Social engineering exploitation detection (SEED) in Malaysia's SMEs using machine learning

• Main Author: Sia, Ken Yen
• Format: Final Year Project / Dissertation / Thesis
• Published: 2022
• Subjects: Q Science (General); T Technology (General)
• Online Access: http://eprints.utar.edu.my/4664/1/fyp_CS_2022_SKY.pdf; http://eprints.utar.edu.my/4664/

Over the past years, computer security has been a field of study that assists in protecting one’s information. It has matured over time in fighting against cybercrime in exploiting the technical vulnerabilities of hardware or software. However, there is a kind of attack that particularly exploits the human psychological weakness in acquiring confidential information is emerging which is called social engineering. It has lesser cost and branches to many variations of type of attacks than the traditional technical approach which challenges organization’s protection such as SMEs. Most of the current detection models only provide a guideline framework in detecting such attacks which is not efficient or with low accuracy. This project aims at building a model that is based on another popular field which is machine learning in detecting attacks. This can be applied to flag a conversation as if it is a social engineering attack. The project will use natural language processing in extracting certain features as the input of an algorithm to generate a reputation score that will be trained using machine learning to build the detection model. The model will be evaluated and validated using datasets by generating the result scores.