Park et al., 2010 - Google Patents
Antibot: Clustering common semantic patterns for bot detectionPark et al., 2010
View PDF- Document ID
- 10590459262704489850
- Author
- Park Y
- Zhang Q
- Reeves D
- Mulukutla V
- Publication year
- Publication venue
- 2010 IEEE 34th Annual Computer Software and Applications Conference
External Links
Snippet
Among malicious software (malware), autonomous malicious programs, called bots, are a serious problem in the Internet. The bot writers have developed a variety of techniques to evade simple signature-based detection. Concise representations of malware behavior, or …
- 238000001514 detection method 0 title abstract description 58
Classifications
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- G06F21/55—Detecting local intrusion or implementing counter-measures
- G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
- G06F21/562—Static detection
- G06F21/563—Static detection by source code analysis
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- G06F21/55—Detecting local intrusion or implementing counter-measures
- G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
- G06F21/566—Dynamic detection, i.e. detection performed at run-time, e.g. emulation, suspicious activities
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- G06F21/55—Detecting local intrusion or implementing counter-measures
- G06F21/554—Detecting local intrusion or implementing counter-measures involving event detection and direct action
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- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
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- G06F21/52—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity ; Preventing unwanted data erasure; Buffer overflow
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