Autonomous, Adaptive, and Real-time Cognitive Intrusion Detection System for the Cloud

Currently, cloud and Internet service providers typically use antivirus, firewall, packet inspection and intrusion detection systems (IDSs) to protect against cyber-security threats on the cloud and internet. These protection systems rely on the knowledge of a fixed and known database of threats to detect malicious activity, but they have no ability to detect new, mutated, threats dynamically. Once an undetected cyber-attack has caused damage and has subsequently been identified by technicians or third party sources, only then can these systems be updated by going offline. The proposed research will develop online, adaptive, self-learning, and self-evolving security software to detect intrusion, viruses, and cyber-attacks, both new and already known, without requiring going offline to update the database. Canadian Tire Corporation (CTC) will benefit from this research, because, with the introduction of the proposed software into their clouds, CTC will be able to provide higher level of security for its clientele, and thus, CTC will lead the industry to protect against cyber-threats with better security solutions for new, unidentified and mutated threats

Intern: 
Muhammad Salman Khan
Faculty Supervisor: 
Dr. Ken Ferens
Project Year: 
2014
Province: 
Manitoba
Partner: 
Program: