Detecting DDoS attack based on PSO Clustering algorithm

Hao, Xiaohong; Meng, Boyu; Gu, Kaicheng

doi:10.2991/icmemtc-16.2016.133

Cited by 4 publications

(6 citation statements)

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“…This approach refines the K initial cluster centers in a data set by each case that will enter the nearest cluster center after first identifying the initial cluster centres. To identify DDoS attacks of unknown sessions, Hao et al [43] developed a detection algorithm. Suggested a method for identifying DDoS attacks using the clustering algorithm of K-means, and they attained a 97.83% accuracy rate [33].…”

Section: K-mean Clusteringmentioning

confidence: 99%

DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison

2023

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The security of the internet is seriously threatened by a distributed denial of service (DDoS) attacks. The purpose of a DDoS assault is to disrupt service and prevent legitimate users from using it by flooding the central server with a large number of messages or requests that will cause it to reach its capacity and shut down. Because it is carried out by numerous bots that are managed (infected) by a single botmaster using a fake IP address, this assault is dangerous because it does not involve a lot of work or special tools. For the purpose of identifying and analyzing DDoS attacks, this paper will discuss various machine learning (ML) and deep learning (DL) techniques. Additionally, this study analyses and comparatives the significant distinctions between ML and DL techniques to aid in determining when one of these techniques should be used.

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Section: K-mean Clusteringmentioning

confidence: 99%

DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison

2023

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“…In Hao et al's study [36] for achieving pattern recognition on weblog data for differentiating the normal and abnormal or malicious data, the user sessions are extracted from the log record. k-means clustering techniques are applied with a hybrid form of particle swarm optimization to generate an efficient attack detection model.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Hao et al [49] proposed security against denial of service attack by using a cross-layer approach as the best solution. e cross-layer approach was the combined form of device-driver packet filter (cuckoo-based filter) and remotely firewall.…”

Section: Cuckoo Algorithmmentioning

confidence: 99%

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Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Dixit

Kohli

Acevedo-Duque

et al. 2021

Security and Communication Networks

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Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. Therefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. These attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning and metaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. This paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. This paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. This study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. The techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.

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“…Finally, we can classify normal and abnormal network behaviour according to the results of cluster. Literature [10] is established in the different between the normal user's browsing mode and the abnormal user's browsing mode, extracts the user sessions from the normal user's network log. This algorithm uses the particle swarm optimisation K‐means clustering algorithm to generate a detection model to detect if the user behaviour is abnormal or not.…”

Section: Related Workmentioning

confidence: 99%

Method for detecting abnormal behaviour of users based on selective clustering ensemble

Xie

Zhu

et al. 2018

IET Networks

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With the development of cloud computing in the mobile communications industry, the credibility between users and the cloud has become an important obstacle to the development of mobile cloud services. Therefore, the user's abnormal behaviour detection is particularly important. The authors propose a selective clustering ensemble algorithm based on fractal dimension. In their proposed algorithm, they firstly use the sliding window to dynamically obtain data to improve the accuracy of user behaviour acquisition. Secondly, they use the initial clustering stage and incremental clustering stage to produce clustered members. Thirdly, they also use the Duun_index to select the clustered members, then the selection of high-quality clustered members with the voting algorithm to get the final result. Finally, they use the average difference of change to determine whether the current behaviour is abnormal based on the clustering results. The experimental results show that the proposed scheme has a better performance than the traditional clustering algorithm in clustering accuracy. Moreover, their algorithm can improve the detection rate and accuracy rate, respectively.

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Detecting DDoS attack based on PSO Clustering algorithm

Cited by 4 publications

References 0 publications

DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison

DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Method for detecting abnormal behaviour of users based on selective clustering ensemble

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