TY - GEN
T1 - Network-based Intrusion detection - Modeling for a larger picture
AU - Totsuka, Atsushi
AU - Ohwada, Hidenari
AU - Fujita, Nobuhisa
AU - Chakraborty, Debasish
AU - Keeni, Glenn Mansfield
AU - Shiratori, Norio
N1 - Publisher Copyright:
© Proceedings of the 16th Conference on Systems Administration, LISA 2002. All rights reserved.
PY - 2002
Y1 - 2002
N2 - The Internet is changing computing more than ever before. As the possibilities and the scopes are limitless, so too are the risks and chances of malicious intrusions. Due to the increased connectivity and the vast spectrum of financial possibilities, more and more systems are subject to attack by intruders. One of the commonly used method for intrusion detection is based on anomaly. Network based attacks may occur at various levels, from application to link levels. So the number of potential attackers or intruders are extremely large and thus it is almost impossible to ''profile'' entities and detect intrusions based on anomalies in host-based profiles. Based on meta-information, logical groupings has been made for the alerts that belongs to same logical network, to get a clearer and boarder view of the perpetrators. To reduce the effect of probably insignificant alerts a threshold technique is used.
AB - The Internet is changing computing more than ever before. As the possibilities and the scopes are limitless, so too are the risks and chances of malicious intrusions. Due to the increased connectivity and the vast spectrum of financial possibilities, more and more systems are subject to attack by intruders. One of the commonly used method for intrusion detection is based on anomaly. Network based attacks may occur at various levels, from application to link levels. So the number of potential attackers or intruders are extremely large and thus it is almost impossible to ''profile'' entities and detect intrusions based on anomalies in host-based profiles. Based on meta-information, logical groupings has been made for the alerts that belongs to same logical network, to get a clearer and boarder view of the perpetrators. To reduce the effect of probably insignificant alerts a threshold technique is used.
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M3 - Conference contribution
AN - SCOPUS:85094558031
T3 - Proceedings of the 16th Conference on Systems Administration, LISA 2002
SP - 227
EP - 232
BT - Proceedings of the 16th Conference on Systems Administration, LISA 2002
PB - USENIX Association
T2 - 16th Systems Administration Conference, LISA 2002
Y2 - 3 November 2002 through 8 November 2002
ER -