Abstract
Most of the current virus detection approaches, such as antivirus software, require precognition of virus signatures for detection, but they are difficult to detect firstly unknown virus. A novel virus detection method inspired by immune theory with GA-RVNS (Genetic Algorithm based real-valued negative selection) is proposed. Feature vectors of program codes are mapped into high dimension real-valued space. The architecture of this model, the formal definitions of self, non-self, antigen, antibody, and gene library are given. And the process of generation of detectors by GA-RVNS in real-valued space is discussed in detail. The experimental results show that the method can detect obfuscated and firstly unknown virus more effectively than traditional model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
