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Conference Paper
Visualization of Malware Detection Based on API Call Sequence Alignment
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- Authors
- Jong-Hyun Kim, Hyun-Joo Kim, Ik-Kyun Kim
- Issue Date
- 2016-12
- Citation
- International Conference on Internet (ICONI) 2016, pp.175-178
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently, the threats of the cyber-attacks such as cyber personal information disclosure, bank fraud, DDoS attacks and APT attack are occurring continuously. There are many different types of cyberattacks, but the cause of those attacks is accounted for the malicious code. Therefore, the aggressive defense technology which deals with cyber-attacks increasing exponentially is required. This paper proposes a malware detection system based on API call sequence alignment and extracting malicious behavioral patterns. It also describes 3D security visualization technology regarding the cyber genome technology for detecting known and unknown malwares.
- KSP Keywords
- API call sequence, Behavioral Patterns, Cyber attacks, DDoS attacks, Detection Systems(IDS), Malicious code, Malware detection, Personal Information Disclosure, Security visualization, Sequence Alignment, visualization technology