Intrusion detection system based on new association rule mining model

Intrusion detection is a problem of great significance to protecting information systems security. Its techniques fall into two general categories: anomaly detection and misuse detection, which complement each other. This research focuses on anomaly detection techniques for intrusion detection. Pres...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2005 IEEE International Conference on Granular Computing : Beijing, China, July 25-27, 2005 Ročník 2; s. 512 - 515 Vol. 2
Hlavní autoři: Tian-rui Li, Wu-ming Pan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 2005
Témata:
Association rule
Association rules
Computer networks
Data mining
Hidden Markov models
Information security
Information systems
Intrusion detection
Itemsets
Network security
Protection
Spatial databases
ISBN:0780390172, 9780780390171
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Intrusion detection is a problem of great significance to protecting information systems security. Its techniques fall into two general categories: anomaly detection and misuse detection, which complement each other. This research focuses on anomaly detection techniques for intrusion detection. Presently, several approaches based on classical association rule mining have been proposed for intrusion detection. Because two shortages existed in classical association rule mining problem, namely every item set is treated equivalently and a uniform minimum support and minimum confidence is used as weighing standard, many rules and uninteresting rules will be generated that causes low effectiveness of intrusion detection. Based on new association rule mining model proposed by Li etc. (2002) that can solve the two shortages at the same time, a new intrusion detection system was proposed. Because the interest of item as a degree is used and the mining algorithm is based on FP-tree, our preliminary experiment results show that the proposed system is more robust and efficient than that based on APRIORI.
ISBN:0780390172
9780780390171
DOI:10.1109/GRC.2005.1547344