FDF: Frequency detection-based filtering of scanning worms

Byungseung Kim; Saewoong Bahk; Hyogon Kim

doi:10.1109/ICC.2006.255084

FDF: Frequency detection-based filtering of scanning worms

Byungseung Kim, Saewoong Bahk, Hyogon Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a simple algorithm for detecting scanning worms with high detection rate and low false positive rate. The novelty of our algorithm is inspecting the frequency characteristic of scanning worms from a monitored network. Its low complexity allows it to be used on any networkbased intrusion detection system as a real time detection module for high-speed networks. Our algorithm need not be adjusted to network status because its parameters depend on application types, which are generally and widely used in any networks such as web and P2P services. By using real traces, we evaluate the performance of our algorithm and compare it with that of SNORT. The results confirm that our algorithm outperforms SNORT with respect to detection rate and false positive rate.

Original language	English
Title of host publication	2006 IEEE International Conference on Communications, ICC 2006
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2124-2129
Number of pages	6
ISBN (Print)	1424403553, 9781424403554
DOIs	https://doi.org/10.1109/ICC.2006.255084
Publication status	Published - 2006
Event	2006 IEEE International Conference on Communications, ICC 2006 - Istanbul, Turkey Duration: 2006 Jul 11 → 2006 Jul 15

Publication series

Name	IEEE International Conference on Communications
Volume	5
ISSN (Print)	0536-1486

Other

Other	2006 IEEE International Conference on Communications, ICC 2006
Country/Territory	Turkey
City	Istanbul
Period	06/7/11 → 06/7/15

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering

Access to Document

10.1109/ICC.2006.255084

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FDF: Frequency detection-based filtering of scanning worms. / Kim, Byungseung; Bahk, Saewoong; Kim, Hyogon.
2006 IEEE International Conference on Communications, ICC 2006. Institute of Electrical and Electronics Engineers Inc., 2006. p. 2124-2129 4024479 (IEEE International Conference on Communications; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, B, Bahk, S & Kim, H 2006, FDF: Frequency detection-based filtering of scanning worms. in 2006 IEEE International Conference on Communications, ICC 2006., 4024479, IEEE International Conference on Communications, vol. 5, Institute of Electrical and Electronics Engineers Inc., pp. 2124-2129, 2006 IEEE International Conference on Communications, ICC 2006, Istanbul, Turkey, 06/7/11. https://doi.org/10.1109/ICC.2006.255084

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title = "FDF: Frequency detection-based filtering of scanning worms",

abstract = "In this paper, we propose a simple algorithm for detecting scanning worms with high detection rate and low false positive rate. The novelty of our algorithm is inspecting the frequency characteristic of scanning worms from a monitored network. Its low complexity allows it to be used on any networkbased intrusion detection system as a real time detection module for high-speed networks. Our algorithm need not be adjusted to network status because its parameters depend on application types, which are generally and widely used in any networks such as web and P2P services. By using real traces, we evaluate the performance of our algorithm and compare it with that of SNORT. The results confirm that our algorithm outperforms SNORT with respect to detection rate and false positive rate.",

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year = "2006",

doi = "10.1109/ICC.2006.255084",

language = "English",

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series = "IEEE International Conference on Communications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AB - In this paper, we propose a simple algorithm for detecting scanning worms with high detection rate and low false positive rate. The novelty of our algorithm is inspecting the frequency characteristic of scanning worms from a monitored network. Its low complexity allows it to be used on any networkbased intrusion detection system as a real time detection module for high-speed networks. Our algorithm need not be adjusted to network status because its parameters depend on application types, which are generally and widely used in any networks such as web and P2P services. By using real traces, we evaluate the performance of our algorithm and compare it with that of SNORT. The results confirm that our algorithm outperforms SNORT with respect to detection rate and false positive rate.

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ER -

FDF: Frequency detection-based filtering of scanning worms

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