Malware Analysis using Multiple API Sequence Mining Control Flow Graph
This work addresses malware detection for cybersecurity, but it appears incremental as it builds on existing API sequence mining methods.
The paper tackles malware detection by identifying families through shared API sequence characteristics, using n-grams and similarity scores to analyze malware effectively.
Malwares are becoming persistent by creating full- edged variants of the same or different family. Malwares belonging to same family share same characteristics in their functionality of spreading infections into the victim computer. These similar characteristics among malware families can be taken as a measure for creating a solution that can help in the detection of the malware belonging to particular family. In our approach we have taken the advantage of detecting these malware families by creating the database of these characteristics in the form of n-grams of API sequences. We use various similarity score methods and also extract multiple API sequences to analyze malware effectively.