Enhanced Learning Vector Quantization for Detecting Intrusions In IDS

Abstract

Nowadays, computer infrastructure attacks have become more challenging with computer network extension. The detection of intrusion is the most important process against threats. Several traditional methods are there, but still, there is an issue in detecting the errors related to security. In this article, the intrusion detection system is performed through various detection methods. The efficient proposed method is the Enhanced Learning Vector Quantization (ELVQ) algorithm for detecting the intrusions presented in network traffic. Also, the Pearson Correlation Coefficient Function (PCCF) for similarity determination is introduced. The proposed ELVQ classification achieves higher classification accuracy. In the end, risk factors are analyzed using Hidden Bernoulli Model (HBM). The proposed system is evaluated with KDD CUP99 dataset for efficient results. The evaluation results proved the proposed method performance with various measures and compared with various methods such as Random forest, Random tree, MLP, and Naïve Bayes.

Class Image Processing Application of XSS Intrusion Intelligent Detection for Big Data in Campus Network Construction

“…And in the logistics sector, smart connectivity has the potential to improve the efficiency and flexibility of the delivery of goods, making logistics faster and cheaper. At the same time, the frequency and scale of attacks in the network also show an increasing trend year by year [1]. For example, in the Internet security threat report released by the United States in 2019, it was pointed out that: (1) in terms of malware, the frequency of malware attacks decreased slightly, and the number of blackmail software attacks decreased by 20% for the first time since 2013, but the number of attacks against enterprises increased by 12%.…”

“…At the same time, the frequency and scale of attacks in the network also show an increasing trend year by year [1]. For example, in the Internet security threat report released by the United States in 2019, it was pointed out that: (1) in terms of malware, the frequency of malware attacks decreased slightly, and the number of blackmail software attacks decreased by 20% for the first time since 2013, but the number of attacks against enterprises increased by 12%. In 2018, the number of mine hijacking attacks was four times that of 2017.…”