research-article

Detecting worm variants using machine learning

Authors:

Joseph SventekAuthors Info & Claims

CoNEXT '07: Proceedings of the 2007 ACM CoNEXT conference

Article No.: 2, Pages 1 - 12

https://doi.org/10.1145/1364654.1364657

Published: 10 December 2007 Publication History

Abstract

Network intrusion detection systems typically detect worms by examining packet or flow logs for known signatures. Not only does this approach mean worms cannot be detected until the signatures are created, but that variants of known worms will remain undetected since they will have different signatures. The intuitive solution is to write more generic signatures. This solution, however, would increase the false alarm rate and is therefore practically not feasible. This paper reports on the feasibility of using a machine learning technique to detect variants of known worms in real-time.

Support vector machines (SVMs) are a machine learning technique known to perform well at various pattern recognition tasks, such as text categorization and handwritten digit recognition. Given the efficacy of SVMs in standard pattern recognition problems this work applies SVMs to the worm detection problem. Specifically, we investigate the optimal configuration of SVMs and associated kernel functions to classify various types of synthetically generated worms. We demonstrate that the optimal configuration for real time detection of variants of known worms is to use a linear kernel, and unnormalized bi-gram frequency counts as input.

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Cited By

Yeboah-Ofori ASwart COpoku-Boateng FIslam S(2022)Cyber resilience in supply chain system security using machine learning for threat predictionsContinuity & Resilience Review10.1108/CRR-10-2021-00344:1(1-36)Online publication date: 9-Feb-2022
https://doi.org/10.1108/CRR-10-2021-0034
Abdullahi Yari IDehling TKluge FGeck JSunyaev AEskofier B(2021)Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic ReviewJournal of Medical Internet Research10.2196/2446023:11(e24460)Online publication date: 15-Nov-2021
https://doi.org/10.2196/24460
Yeboah-Ofori ABoachie C(2019)Malware Attack Predictive Analytics in a Cyber Supply Chain Context Using Machine Learning2019 International Conference on Cyber Security and Internet of Things (ICSIoT)10.1109/ICSIoT47925.2019.00019(66-73)Online publication date: May-2019
https://doi.org/10.1109/ICSIoT47925.2019.00019
Show More Cited By

Detecting worm variants using machine learning
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Social and professional topics
  1. Computing / technology policy

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Published In

cover image ACM Conferences

CoNEXT '07: Proceedings of the 2007 ACM CoNEXT conference

December 2007

448 pages

ISBN:9781595937704

DOI:10.1145/1364654

General Chairs:
Jim Kurose
University of Massachusetts
,
Henning Schulzrinne
Columbia University

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 December 2007

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Cited By

Yeboah-Ofori ASwart COpoku-Boateng FIslam S(2022)Cyber resilience in supply chain system security using machine learning for threat predictionsContinuity & Resilience Review10.1108/CRR-10-2021-00344:1(1-36)Online publication date: 9-Feb-2022
https://doi.org/10.1108/CRR-10-2021-0034
Abdullahi Yari IDehling TKluge FGeck JSunyaev AEskofier B(2021)Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic ReviewJournal of Medical Internet Research10.2196/2446023:11(e24460)Online publication date: 15-Nov-2021
https://doi.org/10.2196/24460
Yeboah-Ofori ABoachie C(2019)Malware Attack Predictive Analytics in a Cyber Supply Chain Context Using Machine Learning2019 International Conference on Cyber Security and Internet of Things (ICSIoT)10.1109/ICSIoT47925.2019.00019(66-73)Online publication date: May-2019
https://doi.org/10.1109/ICSIoT47925.2019.00019
Naval SLaxmi VGupta NGaur MRajarajan MPoet RMuttukrishnan R(2014)Exploring Worm Behaviors using DTWProceedings of the 7th International Conference on Security of Information and Networks10.1145/2659651.2659737(379-384)Online publication date: 9-Sep-2014
https://dl.acm.org/doi/10.1145/2659651.2659737
Beaver JSymons CGillen R(2013)A learning system for discriminating variants of malicious network trafficProceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop10.1145/2459976.2460003(1-4)Online publication date: 8-Jan-2013
https://dl.acm.org/doi/10.1145/2459976.2460003
Beaver JBorges-Hink RBuckner M(2013)An Evaluation of Machine Learning Methods to Detect Malicious SCADA CommunicationsProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2013.105(54-59)Online publication date: 4-Dec-2013
https://dl.acm.org/doi/10.1109/ICMLA.2013.105

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