Date of Submission
Spring 5-10-2023
Degree Type
Thesis
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
Committee Chair/First Advisor
Dr. Dan Lo
Track
Others
Machine Learning and Cybersecurity
Chair
Dr. Yong Pei
Committee Member
Dr. Selena He
Committee Member
Dr. Jiho Noh
Abstract
The number one threat to the digital world is the exponential increase in ransomware attacks. Ransomware is malware that prevents victims from accessing their resources by locking or encrypting the data until a ransom is paid. With individuals and businesses growing dependencies on technology and the Internet, researchers in the cyber security field are looking for different measures to prevent malicious attackers from having a successful campaign. A new ransomware variant is being introduced daily, thus behavior-based analysis of detecting ransomware attacks is more effective than the traditional static analysis. This paper proposes a multi-variant classification to detect ransomware I/O operations from benign applications. The deep learning models implemented in the proposed approach are Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Networks (CNN). The deep learning models are compared against a classic machine learning model such as Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). The ransomware samples contain 70 binaries from 30 different ransomware extracted during the encryption of an extensive network shared directory. The benign samples came from network traffic traces recorded in a campus LAN where staff users access files from shared servers. A sample contains I/O operations (short Control Commands, bytes being read, and written) per second over a period of T seconds. The proposed deep learning models are tested with Zero-day ransomware samples as well. Both Bi-LSTM and CNN achieved above 98% in accurately classifying ransomware and benign samples.