Malicious Code Detection Using Penalized Splines on OPcode Frequency

摘要: Recently, malicious software are gaining exponential growth due to the innumerable obfuscations of extended x86 IA-32 (OPcodes) that being employed evade from traditional detection methods. In this paper, we design a novel distinguisher separate malware benign combines Multivariate Logistic Regression model using kernel HS in Penalized Splines along with OPcode frequency feature selection technique for efficiently detecting obfuscated malware. The main advantage our penalized splines based is its performance capability achieved through efficient filtering and identification most important OPcodes used obfuscation This demonstrated successful implementation experimental results proposed on large datasets. presented approach effective at identifying previously examined non-malware assist reverse engineering.