An improved k-Means Clustering algorithm for Intrusion Detection using Gaussian function

Kumar, Gunupudi Rajesh; Mangathayaru, Nimmala; Narasimha, G.

doi:10.1145/2832987.2833082

Cited by 46 publications

(6 citation statements)

References 12 publications

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“…To give users security, it is necessary to address the weaknesses and problems each of these services and methods possesses [2]. In recent years, different methods have been proposed for the IDS, such as the traditional machine learning techniques, for example, the support vector machine (SVM) [3,4], decision trees [5,6], k-means clustering [7,8], and others. The recent advances in deep neural networks, including conventional neural networks (CNNs) and recurrent neural networks (RNNs), were also adopted in this field [9].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Fatani

Dahou

Elaziz

et al. 2023

Sensors

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Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

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Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Fatani

Dahou

Elaziz

et al. 2023

Sensors

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“…First, k-means has proven its validity in clustering. It has a great number of improved versions that are used in different research fields [25,26,33,66]. For instance, k-means++ is a sophisticated type of k-means.…”

Section: Instance Selection-based Studiesmentioning

confidence: 99%

complexFuzzy: A novel clustering method for selecting training instances of cross-project defect prediction

Öztürk

2021

csci

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Over the last decade, researchers have investigated to what extent cross-project defect prediction (CPDP) shows advantages over traditional defect prediction settings. These works do not take training and testing data of defect prediction from the same project. Instead, dissimilar projects are employed. Selecting proper training data plays an important role in terms of the success of CPDP. In this study, a novel clustering method named complexFuzzy is presented for selecting training data of CPDP. The method is developed by determining membership values with the help of some metrics which can be considered as indicators of complexity. First, CPDP combinations are created on 29 different data sets. Subsequently, complexFuzzy is evaluated by considering cluster centers of data sets and comparing some performance measures including area under the curve (AUC) and F-measure. The method is superior to other five comparison algorithms in terms of the distance of cluster centers and prediction performance.

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“…Previously, different methods have been developed for intrusion detection systems (IDS) using traditional machine learning methods, such as k-means clustering [ 2 , 3 ], decision tree (DT) [ 4 , 5 ], k-nearest neighbor (kNN) [ 6 , 7 ], support vector machine (SVM) [ 8 , 9 ], and other traditional machine learning (ML) approaches. With the wide spread of the deep learning methods, in recent years thy are also adopted for IDS, such as multi-layered perceptron neural network [ 10 ], convolutional neural networks (CNN) [ 11 ], and deep recurrent neural network (RNN) [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Fatani

Dahou

Al-qaness

et al. 2021

Sensors

115

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Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

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An improved k-Means Clustering algorithm for Intrusion Detection using Gaussian function

Cited by 46 publications

References 12 publications

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

complexFuzzy: A novel clustering method for selecting training instances of cross-project defect prediction

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

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