LKM: A LDA-Based<i>K</i>-Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks

Zhang, Yuhua; Wang, Kun; Gao, Min; Ouyang, Zhiyou; Chen, Siguang

doi:10.1155/2015/491910

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…First, character features were converted into digital features, and finally 41 features were converted into 122 digital features. Then, in order to eliminate the problem of too large numerical span between different data, the average variance method [14] was used for standard operation of data:…”

Section: Experimental Stepsmentioning

confidence: 99%

Security detection of network intrusion: application of cluster analysis method

Yang¹

2020

Computer Optics

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In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.

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Section: Experimental Stepsmentioning

confidence: 99%

Security detection of network intrusion: application of cluster analysis method

Yang¹

2020

Computer Optics

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“…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. In [11], the author proposed a linear discriminant analysis (LDA)‐based K‐means clustering algorithm. Firstly, the author uses LDA dimension reduction to classify high‐dimensional datasets into two‐dimensional datasets.…”

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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“…IDS detects whether there is an attack behavior in the network and immediately performs response processing. Recently, a large number of techniques are applied to intrusion detection, such as rule-based network intrusion detection technology [8], artificial immune system [9], clustering-based technology [10,11], Support Vector Machine (SVM) [12,13], neural network [14][15][16], etc. Currently, among many intrusion detection algorithms, neural network-related algorithms have been widely used because of their good robustness and adaptability.…”

Section: Introductionmentioning

confidence: 99%

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

Zheng

Zhou

Wang

et al. 2020

Sensors

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The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms.

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LKM: A LDA-BasedK-Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks

Cited by 5 publications

References 18 publications

Security detection of network intrusion: application of cluster analysis method

Security detection of network intrusion: application of cluster analysis method

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

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

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