DSpace Repository

A Frequent Pattern Based Extension of Snort for Intrusion Detection

Show simple item record

dc.contributor.author Chettiba, Youcef
dc.contributor.author Ben Atallah, Abdennour
dc.date.accessioned 2022-02-17T09:13:59Z
dc.date.available 2022-02-17T09:13:59Z
dc.date.issued 2019
dc.identifier.uri https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/695
dc.description.abstract Snort is a lightweight, open source, rule-based intrusion detection system. In principle, malicious traffic is recognized thanks to a manually elaborated set of rules by an expert. In this thesis, we develop a different approach, which consists of automatic generation of snort rules. The basic idea is to use frequent pattern algorithms to extract a set of characterization rules of attack packets using traffic data analysis. We design a framework which includes a preprocessing phase and frequent pattern mining phase. We use the LBLN dataset and two class of mining algorithms: all frequent patterns (Apriori, FPGrowth, FIN), and maximal frequent patterns (FPMax) as implemented in the SPMF library. The set of experiments in both linux and windows shows that the quality of the system is sensitive to the minimum support value. We reach the best result using the FIN algorithm with an accuracy of 0.75 when the minimum support is equal to 0.4. ... EN_en
dc.publisher جامعة غرداية EN_en
dc.subject Frequent patterns mining, Intrusion detection, Snort, Network Traffic Analysis EN_en
dc.subject ,HPñ J Ë@ ,ÉÊ Ë@ ­ » , èPQº JÖÏ@ AÖ ß B@ á« I . J ® J JË@ : éJ kA J ®Ó HAÒÊ¿ HA¾J . Ë@ ¯Y K ÉJ Êm EN_en
dc.title A Frequent Pattern Based Extension of Snort for Intrusion Detection EN_en
dc.type Thesis EN_en


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account