Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures
Joint Authors
Mohd Nor, Sulaiman
Ismail, Ismahani
Marsono, Muhammad Nadzir
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-15
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Malware detection done at the network infrastructure level is still an open research problem ,considering the evolution of malwares and high detection accuracy needed to detect these threats.
Content based classification techniques have been proven capable of detecting malware without matching for malware signatures.
However, the performance of the classification techniques depends on observed training samples.
In this paper, a new detection method that incorporates Snort malware signatures into Naive Bayes model training is proposed.
Through experimental work, we prove that the proposed work results in low features search space for effective detection at the packet level.
This paper also demonstrates the viability of detecting malware at the stateless level (using packets) as well as at the stateful level (using TCP byte stream).
The result shows that it is feasible to detect malware at the stateless level with similar accuracy to the stateful level, thus requiring minimal resource for implementation on middleboxes.
Stateless detection can give a better protection to end users by detecting malware on middleboxes without having to reconstruct stateful sessions and before malwares reach the end users.
American Psychological Association (APA)
Ismail, Ismahani& Mohd Nor, Sulaiman& Marsono, Muhammad Nadzir. 2014. Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures. Applied Computational Intelligence and Soft Computing،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-453841
Modern Language Association (MLA)
Ismail, Ismahani…[et al.]. Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures. Applied Computational Intelligence and Soft Computing No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-453841
American Medical Association (AMA)
Ismail, Ismahani& Mohd Nor, Sulaiman& Marsono, Muhammad Nadzir. Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures. Applied Computational Intelligence and Soft Computing. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-453841
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-453841