Research on Alarm Reduction of Intrusion Detection System Based on Clustering and Whale Optimization Algorithm

Wang, Leiting; Gu, Lize; Tang, Yifan

doi:10.3390/app112311200

Cited by 6 publications

(1 citation statement)

References 46 publications

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“…Extracting features from network traffic with CNN deep learning, feature selection by majority voting among several meta-heuristic methods, and using multiple deep learning classifiers in majority voting to detect an intrusion is more accurate than our future works. From right to left, Upward-spirals and Double-loops movement of the valves around the optimal solution [14] Figure 2 Various attacks and their contributions against the Internet of Things network [15] Figure 3 The mechanism of botnet attacks in the smart city and the IoT [17] Figure 4 Network-based intrusion detection system [18] Figure 5 The framework of the proposed method for detecting attacks Figure 6 Dimension reduction of samples with improved WOA algorithm in fog nodes Figure 7 Comparison of attack detection time in the proposed method and other meta-heuristic methods Figure 8 Comparison of attack detection accuracy in the proposed method and other methods Figure 9 Comparison of attack detection precision in the proposed method and other methods…”

Section: Discussionmentioning

confidence: 99%

Detection of attacks in the Internet of Things with the feature selection approach based on the whale optimization algorithm and learning by majority voting

Aliabadi¹,

Jalalian²

2023

Preprint

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Penetration into the Internet of Things network is a challenge in the security of new-generation networks and smart cities. In most cases, malware is distributed in the Internet of Things and smart objects are infected by malware. Objects infected with malware or viruses, which are called botnets, perform attacks such as DDoS against network services. DDoS attacks make network services inaccessible to users. A suitable approach to detect attacks based on malware and botnet is to use intelligent and distributed intrusion detection systems in the Internet of Things and smart cities. In other research, a centralized architecture and deep learning and machine learning method have been used to design intrusion detection systems. Centralized approaches have limited ability to process large volumes of traffic and are vulnerable to DDoS attacks. In this paper, a distributed intrusion detection system is designed with two stages dimensionality reduction and classification. In the first stage, a new and improved version of the whale optimization algorithm(WOA) has been used to select features and reduce traffic dimensions in fog nodes. In the second stage, each fog node performs the classification of the important features of the network traffic by voting and combined learning. The fog nodes share the IP address of the attacking nodes with the detection of the attacking node. Experiments showed that the improved WOA algorithm has less error in calculating the optimal solution than the optimization algorithm of the WOA algorithm. Reducing the feature selection objective function in the proposed method shows that the WOA algorithm is finding optimal features for intrusion detection and reducing the intrusion detection error. The advantage of the proposed intrusion detection system is to deal with DDoS attacks and cooperation between fog nodes to share blacklists. Tests showed that the proposed method in detecting network intrusion without feature selection has accuracy, sensitivity, and precision of 98.21%, 98.09%, and 97.88%. The proposed method with feature selection has accuracy, sensitivity, and precision of 99.39%, 99.31%, and 99.28%. The accuracy and precision of the proposed method in network intrusion detection are higher than the gray wolf algorithm, genetics and support vector machine, the binary gray wolf algorithm, and the hybridized GWO and GA algorithm. The proposed method is more accurate in intrusion detection than the GWO + PSO and firefly algorithms.

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Section: Discussionmentioning

confidence: 99%

Detection of attacks in the Internet of Things with the feature selection approach based on the whale optimization algorithm and learning by majority voting

Aliabadi¹,

Jalalian²

2023

Preprint

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Detecting and Analyzing Threats for Network Traffic Control using Machine Learning Approaches

Mathur,

Dutta

2023

2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT)

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Research and Implementation of Alarm Terminal for Security Prevention in Rural Substations Based on Video Technology

李

2023

CSA

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Research on Alarm Reduction of Intrusion Detection System Based on Clustering and Whale Optimization Algorithm

Cited by 6 publications

References 46 publications

Detection of attacks in the Internet of Things with the feature selection approach based on the whale optimization algorithm and learning by majority voting

Detection of attacks in the Internet of Things with the feature selection approach based on the whale optimization algorithm and learning by majority voting

Detecting and Analyzing Threats for Network Traffic Control using Machine Learning Approaches

Research and Implementation of Alarm Terminal for Security Prevention in Rural Substations Based on Video Technology

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