Artificial Immune System based General Purpose Intrusion Detection System

In this paper, we present an accurate and realtime PE-Miner framework that automatically extracts distinguishing features from portable executables (PE) to detect zero-day malware without any a priori knowledge about them. The distinguishing features are extracted using the structural information standardized by the Microsoft Windows operating system for executables, DLLs and object files. We follow a threefold research methodology: (1) identify a set of structural features for PE files, which is computable in realtime, (2) use an efficient preprocessor for removing redundancy in the features’ set, and (3) select an efficient data mining algorithm for final classification. The primary objective of PE-Miner is to distinguish between the benign and malicious executables; while its secondary task is to categorize the malicious executables as a function of their payload. We evaluated PE-Miner on two malware collections, VX Heavens and Malfease datasets that contain 11 and 5 thousand malicious PE files respectively. The results of our experiments show that PE-Miner achieves more than 99% detection rate with less than 0.5% false alarm rate for distinguishing between the benign and malicious executables. Furthermore, it achieves an average detection rate of 90% with an average false alarm rate of less than 5% for categorizing the malicious executables as a function of their payload. It is important to emphasize that PE-Miner has low processing overheads and takes only 0.244 seconds on the average to scan a given PE file. Finally, we evaluate the robustness and reliability of PE-Miner under several regression tests. Our results show that extracted features are “robust” to different packing techniques and PE-Miner is also resilient to majority of “crafty” evasion strategies. ∗The research presented in this paper is conducted at nexGIN RC and is funded by National ICT R&D Fund, Ministry of Information Technology, Government of Pakistan under the grant # ICTRDF/AN/2007/37. The information, data, comments, and views detailed herein may not necessarily reflect the endorsements of views of the National ICT R&D Fund.