Detection of Advanced Malware by Machine Learning Techniques
This addresses the limitation of signature-based anti-malware tools for cybersecurity, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of detecting advanced unknown malware, specifically metamorphic malware, by using machine learning techniques based on opcode frequency analysis, achieving nearly 100% accuracy with several classifiers on a Kaggle dataset.
In today's digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. metamorphic malware. In this paper, we study the frequency of opcode occurrence to detect unknown malware by using machine learning technique. For the purpose, we have used kaggle Microsoft malware classification challenge dataset. The top 20 features obtained from fisher score, information gain, gain ratio, chi-square and symmetric uncertainty feature selection methods are compared. We also studied multiple classifier available in WEKA GUI based machine learning tool and found that five of them (Random Forest, LMT, NBT, J48 Graft and REPTree) detect malware with almost 100% accuracy.