Data Mining, Malware Detection, Machine Learning, Classification, Instruction Sequences, Signature Extraction, Predictive Modeling, Supervised Learning, Unsupervised Learning, Feature Selection, Feature Reduction
This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval and classification techniques and borrows a number of ideas from the field. We used a vector space model to represent the programs in our collection. Our data mining framework includes two separate and distinct classes of experiments. The first are the supervised learning experiments that used a dataset, consisting of several thousand malicious and clean program samples to train, validate and test, an array of classifiers. In the second class of experiments, we proposed using sequential association analysis for feature selection and automatic signature extraction. With our experiments, we were able to achieve as high as 98.4% detection rate and as low as 1.9% false positive rate on novel malwares.
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Doctor of Philosophy (Ph.D.)
College of Sciences
Modeling and Simulation
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Siddiqui, Muazzam, "Data Mining Methods For Malware Detection" (2008). Electronic Theses and Dissertations, 2004-2019. 3709.