2021/03/07 by Sunhera Paul, Paul, Sunhera, Mark Stamp +1
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Spam and Phishing Detection #cs.CR #cs.LG
paper · pdf · doi:10.48550/arxiv.2103.05759
arxiv created 2021/03/07 · openalex publication_date 2021/03/07 · arxiv updated 2021/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Malware detection is a critical aspect of information security. One difficulty that arises is that malware often evolves over time. To maintain effective malware detection, it is necessary to determine when malware evolution has occurred so that appropriate countermeasures can be taken. We perform a variety of experiments aimed at detecting points in time where a malware family has likely evolved, and we consider secondary tests designed to confirm that evolution has actually occurred. Several malware families are analyzed, each of which includes a number of samples collected over an extended period of time. Our experiments indicate that improved results are obtained using feature engineering based on word embedding techniques. All of our experiments are based on machine learning models, and hence our evolution detection strategies require minimal human intervention and can easily be automated.