Title :
Applied artificial immune on P2P network virus detection technology for information security
Author :
Qiao Peili ; Sun Wanting
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Univ. of Sci. & Technol. Harbin, Harbin, China
Abstract :
Existing virus detection methods to detect malicious using signature cannot adapt to the virus technology development, it cannot detect virus effectively, especially for new virus. Inspired by natural immune, an improved distributed network detection model of P2P network using immune and virus code relevance is proposed. The feature detection function and various parts such as feature extraction and information fusion are described in this paper. The relevance between virus codes is used to extract the feature of computer virus. We match the feature of malicious code automatically by Rabin fingerprinting algorithm and adopt a strategy of distributed information fusion on subsequent fingerprint based on computer immune. Finally, we propose an improved malicious code detection algorithm in distributed system. This malicious code detection method is suitable for large-scale network.
Keywords :
artificial immune systems; computer viruses; feature extraction; peer-to-peer computing; security of data; sensor fusion; P2P network virus detection technology; Rabin fingerprinting algorithm; applied artificial immune; computer virus; distributed network detection model; feature detection function; feature extraction; immune code relevance; information fusion; information security; malicious code detection algorithm; malicious detection; natural immune; virus code relevance; virus detection methods; virus technology development; Computers; Feature extraction; Immune system; Law; Peer-to-peer computing; Training; Artificial Immune; Code Relevance; Distributed Detection; Virus Detection; information security;
Conference_Titel :
Strategic Technology (IFOST), 2014 9th International Forum on
Conference_Location :
Cox´s Bazar
Print_ISBN :
978-1-4799-6060-6
DOI :
10.1109/IFOST.2014.6991107