MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWARE
Abdurrahman Pektaş1, Elif Nurdan Pektaş2 and Tankut Acarman1
1Department of Computer Engineering, Galatasaray University, İstanbul, Turkey
2Siemens Turkey, Yakack Caddesi No: 111, 34870 Kartal, Istanbul, Turkey
ABSTRACT
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples.
KEYWORDS
Android, Malware, Frequent Sequence Mining, Behavioural Pattern, API Calls, Dynamic Analysis
Original Source Link : http://aircconline.com/ijnsa/V10N4/10418ijnsa01.pdf
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