| Title | Automatically Generating Malware Summary Using Semantic Behavior Graphs (SBGs) |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Yang, Ping, Shu, Hui, Kang, Fei, Bu, Wenjuan |
| Conference Name | 2020 Information Communication Technologies Conference (ICTC) |
| Keywords | behavior association, behavior description, data mining, feature extraction, graph theory, Grippers, Heuristic algorithms, Human Behavior, Malware, malware analysis, malware summary, Metrics, Microsoft Windows, privacy, pubcrawl, resilience, Resiliency, semantic behavior graph, Semantics |
| Abstract | In malware behavior analysis, there are limitations in the analysis method of control flow and data flow. Researchers analyzed data flow by dynamic taint analysis tools, however, it cost a lot. In this paper, we proposed a method of generating malware summary based on semantic behavior graphs (SBGs, Semantic Behavior Graphs) to address this issue. In this paper, we considered various situation where behaviors be capable of being associated, thus an algorithm of generating semantic behavior graphs was given firstly. Semantic behavior graphs are composed of behavior nodes and associated data edges. Then, we extracted behaviors and logical relationships between behaviors from semantic behavior graphs, and finally generated a summary of malware behaviors with true intension. Experimental results showed that our approach can effectively identify and describe malicious behaviors and generate accurate behavior summary. |
| DOI | 10.1109/ICTC49638.2020.9123267 |
| Citation Key | yang_automatically_2020 |
Automatically Generating Malware Summary Using Semantic Behavior Graphs (SBGs)