Content Details | NUML Online Research Repository

Title ALGORITHM DEVELOPMENT FOR IMPROVING CYBER THREAT INTELLIGENCE AGAINST ONION SERVICE

Author(s) Muhammad Faizan Raja

Abstract Web applications are widely used in various business domains due to their affordability and platform independence. Billions of users rely on these applications to perform daily tasks. Cybersecurity is safeguarding computer systems, networks, and data from unauthorized access, theft, and other malicious attacks. The Tor network allows users to host anonymous websites known as onion services, or torch hidden services. This helps to conceal the IP addresses of the server and the client, making it difficult for third parties to intercept or monitor the conversation. There is a noticeable rise in the variety of onion services with illicit and criminal intent on the dark web. In recent years, attackers spread malware through images by embedding malicious code, tricking users into downloading harmful files, and exploiting image-related vulnerabilities. Identifying and neutralizing these threats involves various strategies, but automated malware generation techniques continue to produce malware that resists current detection technologies. It presents unique challenges for cyber threat intelligence, including the increasing difficulty of cyber threats targeting them. There is a greater need for sophisticated and precise malware classification and detection methods. This study presents a novel approach to malware image classification by employing a convolutional neural network. The results demonstrate a significant accuracy of 96% on the malware image collection. Various metrics such as precision, recall, specificity, and F1 score were employed to evaluate the model's performance. The experiment findings demonstrate the effectiveness of the suggested approach as a reliable technique for detecting malware through images. It enhances detection, analysis, and attribution capabilities, optimizing resource allocation and informing effective mitigation strategies for complex threats within the Tor network. The study illustrates how deep learning frameworks can reduce the likelihood of malware attacks

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date

Subject Software Engineering

Publisher

Contributor(s)

Format

Identifier

Source

Relation

Coverage

Rights