AI-driven threat detection and response

This Collection supports and amplifies research related to SDG9 - Industry, Innovation & Infrastructure. 
 

AI-Driven Threat Detection and Response is a rapidly evolving research field focused on leveraging artificial intelligence to identify, assess, and neutralize cybersecurity threats with speed and precision beyond human capabilities. By integrating AI-powered threat detection systems, organizations can continuously analyze vast and dynamic data streams, uncovering subtle attack patterns and zero-day exploits in near real-time. This enables real-time response and paves the way for autonomous defense mechanisms, where AI agents not only detect intrusions but also initiate mitigation actions—such as isolating compromised nodes or reconfiguring firewalls—without human intervention. The current landscape is marked by the deployment of machine learning models in endpoint protection, network traffic analysis, and behavior-based anomaly detection. Deep learning has shown particular promise in modeling complex attack vectors and detecting polymorphic malware, while reinforcement learning is increasingly applied to adaptive defense strategies. However, these advancements also give rise to adversarial AI, in which malicious actors craft inputs to deceive models, prompting active research into robust model architectures and adversarial training.

This Collection aims to attract original submissions focused on the development of tightly integrated, multi-agent autonomous defense frameworks capable of collaborating across networks and systems in a decentralized manner. It also welcomes studies exploring continuous learning in non-stationary threat landscapes, privacy-preserving model training across organizations, and the detection of adversarial behavior at both the input and model-response levels.