Metamorphic malware detection based on support vector machine classification of malware sub-signatures

• Main Authors: Khammas, Ban Mohammed, Monemi, Alireza, Ismail, Ismahani, Mohd. Nor, Sulaiman, Marsono, Muhammad Nadzir
• Format: Article
• Language: English
• Published: Universitas Ahmad Dahlan 2016
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/71495/1/IsmahaniIsmail2016_Metamorphicmalwaredetectionbasedon.pdf; http://eprints.utm.my/id/eprint/71495/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994645699&doi=10.12928%2ftelkomnika.v14.i3.3850&partnerID=40&md5=9bddd91d72dd3d7765283346cee06803

Achieving accurate and efficient metamorphic malware detection remains a challenge. Metamorphic malware is able to mutate and alter its code structure in each infection that can circumvent signature matching detection. However, some vital functionalities and code segments remain unchanged between mu...