Distributed Security Control for Complex Cyber-physical Systems against Denial-of-service Attacks

Huang, Xiaojie; Ding, Dawei; Li, Zhiqiang; An, Cuijuan

doi:10.1007/s12555-020-0906-7

“…In gure (5), the framework of the proposed method in network intrusion detection is introduced. To detect attacks in the IoT and the proposed method, the following steps are used: Each fog node acts as an intrusion detection system and receives IoT network tra c.…”

Section: Methodsmentioning

confidence: 99%

“…The expansion of computer networks and new emerging applications have caused attackers to carry out various attacks with different tools against computer networks. Figures (5) shows the percentage of security attacks from McAfee laboratories in 2017. Reports show that browser attacks, brute force attacks, and distributed denial of service attacks have the largest share of attacks.…”

Section: Attacks On the Networkmentioning

confidence: 99%

See 1 more Smart Citation

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

0

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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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“…In figure (5), the framework of the proposed method in network intrusion detection is introduced. To detect attacks in the IoT and the proposed method, the following steps are used:…”

Section: Methodsmentioning

confidence: 99%

“…The expansion of computer networks and new emerging applications have caused attackers to carry out various attacks with different tools against computer networks. Figure (5) shows the percentage of security attacks from McAfee laboratories in 2017. Reports show that browser attacks, brute force attacks, and distributed denial of service attacks have the largest share of attacks.…”

Section: Attacks On the Networkmentioning

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

0

View full text Add to dashboard Cite

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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Event‐triggered networked cascade control systems design subject to hybrid attacks

Kazemy,

Saravanakumar

2023

Math Methods in App Sciences

0

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This paper deals with the design problem of networked cascade control systems (NCCSs) with two different event‐triggered mechanisms (ETMs) for the feedback loops. A combination of stochastic denial‐of‐service (DoS) attack and deception attack has been considered for the communication network. To comply with the practical point of view, the primary and the secondary systems have been considered with distinct disturbances. First, the closed‐loop system with state feedback controllers is provided in a unified stochastic delayed system. Then, the Lyapunov–Krasovskii stability theory and stochastic analysis techniques are employed to derive and formulate control design conditions in terms of linear matrix inequalities (LMIs). Finally, a superheater steam temperature control is utilized to assess the applicability of the proposed method.

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Distributed Security Control for Complex Cyber-physical Systems against Denial-of-service Attacks

Cited by 8 publications

References 40 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

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

Event‐triggered networked cascade control systems design subject to hybrid attacks

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