A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection

Xu, Hui; Przystupa, Krzysztof; Fang, Ce; Marciniak, Andrzej; Кочан, Орест; Beshley, Mykola

doi:10.3390/electronics9081206

Cited by 40 publications

(19 citation statements)

References 46 publications

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“…2, April 2022: 1869-1880 1872 [22]. PSO is a common swarm intelligence algorithm employed in the continuous search space to solve global optimization [23], [24].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

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A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

Nuiaa

Manickam

Alsaeedi

et al. 2022

IJECE

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Cyberattacks have grown steadily over the last few years. The distributed reflection denial of service (DRDoS) attack has been rising, a new variant of distributed denial of service (DDoS) attack. DRDoS attacks are more difficult to mitigate due to the dynamics and the attack strategy of this type of attack. The number of features influences the performance of the intrusion detection system by investigating the behavior of traffic. Therefore, the feature selection model improves the accuracy of the detection mechanism also reduces the time of detection by reducing the number of features. The proposed model aims to detect DRDoS attacks based on the feature selection model, and this model is called a proactive feature selection model proactive feature selection (PFS). This model uses a nature-inspired optimization algorithm for the feature subset selection. Three machine learning algorithms, i.e., k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were evaluated as the potential classifier for evaluating the selected features. We have used the CICDDoS2019 dataset for evaluation purposes. The performance of each classifier is compared to previous models. The results indicate that the suggested model works better than the current approaches providing a higher detection rate (DR), a low false-positive rate (FPR), <span>and increased accuracy detection (DA).</span> The PFS model shows better accuracy to detect DRDoS attacks with 89.59%.

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“…2, April 2022: 1869-1880 1872 [22]. PSO is a common swarm intelligence algorithm employed in the continuous search space to solve global optimization [23], [24].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The KNN is a lazy learning algorithm aimed at classifying a new object based on the current classes in which the previous training points are categorized. It categorizes the latest data points based on metrics of resemblance [24], [28].…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

Nuiaa

Manickam

Alsaeedi

et al. 2022

IJECE

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“…So, the earlier researchers used various approaches based on conventional ML for intrusion detection (ID). Xu et al [19] applied K-nearest neighbors (K-NN) for anomaly ID, and evaluated the efficacy of the proposed ID system using the KDDCup ID dataset. Bhati et al [20] applied variants of support vector machine (SVM), such as quadratic, linear, fine, and medium Gaussian, to analyze the performance of SVM techniques using the NSL-KDD dataset.…”

Section: Related Workmentioning

confidence: 99%

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Khan

2021

Processes

161

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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

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“…The K-Means [48][49][50] algorithm uses distance as the classification criterion. The smaller the distance between two samples, the more similar they are.…”

Section: Obtaining the Optimal Anchor Boxesmentioning

confidence: 99%

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

et al. 2021

Self Cite

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Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

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A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection

Cited by 40 publications

References 46 publications

A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

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