Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

Similarity and self-learning based anti-Trojan Mechanism

Authors
Yiying Zhang, Yeshen He, Qing Zhao, Kun Liang
Corresponding Author
Yiying Zhang
Available Online November 2016.
DOI
https://doi.org/10.2991/aest-16.2016.100How to use a DOI?
Keywords
anti-trojan mechanism; security; trojan attack; self-learning mechanism.
Abstract
Trojans inject systems and launch various attacks, such as eavesdropping secret information, tampering with system configuration etc., which threats to system security seriously. In this paper, a novel anti-Trojan malware mechanism was proposed based on attribute behaviour and cosine similarity. Firstly, according to the initial rules base and application behaviour, the mechanism regularized the operations of application, and then, the mechanism invoked rules to judges suspicious behaviours based on current rules base and operational impact. Once the application was considered as Trojan malware, the system would dispatch the appropriate algorithm for processing. The mechanism triggered by sensitive behaviours, and had the active prevention function and self-learning function. The analysis and experiment show the solution can detect Trojan malware effectively.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-257-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/aest-16.2016.100How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yiying Zhang
AU  - Yeshen He
AU  - Qing Zhao
AU  - Kun Liang
PY  - 2016/11
DA  - 2016/11
TI  - Similarity and self-learning based anti-Trojan Mechanism
BT  - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.100
DO  - https://doi.org/10.2991/aest-16.2016.100
ID  - Zhang2016/11
ER  -