Enterprise security faces a watershed as AI tools mature from passive analytics to autonomous operatives in both offense and defense. To date, traditional ...
Today’s global cybersecurity threat landscape is now defined by stealth and sophistication, fundamentally reshaping how organizations approach risk ...
This project implements an Intrusion Detection System using machine learning algorithms to detect malicious network activities. It analyzes network traffic patterns, packet headers, and flow data to ...
The accelerating sophistication of cyberattacks poses unprecedented challenges for national security, critical infrastructures, and global digital resilience. Traditional signature-based defenses have ...
Abstract: Cyber attacks on power grids are imminent and potentially have a severe impact, as evidenced by the cyber attacks in Ukraine in 2015, 2016, and 2022. In response to this challenge, machine ...
Hertz and other agencies are increasingly relying on scanners that use high-res imaging and A.I. to flag even tiny blemishes, and customers aren’t happy. By Gabe Castro-Root The next time you rent a ...
1 Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA. 2 Department of Computer Science, Kettering University, Flint, MI, USA. Intelligent vehicles require strong ...
This project is a real-time Intrusion Detection System (IDS) that captures and analyzes live network traffic to detect suspicious activity using machine learning. It is built on top of a custom Python ...
Abstract: This research pioneers an NS2 (Network Simulator 2)-driven Network Intrusion Detection System (NIDS) for smart city cybersecurity, leveraging NS2's discrete-event simulation to model ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results