论文标题
使用基于规则的机器学习在SNMP-MIB数据集中检测网络异常
Detecting Network Anomalies using Rule-based machine learning within SNMP-MIB dataset
论文作者
论文摘要
针对网络犯罪分子限制网络性能的最有效威胁之一是拒绝服务（DOS）攻击。因此，这种类型的攻击可能会严重损害数据安全，完整性和效率。本文开发了一个网络流量系统，该系统依赖于采用的数据集将DOS攻击与正常流量区分开。检测模型是使用五个基于规则的机器学习分类器（DecisionTable，Jrip，Oner，Part和Zeror）构建的。这些发现表明，使用零件分类器以大约99.7％的高精度识别ICMP变量是在ICMP攻击，HTTP洪水攻击和慢速果中实现的。此外，部分分类器已成功地将不同DOS攻击的正常流量分类为100％。
One of the most effective threats that targeting cybercriminals to limit network performance is Denial of Service (DOS) attack. Thus, data security, completeness and efficiency could be greatly damaged by this type of attacks. This paper developed a network traffic system that relies on adopted dataset to differentiate the DOS attacks from normal traffic. The detection model is built with five Rule-based machine learning classifiers (DecisionTable, JRip, OneR, PART and ZeroR). The findings have shown that the ICMP variables are implemented in the identification of ICMP attack, HTTP flood attack, and Slowloris at a high accuracy of approximately 99.7% using PART classifier. In addition, PART classifier has succeeded in classifying normal traffic from different DOS attacks at 100%.
