Analysis of packet traffics and detection of abnormal traffics using Pareto learning self organizing maps

Authors:
Hiroshi Dozono;Masanori Nakakuni;Takaru Kabashima;Shigeomi Hara
Affiliations:
Faculty of Science and Engineering, Saga University, Saga, Japan;Information Technology Center, Fukuoka University, Fukuoka, Japan;Faculty of Science and Engineering, Saga University, Saga, Japan;Faculty of Science and Engineering, Saga University, Saga, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

Recently, the spread of the Internet makes familiar to the incident concerning the Internet, such as a DoS attack and a DDoS attack. Some methods which detect the abnormal traffics in the network using the information from headers and payloads of IP-packets transmitted in the networks are proposed. In this research, we propose a method of Pareto Learning SOM (Self Organizing Map) for IP packet flow analysis in which the occurrence rate is used for SOM computing. The flow of the packets can be visualized on the map and it can be used for detecting the abnormal flows of packets.