Neural Network based Obfuscated Malware detection

Autor:	Parvathala Balakesavareddy, Srinivas Kolli, D. Saravanan
Rok vydání:	2021
Předmět:	Training set System change Change detection algorithms Artificial neural network business.industry Computer science computer.software_genre Automation Signature (logic) ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS Software Malware Data mining business computer
Zdroj:	2021 International Conference on System, Computation, Automation and Networking (ICSCAN).
DOI:	10.1109/icscan53069.2021.9526496
Popis:	Obfuscated Malwares are latest trend in parasite writers to thwart detection by anti-virus software. There are various techniques which are used commonly to detect malwares namely Signature based techniques, System change detection techniques. Signature based techniques fails as obfuscated malware continuously change it signature. System change detection techniques fails due to it relying on system functionality. In this article, I proposed technique for detecting obfuscated malware using similarity based neural network algorithm. Results indicate that false rate drastically reduce to 8% when training data increases.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d05740c7c321f23e946901430fb68db2 https://doi.org/10.1109/icscan53069.2021.9526496 Zobrazit plný text záznamu