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A Machine Learning approach for detecting a Malware:
The project is to improve the way we detect script based malware using Machine Learning. Malware has become one of the most active channel to deliver threats like Banking Trojans and Ransomware. The talk is aimed at finding a new and effective way to detect the malware. We started with acquiring both malicious and clean samples. Later we performed feature identification, while building on top of existing knowledge base of malware. Then we performed automated feature extraction. After certain feature set is obtained, we teased-out feature which are categorical, interdependent or composite. We applied varying machine learning models, producing both binary and categorical outcomes. We cross validated our results and re-tuned our feature set and our model, until we obtained satisfying results, with least false-positives. We concluded that not all the extracted features are significant, in fact some features are detrimental on the model performance. Once such features are factored-out, it results not only in better match, but also provides a significant gain in performance.