2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC) (2015)
July 1, 2015 to July 5, 2015
As information technology improves, the Internet is involved in every area in our daily life. When the mobile devices and cloud computing technology start to play important parts of our life, they have become more susceptible to attacks. In recent years, phishing and malicious websites have increasingly become serious problems in the field of network security. Attackers use many approaches to implant malware into target hosts in order to steal significant data and cause substantial damage. The growth of malware has been very rapid, and the purpose has changed from destruction to penetration. The signatures of malware have become more difficult to detect. In addition to static signatures, malware also tries to conceal dynamic signatures from anti-virus inspection. In this research, we use hooking techniques to trace the dynamic signatures that malware tries to hide. We then compare the behavioural differences between malware and benign programs by using data mining techniques in order to identify the malware. The experimental results show that our detection rate reaches 95% with only 80 attributes. This means that our method can achieve a high detection rate with low complexity.
Malware, Monitoring, Feature extraction, Accuracy, Data mining, Training, Bayes methods
C. Fan, H. Hsiao, C. Chou and Y. Tseng, "Malware Detection Systems Based on API Log Data Mining," 2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC), Taichung, Taiwan, 2015, pp. 255-260.