Security News > 2016 > July > How online learning algorithms can help improve Android malware detection (Help Net Security)
2016-07-01 13:45
A group of researchers from Nanyang Technological University, Singapore, have created a novel solution for large-scale Android malware detection. It’s called DroidOL, and it’s an adaptive and scalable malware detection framework based on online learning. “DroidOL’s achieves superior accuracy through extracting high quality features from inter-procedural control-flow graphs (ICFGs) of apps, which are known to be robust against evasion and obfuscation techniques adopted by malware,” the researchers explained. They used the Weisfeiler-Lehman (WL) graph kernel … More →
News URL
http://feedproxy.google.com/~r/HelpNetSecurity/~3/knnTmwSR884/
Related news
- SpyLoan Android malware on Google play installed 8 million times (source)
- 8 Million Android Users Hit by SpyLoan Malware in Loan Apps on Google Play (source)
- New DroidBot Android banking malware spreads across Europe (source)
- New DroidBot Android malware targets 77 banking, crypto apps (source)
- Germany sinkholes BadBox malware pre-loaded on Android devices (source)
- Germany blocks BadBox malware loaded on 30,000 Android devices (source)
- Hackers Exploit Webview2 to Deploy CoinLurker Malware and Evade Security Detection (source)
- Vanir: Open-source security patch validation for Android (source)
- Android malware found on Amazon Appstore disguised as health app (source)
- BadBox malware botnet infects 192,000 Android devices despite disruption (source)