عنوان فارسی مقاله: خوشه بندی نرم افزارهای مخرب و طبقه بندی


عنوان انگلیسی مقاله:

Malware clustering and classification



 

بخشی از مقاله

Results

Accuracy vs. #Clusters Error rate reduces as number of clusters increase. 

Accuracy vs. Maximum #Events Error rate reduces as the event cap increases, because the more events we observe, the more accurately we can capture the behavior of the malware.

Accuracy Gain vs. Number of Events The gain in accuracy is more substantial at lower event caps (100 vs. 500) than at higher event caps (500 vs. 1000), which indicates that between 100 to 500 events, the clustering had most of the information it needs to form good quality clusters. 

Accuracy vs. Number of Families The 11-family experiment outperforms in accuracy the 3-family experiment in high event cap tests (1000), but the result is opposite in lower event cap tests (100). As we investigate further, we found that the same outliers were found in both experiments, and because there were more semantic clusters (11 vs. 3), the outlier effects were contained.





 

کلمات کلیدی: 

Malware Analysis, Clustering and Classification: A Literature Review https://www.researchgate.net/.../273266602_Malware_Analysis_Clustering_and_Classifi... Malware Analysis, Clustering and Classification: A Literature Review on ResearchGate, the professional network for scientists. Study of dynamic malware clustering and classification. - DR-NTU https://repository.ntu.edu.sg/handle/10356/54592 Malware or malicious software is one of the major threats in the internet today and there are thousands of malware samples introduced every day. Antivirus ... Scalable, Behavior-Based Malware Clustering - UCSB Computer ... https://www.cs.ucsb.edu/~chris/research/doc/ndss09_cluster.pdf by U Bayer - ‎Cited by 483 - ‎Related articles to examine malware, dynamic malware analysis tools such as CWSandbox [3] ... into malware fam- ilies is not a new idea, and clustering and classification. Malware Classification based on Call Graph Clustering https://arxiv.org › cs by J Kinable - ‎2010 - ‎Cited by 88 - ‎Related articles Aug 25, 2010 - This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to ... Searches related to Malware clustering and classification malware classification tree malware classification machine learning malware family classification google scholar