Keynote Speech:
Title: Computer vision based intelligent malware detection and classification.
Abstract
As malware poses a significant threat to computer security, existing defense systems primarily rely on defensive approaches like signature-based detection. However, these existing methods have limited capabilities, are costly, and are vulnerable to obfuscation techniques used by malware authors, leading to an increased risk of undetected malware attacks. The present invention is capable of detecting malware attacks with AI-based algorithms. Unlike traditional approaches, the invention utilizes multiple advanced features to characterize and model the semantic behavior of malware instead of relying on a single set of feature maps. These features include static code patterns, dynamic API call sequences, and textural patterns. It overcomes the limitations of signature-based methods, providing a more robust defense against evolving malware attacks. The Signature Matching and Scanning provides the Intelligence searches/downloads. The invention integrates hardware components such as processors and memory units.
Dr. Sanjeev Kumar
He is working as Scientist ‘E’ at C-DAC (Centre for Development of Advanced Computing), Ministry of Electronics and IT (MeitY), Government of INDIA Mohali, Punjab. He has contributed as Principal Investigator/Co-Investigator for several Government of India, funded projects in the areas of Computer Networks & Cyber Security, Software Defined Networking (SDN), and Artificial Intelligence in Cybersecurity etc. He has worked on several R & D project of National Importance. Dr. Sanjeev Kumar has extensive experiences in cybersecurity, malware analysis, honeypots, and honeynets, software defined networking, AI in cybersecurity. He has worked on the latest cybersecurity technology, such as the distributed Honeypot system, malware detection, cyber-attack data capturing, and collections through Honeypots/Honeynets, Cyber threat intelligence generation, Image analysis for malware detection and classification etc. He has guided more than 25 intern students in the completion of their project and thesis work. He has established a state-of-the-art distributed honeypot sensors at various geo-locations covering various regions/sectors for attack data capturing, malware detection and collections, and cyber threat intelligence generations. He has begged various awards for technology development in cybersecurity area. He has a wide publication in international conferences/Journals/workshops such as IEEE, Springer, Elsevier Q1 journals. He has 01 patent titled “A System and Comprehensive Methods for Malware Detection and Classification based on Artificial Intelligence”, IPR Patent No: 542238.
