@inproceedings{2ceb08b1cf6b48d3b0c1ec1db0277f4d,
title = "Malware detection based on objective-oriented association mining",
abstract = "Signature matching methods are inadequate to detect unseen malwares. In this paper an API (Application Programming Interface) based data mining method is proposed to detect unseen malwares. The data mining algorithm, objective-oriented associate mining (OOA), is employed to mine association rules for detecting malwares. To find association rules with strong discrimination power, an improved algorithm for frequent item generation is presented. In this algorithm a frequent item is evaluated by its support and its classification capability. The experiments prove that the proposed methods are effective and can be used to detect malware variants and unknown malicious executable.",
keywords = "Classification, Machine learning, Malware detection, Objective-oriented associate mining, Security, Tracking",
author = "Xiao Xiao and Ding Yuxin and Zhang Yibin and Tang Ke and Dai Wei",
note = "Publisher Copyright: {\textcopyright} 2013 IEEE.; 12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 ; Conference date: 14-07-2013 Through 17-07-2013",
year = "2013",
doi = "10.1109/ICMLC.2013.6890497",
language = "英语",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "375--380",
booktitle = "Proceedings - International Conference on Machine Learning and Cybernetics",
address = "美国",
}