Antivirus software running on your computer has one big weak point - if a new virus is released before the antivirus provider knows about it or before the next scheduled antivirus software update, your system can be infected. Such zero-day infections are common.
The new approach employs a data mining algorithm to identify malicious code on a system and the anomaly of behaviour patterns detected is predominantly based on the rate at which various operating system functions are being "called". Their initial tests show an almost 100% detection rate and a false positive rate of just 2.5% for spotting embedded malicious code that is in "stealth mode" prior to being activated for particular malicious purposes.
"Securing computer systems against new diverse malware is becoming harder since it requires a continuing improvement in the detection engines," the team of Mamoun Alazab (ANU) and Sitalakshmi Venkatraman (NMIT) explain. "What is most important is to expand the knowledgebase for security research through anomaly detection by applying innovative pattern recognition techniques with appropriate machine learning algorithms to detect unknown malicious behaviour."
More information: Alazab M. & Venkatraman S. (2013). Detecting malicious behaviour using supervised learning algorithms of the function calls, International Journal of Electronic Security and Digital Forensics, 5 (2) 90. DOI: 10.1504/IJESDF.2013.055047
Citation: Malware bites (2013, August 15) retrieved 9 December 2021 from https://phys.org/news/2013-08-malware.html
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