Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection

Kasongo, Sydney Mambwe

doi:10.20944/preprints202106.0710.v1

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…A machine learning IDS in conjunction with the GA for features selection was proposed in [16]. For attack classification, decision trees, SVM, random forests, extreme gradient boosting, extra-trees, and naive Bayes were utilized.…”

Section: Literature Review 21 Genetic Algorithm Based Feature Reductionmentioning

confidence: 99%

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

Reji¹,

Joseph²,

Thaiyalnayaki³

et al. 2023

Computer Systems Science and Engineering

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The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves, when the destination and source nodes are not in range of coverage. Because of its wireless type, it has lot of security concerns than an infrastructure networks. Wormhole attacks are one of the most serious security vulnerabilities in the network layers. It is simple to launch, even if there is no prior network experience. Signatures are the sole thing that preventive measures rely on. Intrusion detection systems (IDS) and other reactive measures detect all types of threats. The majority of IDS employ features from various network layers. One issue is calculating a huge layered features set from an ad-hoc network. This research implements genetic algorithm (GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks. For attack detection, the reduced feature set was put to a fuzzy logic system (FLS). The performance of proposed model was compared with principal component analysis (PCA) and statistical parametric mapping (SPM). Network performance analysis like delay, packet dropping ratio, normalized overhead, packet delivery ratio, average energy consumption, throughput, and control overhead are evaluated and the IDS performance parameters like detection ratio, accuracy, and false alarm rate are evaluated for validation of the proposed model. The proposed model achieves 95.5% in detection ratio with 96.8% accuracy and produces very less false alarm rate (FAR) of 14% when compared with existing techniques.

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Section: Literature Review 21 Genetic Algorithm Based Feature Reductionmentioning

confidence: 99%

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

Reji¹,

Joseph²,

Thaiyalnayaki³

et al. 2023

Computer Systems Science and Engineering

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“…In addition, HLBDA was able to increase the classification accuracy by decreasing the number of selected attributes. Sydney Mambwe Kasongo [18] proposed machine learning methods to create effective intrusion detection system utilizing the NSL-KDD dataset. To select the best features, feature selection technique based on wrapper method was used with the Genetic Algorithm (GA).…”

Section: Related Workmentioning

confidence: 99%

A Comparison Between the Performance of Features Selection Techniques: Survey Study

Majeed¹,

Ramo²

2023

Technium

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Feature selection is one of the most popular and crucial methods of data processing used in different machine learning and data mining approaches to avoid high dimensionality and increase classification accuracy. Additionally, attribute selection aids in accelerating machine learning algorithms, improving prediction accuracy, data comprehension, decreasing data storage space, and minimizing the computational complexity of learning algorithms. For this reason, several feature selection approaches are used. To determine the essential feature or feature subsets needed to achieve classification objectives, several feature selection techniques have been suggested in the literature. In this research, different widely employed feature selection strategies have been evaluated by using different datasets to see how efficiently these techniques may be applied to achieve high performance of learning algorithms, which improves the classifier's prediction accuracy.

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“…Veri setindeki örnek sayısının fazla olması, geleneksel makine öğrenmesi algoritmalarına kıyasla Derin Öğrenme (Deep Learning-DL) tabanlı algoritmalarda hem daha kritik öneme sahiptir hem de örnek sayısı arttıkça eğitim modelinin başarısını artırmaktadır [5]. Buna rağmen hem sığ hem derin makine öğrenmesi yöntemlerinde özniteliklerin bir kısmı belirli sınıflandırma problemleri için bir takım önem derecelerine sahip olabilir.…”

Section: Literatür İncelemesi Ve Temel Bilgilerunclassified

Genetic Feature Selection Approach in Detection of Web Application Attacks Using Machine Learning Methods

Ahmetoglu

Daş

2021

Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi

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Applications on the Internet have some coding-related security concerns. Weaknesses or vulnerabilities allow criminals to gain direct and public access to databases to steal sensitive data. This study proposes an approach based on heuristic feature selection and machine learning for easier and more accurate detection of web application attacks with hybrid intrusion detection systems. Web application attacks and benign flow examples in CIC-IDS2017 and CSE-CIC-IDS2018 datasets were combined after a series of data preprocessing stages, and a new dataset was created. Using Genetic Algorithm and LogisticRegression, mean square error and feature count optimization were performed, and the results were tested with five different machine learning algorithms. When the results obtained were examined, it was observed that the success rate in classification remained at the level of 99%, although the number of features was reduced by 85%

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Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection

Cited by 5 publications

References 18 publications

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

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

A Comparison Between the Performance of Features Selection Techniques: Survey Study

Genetic Feature Selection Approach in Detection of Web Application Attacks Using Machine Learning Methods

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