Researchers at the Indian Institute of Technology (IIT), Madras have developed an algorithm-powered novel strategy to mitigate disruptions in critical networks such as air traffic control and power distribution during a targeted attack.

According to officials, with the Internet of Things (IoT)-based technologies being widely implemented across societies, creating networks that are resilient to such attacks is of paramount importance. The strategy developed by IIT Madras has been tested on two infrastructure networks of air traffic and power distribution.

"The terror attacks of 11th September 2001, all of which happened only on one day targeting a single country resulted in the entire airline industry coming to a standstill. Such threats are a reminder that in today’s highly interconnected world, there exists a high risk of one adverse event leading to the disruption of the entire network. Air traffic, road traffic, power distribution infrastructure and even social media platforms are all examples of highly connected networks and are, therefore, highly vulnerable to targeted attacks," Karthik Raman, Core Member, Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, said.

"A variety of technological networks form the backbone of modern world infrastructure, and it is very essential to build safeguards to protect these networks against both failures and targeted attacks," he added.

"The algorithm takes a network whose spare capacity has to be determined as an input and gives out a modified network with added spare capacity, the cost of spare capacity for the network etc. Importantly, the algorithm also optimizes the cost associated with adding spare capacity," he said.

Sai Saranga Das, a student at IIT Madras and lead author of the study said, "Through this study, we have addressed the interplay between the addition of dormant spare capacity in a network and the associated capital and operational costs. Our future course of study would be to apply our algorithm in the context of biological networks to gain potentially incisive insights about them.”

"It was found that the algorithm increased the robustness of these networks to targeted attacks. The algorithm was also highly effective in increasing the robustness of ‘canonical scale-free networks,’ which are representative of many real-world networks when compared to existing strategies to mitigate targeted attacks on these networks," he added.