When LiDAR is NOT for vehicles

Purdue University creates cybersecurity detection system
Purdue University’s LIDAR system, which stands for lifelong, intelligent, diverse, agile and robust, is an intelligent detection system that alerts organizations to cyberattacks. (Purdue University)

Think of LiDAR and first thing that comes to mind is a light-based system that detects obstacles to help vehicles safely navigate roads around obstacles and through adverse conditions. But to Purdue University researchers, LiDAR—which they  term as LIDAR—does not stand for light imaging, detection and ranging.

Instead, Purdue’s LIDAR is an AI-based detection system that alerts organizations to cyberattacks and stands for lifelong, intelligent, diverse, agile, and robust.

“The name for this architecture for network security really defines its significant attributes,” said Aly El Gamal, an assistant professor of electrical and computer engineering in Purdue’s College of Engineering, in an article on the university’s website. “Our system is robust and able to adapt to different environments through lifelong learning.”

Fierce AI Week

Register today for Fierce AI Week - a free virtual event | August 10-12

Advances in AI and Machine Learning are adding an unprecedented level of intelligence to everything through capabilities such as speech processing and image & facial recognition. An essential event for design engineers and AI professionals, Engineering AI sessions during Fierce AI Week explore some of the most innovative real-world applications today, the technological advances that are accelerating adoption of AI and Machine Learning, and what the future holds for this game-changing technology.

RELATED: Consumers Fear Cyberattacks Disrupting Celebrations

El Gamal created the technology with Arif Ghafoor, a professor in electrical and computer engineering, and Ali Elghariani, a graduate of electrical and computer engineering.

Purdue University designed LIDAR for computer systems and networks, including wireless networks. The system works with preprocessing components that are designed to be resilient to adversarial attacks and a cross-layer feature extraction mechanism for wireless networks.

LIDAR comprises three main parts: supervised machine learning, unsupervised machine learning and rule-based learning.

The supervised machine-learning component uses an algorithm to compare abnormalities detected in the system to known attack templates. The unsupervised component uses an algorithm to detect any anomalies in the overall system being monitored.

Purdue’s LIDAR system also uses a curiosity-driven honeypot, which lures attackers but does not let them infiltrate the system.

The innovators are working with the Purdue Research Foundation Office of Technology Commercialization to patent the technology.

Suggested Articles

Hydrogen refueling stations are limited in the U.S., restricting interest in use of fuel cell electric cars

Silicon Labs is providing the BT module needed for detecting proximity with another Maggy device

Test automation won't fix everything, but can help, according to an automation engineer. Here are five problems to avoi to improve chances of success