document: IMDS : intelligent malware detection system The proliferation of malware has presented a serious threat to the security of computer systems . Traditional signature-based anti-virus systems fail to detect polymorphic and new , previously unseen malicious executables . In this paper , resting on the analysis of Windows API execution sequences called by PE files , we develop the Intelligent Malware Detection System ( IMDS ) using Objective-Oriented Association ( OOA ) mining based classification . IMDS is an integrated system consisting of three major modules : PE parser , OOA rule generator , and rule based classifier . An OOA algorithm is adapted to efficiently generate OOA rules for classification . A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of King-Soft Corporation is performed to compare various malware detection approaches . Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system out perform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan , as well as previous data mining based detection systems which employed Naive Bayes , Support Vector Machine ( SVM ) and Decision Tree techniques .
doc_bio_tags: O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O