In EDS ansehen

VISUALIZATION AND CLUSTERING FOR SNMP INTRUSION DETECTION.

Gespeichert in:
Bibliographische Detailangaben
Titel: VISUALIZATION AND CLUSTERING FOR SNMP INTRUSION DETECTION.
Autoren: Sánchez, Raúl1, Herrero, Álvaro1, Corchado, Emilio2
Quelle: Cybernetics & Systems. Oct2013, Vol. 44 Issue 6/7, p505-532. 28p.
Schlagwörter: *DATA visualization, *SIMPLE Network Management Protocol (Computer network protocol), *INTRUSION detection systems (Computer security), *COMPUTATIONAL intelligence, *DATA analysis, *K-means clustering, *COMPUTER network protocols
Abstract: Accurate intrusion detection is still an open challenge. The present work aims at being one step toward that purpose by studying the combination of clustering and visualization techniques. To do that, the mobile visualization connectionist agent-based intrusion detection system (MOVICAB-IDS), previously proposed as a hybrid intelligent IDS based on visualization techniques, is upgraded by adding automatic response thanks to clustering methods. To check the validity of the proposed clustering extension, it has been applied to the identification of different anomalous situations related to the simple network management network protocol by using real-life data sets. Different ways of applying neural projection and clustering techniques are studied in the present article. Through the experimental validation it is shown that the proposed techniques could be compatible and consequently applied to a continuous network flow for intrusion detection. [ABSTRACT FROM AUTHOR]
Datenbank: Academic Search Index
Beschreibung
Abstract:Accurate intrusion detection is still an open challenge. The present work aims at being one step toward that purpose by studying the combination of clustering and visualization techniques. To do that, the mobile visualization connectionist agent-based intrusion detection system (MOVICAB-IDS), previously proposed as a hybrid intelligent IDS based on visualization techniques, is upgraded by adding automatic response thanks to clustering methods. To check the validity of the proposed clustering extension, it has been applied to the identification of different anomalous situations related to the simple network management network protocol by using real-life data sets. Different ways of applying neural projection and clustering techniques are studied in the present article. Through the experimental validation it is shown that the proposed techniques could be compatible and consequently applied to a continuous network flow for intrusion detection. [ABSTRACT FROM AUTHOR]
ISSN:01969722
DOI:10.1080/01969722.2013.803903