Machine Learning for Network Intrusion Detection-A Comparative Study

被引:3
|
作者
Al Lail, Mustafa [1 ]
Garcia, Alejandro [1 ]
Olivo, Saul [1 ]
机构
[1] Texas A&M Int Univ, Sch Engn, Laredo, TX 78041 USA
来源
FUTURE INTERNET | 2023年 / 15卷 / 07期
基金
美国国家科学基金会;
关键词
machine learning; network intrusion detection; cybersecurity; ALGORITHM;
D O I
10.3390/fi15070243
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Modern society has quickly evolved to utilize communication and data-sharing media with the advent of the internet and electronic technologies. However, these technologies have created new opportunities for attackers to gain access to confidential electronic resources. As a result, data breaches have significantly impacted our society in multiple ways. To mitigate this situation, researchers have developed multiple security countermeasure techniques known as Network Intrusion Detection Systems (NIDS). Despite these techniques, attackers have developed new strategies to gain unauthorized access to resources. In this work, we propose using machine learning (ML) to develop a NIDS system capable of detecting modern attack types with a very high detection rate. To this end, we implement and evaluate several ML algorithms and compare their effectiveness using a state-of-the-art dataset containing modern attack types. The results show that the random forest model outperforms other models, with a detection rate of modern network attacks of 97 percent. This study shows that not only is accurate prediction possible but also a high detection rate of attacks can be achieved. These results indicate that ML has the potential to create very effective NIDS systems.
引用
收藏
页数:17
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