ANTI-OBFUSCATION MODEL FOR MALWARE DETECTION
BOOTH NO : H4
Category : Information Communication Technology & Multimedia
Malware which considered as a significant threat in security system has aggressively spread the attacks where millions of new malwares are being developed every day. The worse scenario is the new malware is highly sophisticated, unable to be detected and obfuscated. They hide their behavior from virtual environment and security tools. Therefore, anti-obfuscation model is proposed to overcome those issues. The supervised learning classification is applied which focusing on Support Vector Machine algorithm for malware detection after reviewing the encrypted, oligomorphic, polymorphic and metamorphic of malwares. The chosen obfuscation technique also been identified and analyzed to generate the most relevant procedures, which able to construct, learn, and detect a wide range of obfuscations. It is equally important that the malware detection is conducted at a reasonable speed and with precise accuracy.
The current trend of obfuscated malware as a modern furtive malware attack, is their ability to camouflage their behaviour and hide from detection which led to the new malware instants or updated malware be detected especially when static analysis is used in malware detection. Furthermore, obfuscated malware will be revised to be suit for the popular infrastructures in smart devices. Thus, it becomes very hard to analyze the malware for getting the useful information in order to design the malware detection system because of anti-static and anti-dynamic analysis technique. The chosen classification technique for malware detection also contributed to these issues. Besides, in the advanced malwares, the technologies of obfuscation malware have become sophisticated and complex based on the growth of the hardware and software technologies.
The growth of malware distribution from 2017 to 2022 (https://www.av-test.org/en/statistics/malware/), to show the important of malware detection solutions
The report on obfuscation techniques. (https://www.mcafee.com/enterprise/en-us/lp/threats-reports/apr-2021.html)
business model will be prepared, so that the idea of this innovation can be proposed to targeted organization or company in Malaysia
Any organization that implements this innovation may reduce issues related to cyber security which provide safe environment on computer system and smart devices specifically on the performance of malware detection accuracy rates.
Competitors – Anti-obfuscation software such as PreEmptive, GuardSquare, antivirus and other researchers from other universities that run this kind of case study.
Cyber Security Malaysia, National Cyber Security Agency (NACSA) , Anti-virus companies,Other security system companies
ALI BIN SELAMAT
MALAYSIA-JAPAN INTERNATIONAL INSTITUTE OF TECHNOLOGY
ALI BIN SELAMAT
MALAYSIA-JAPAN INTERNATIONAL INSTITUTE OF TECHNOLOGY
NOR ZAKIAH BINTI GORMENT
MALAYSIA-JAPAN INTERNATIONAL INSTITUTE OF TECHNOLOGY