Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2014.050102
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 1, 2014.
Abstract: The attackers do not want their Malicious software (or malwares) to be reviled by anti-virus analyzer. In order to conceal their malware, malware programmers are getting utilize the anti reverse engineering techniques and code changing techniques such as the packing, encoding and encryption techniques. Malware writers have learned that signature based detectors can be easily evaded by “packing” the malicious payload in layers of compression or encryption. State-of-the-art malware detectors have adopted both static and dynamic techniques to recover the payload of packed malware, but unfortunately such techniques are highly ineffective. If the malware is packed or encrypted, then it is very difficult to analyze. Therefore, to prevent the harmful effects of malware and to generate signatures for malware detection, the packed and encrypted executable codes must initially be unpacked. The first step of unpacking is to detect the packed executable files. The objective is to efficiently and accurately distinguish between packed and non-packed executables, so that only executables detected as packed will be sent to an general unpacker, thus saving a significant amount of processing time. The generic method of this paper show that it achieves very high detection accuracy of packed executables with a low average processing time. In this paper, a packed file detection technique based on complexity measured by several algorithms, and it has tested using a packed and unpacked dataset of file type .exe. The preliminary results are very promising where achieved high accuracy with enough performance. Where it achieved about 96% detection rate on packed files and 93% detection rate on unpacked files. The experiments also demonstrate that this generic technique can effectively prepared to detect unknown, obfuscated malware and cannot be evaded by known evade techniques.
Dr. Mafaz Mohsin Khalil Al-Anezi, “Generic Packing Detection Using Several Complexity Analysis for Accurate Malware Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 5(1), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050102