Open AccessFeature extraction for enhanced malware detection using genetic algorithm
Author(s) -
Prerna Srivastava,
M. M. Anishin Raj
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.8.10479
Subject(s) - malware , computer science , process (computing) , signature (topology) , feature extraction , data mining , feature (linguistics) , field (mathematics) , artificial intelligence , pattern recognition (psychology) , the internet , machine learning , computer security , mathematics , linguistics , philosophy , geometry , world wide web , pure mathematics , operating system
The use of internet has affected almost every field today. With the increase in use of internet, the number of malwares affecting the systems has also increased to a great deal. A number of techniques have been developed by the researchers in order to detect these malwares. The Malware Detection consists of two parts, the analysis part and the detection part. Malwares analysis can be categorized into Static analysis, Dynamic analysis and Hybrid Analysis. The Detection techniques can broadly be classified into Signature based techniques and Behaviour based techniques. A brief introduction of Malware Detection techniques is addressed here. The process of Feature Extraction plays a very important role in determining the efficiency and accuracy of the Malware Detection process. It aims at determining the subset of features that helps better differentiate between the malicious and benign files. We aim to provide a Feature Extraction process based on Genetic process that can be used for Malware Detection.