LIDAR: Adaptable Intrusion Detection System Suitable for Diverse Settings

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Track Code	2019-GHAF-68713
Categories	Computer Technology Electrical Engineering
Keywords	Computer Technology Cybersecurity Electrical Engineering Intrusion Detection Machine Learning Network Attacks Network Security Wireless Network Security
Public Abstract	Researchers at Purdue University have developed LIDAR, an adaptable intrusion detection system suitable for diverse settings, including wireless networks. This technology can detect a wide range of diverse attacks and allows the intrusion detection system to learn how to best identify the already known attacks as well as recognize unknown/zero-day attacks and capture their behavior for future identification. The robustness of the technology is ensured by using lifelong learning updates, input pre-processing components that are designed to be resilient to adversarial attacks, and a cross-layer feature extraction mechanism for networks with a wireless communication medium. Furthermore, this technology is able to adapt to the environment through lifelong learning, and relies on very little assumptions for the attacker behavior. Advantages: -Adaptable -Explore generic zero day attack behavior -Suitable for diverse settings -Robust Potential Applications: -Intrusion detection system
People	Ghafoor, Arif (Project leader) El Gamal, Aly Elghariani, Ali A
Intellectual Property	Application Date Nov 25, 2020 Type Utility Patent Country of Filing United States Patent Number (None) Issue Date (None) Application Date Feb 3, 2020 Type Provisional-Patent Country of Filing United States Patent Number (None) Issue Date (None)
Contact OTC	Purdue Office of Technology Commercialization The Convergence Center 101 Foundry Drive, Suite 2500 West Lafayette, IN 47906 Phone: (765) 588-3475 Fax: (765) 463-3486 Email: otcip@prf.org