A hybrid pattern based text mining approach for malware detection using DBScan
Malware classification has been a challenging problem in the recent past and several researchers have attempted to solve this problem using various tools. It is security threat which can break machine operation while not knowing user's data and it's tough to spot its behavior. Herein, a novel algorithm is anticipated to classify malwares as clean/normal malwares and polymorphic/metamorphic malwares. The approach is to generate pydasm report. The instruction sets will be extracted from the report via text mining and text preprocessing will be done for various processes like comment removal, function extraction etc. Pattern matching is applied on the instructions pattern in the generated reports. The similarity score will be calculated between any two files using signature based pattern matching technique and then a Hybrid DBScan algorithm will be applied on the selected feature for classification. For the moderation and improvement of the present system the signature based technique is most popular. The results are found to be quite accurate and better than the existing ones in terms of accuracy, precision and recall.
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