Analysis of Machine Learning Techniques Based Intrusion Detection Systems

被引：14
|
作者：
Sharma, Rupam Kr. [1 ]
Kalita, Hemanta Kumar [1 ]
Borah, Parashjyoti [2 ]
机构：
[1] North Eastern Hills Univ, Shillong, Meghalaya, India
[2] Assam Don Bosco Univ, Gauhati, India
来源：
PROCEEDINGS OF 3RD INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING, NETWORKING AND INFORMATICS, ICACNI 2015, VOL 2 | 2016年 / 44卷
关键词：
Intrusion detection system; Supervised learning; Unsupervised learning; KDD'99; Anomaly detection; Host intrusion system;
D O I：
10.1007/978-81-322-2529-4_51
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Attacks on Computer Networks are one of the major threats on using Internet these days. Intrusion Detection Systems (IDS) are one of the security tools available to detect possible intrusions in a Network or in a Host. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate as well as low false positive rate. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by authors at different times.
引用
收藏
页码：485 / 493
页数：9
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