An auto-learning approach for network intrusion detection

被引:6
作者
Boulaiche, Ammar [1 ,2 ]
Adi, Kamel [2 ]
机构
[1] Univ Bejaia, Dept Comp Sci, Bejaia 06000, Algeria
[2] Univ Quebec Outaouais, Comp Secur Res Lab, Quebec City, PQ, Canada
关键词
Intrusion detection; Honeypots; Fuzzy hashing; DARPA'99 dataset; UNSW-NB15; dataset; LONGEST COMMON SUBSEQUENCE; SIGNATURES; GENERATION; SET;
D O I
10.1007/s11235-017-0395-z
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
In this paper, we propose a novel intrusion detection technique with a fully automatic attack signatures generation capability. The proposed approach exploits a honeypot traffic data analysis to build an attack scenarios database, used to detect potential intrusions. Furthermore, for an effective and efficient intrusion detection mechanism, we introduce several new or adapted algorithms for signature generation, signature comparison, etc. Finally, we use DARPA'99 and UNSW-NB15 traffic to evaluate the proposed approach. The results indicate that the generated attack signatures are of high quality with low rates of false negatives and false positives.
引用
收藏
页码:277 / 294
页数:18
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