基于逻辑决策支持向量的机器学习网络安全系统

Sahaya Sheela M, Hemanand D, Ranadheer Reddy Vallem
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引用次数: 1

摘要

如今,我们正朝着防止数字攻击的网络安全方向发展,以保护发展中地区的系统、网络和数据。技术和流程的集合是网络安全的核心。网络安全系统是网络和计算机(主机)安全的一个特征。网络犯罪导致数十亿美元的损失。鉴于这些犯罪,计算机系统的安全已成为必不可少的减少和避免网络犯罪的影响。我们提出了物流决策支持向量(LDSV)算法来处理这个问题。最初,我们收集了KDD Cup 99数据集来创建网络入侵检测,例如渗透或攻击,这是一个在“非恶意”和“恶意”标准链接之间变化的预测模型。该方法根据攻击对象的行为特征来确定攻击类别。在第二步中,应该清除数据预处理中的错误,并将原始数据转换为准备好的数据集。特征选择(FS)技术改进了入侵检测系统(IDS)的特征选择过程,使得使用卡方均值检验(MAC)方法更方便。最后,对基于LDSV的网络入侵检测方法进行了分类和检测。提出的LDSV仿真是基于精度F-Measure,召回率和准确性的最佳结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cyber Security System Based on Machine Learning Using Logistic Decision Support Vector
Nowadays, we are moving towards cybersecurity against digital attacks to protect systems, networks, and data in developing areas. A collection of technologies and processes is at the core of cybersecurity. A network security system is a feature of network and computer (host) security. Cybercrime leads to billion-dollar losses. Given these crimes, the security of computer systems has become essential to reduce and avoid the impact of cybercrime. We propose the Logistics Decision Support Vector (LDSV) algorithm dealing with this problem. Initially, we collected the KDD Cup 99 dataset to create a network intrusion detection, such as penetrations or attacks, a prognosis model that varies between the "Non Malicious" and "Malicious" standard links. These method finds the cyber-attack category based on the behavior features. In the second step, data preprocessing should be cleaned from errors, and raw data should be converted into a prepared dataset. The third step is Feature Selection (FS) techniques often improve the feature selection process in an Intrusion Detection System (IDS) that is more convenient for using the mean of the Chi-square test (MAC) method. Finally, a classification is done to classify and detect the network intrusion detection based on LDSV for Cyber security. The proposed LDSV simulation is based on the Precision F-Measure, Recall, and Accuracy for the best result.
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