of machine learning algorithms. However, feature reduction is an NPhard problem. Therefore, several metaheuristics have been employed to determine the most effective feature subset within reasonable time. In this paper, two intrusion detection models are built based on a modified version of the firefly algorithm to achieve the feature selection task. The first and, the second models have been used for binary and multiclass classification, respectively. The modified firefly algorithm employed a mutation operation to avoid trapping into local optima through enhancing the exploration capabilities of the original firefly. The significance of the selected features is evaluated using a Naïve Bayes classifier over a benchmark standard dataset, which contains different types of attacks. The obtained results revealed the superiority of the modified firefly algorithm against the original firefly algorithm in terms of the classification accuracy and the number of selected features under different scenarios. Additionally, the results assured the superiority of the proposed intrusion detection system against other recently proposed systems in both binary classification and multi-classification scenarios. The proposed system has 96.51% and 96.942% detection accuracy in binary classification and multi-classification, respectively. Moreover, the proposed system reduced the number of attributes from 41 to 9 for binary classification and to 10 for multi-classification.

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