Malware Detection Using Random Forest
Akash Dixit, Sukhwinder Singh · 2023
Malware is a major concern for internet users, and the emergence of polymorphic malware has made it even more challenging to detect and combat. In order to identify and mitigate these malicious threats, we employed Random Forest machine learning technique. The confusion matrix provided valuable insights by measuring the number of false positives and false negatives, thereby assessing the system's effectiveness. Our study demonstrated the potential of using Random Forest for malware analysis and detection, thereby enhancing computer network security. Particularly, the results showcased the effectiveness of Random Forest algorithm, which achieved a 98% detection accuracy and displayed a reduced false positive rate in the dataset provided.