Collection Overview

Scientific Reports has launched a Guest-Edited Collection on AI-driven threat detection and response.

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 will be a Collection of original research papers and will be open for submissions from all authors – on the condition that the manuscripts fall within the scope of the Collection and of Scientific Reports more generally. We are welcoming submissions until 15th July 2026.

Why are you excited about this Collection? 

"Cyberattacks now move at machine speed and can overwhelm human analysts, so AI is becoming essential for spotting subtle, fast-evolving threats and coordinating response. I’m excited about this Collection because it brings together work on ML-driven detection, autonomous and multi-agent defense, continuous learning in non-stationary environments, and robustness to adversarial AI. By showcasing rigorous evaluations and deployable methods, the collection can help push the field from isolated detectors to resilient, end-to-end security systems. Researchers should submit here to reach a broad AI and cybersecurity audience and to publish alongside complementary advances shaping next-generation defenses."- Dr. Mahawaga Arachchige Pathum Chamikara

This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure.

Why submit to a Collection? 

Collections like this one help promote high-quality science. They are led by Guest Editors, who are experts in their fields, and In-House Editors and are supported by a dedicated team of Commissioning Editors and Managing Editors at Springer Nature. Collection manuscripts typically see higher citations, downloads, and Altmetric scores, and provide a one-stop-shop on a cutting-edge topic of interest. 

Who is involved? 

Guest Editors:

• Mahawaga Arachchige Pathum Chamikara, CSIRO, Australia
• Biju Issac, Northumbria University, UK
• Balasubramaniam S, Kerala University of Digital Sciences, India

Internal Team:

• In-House Editor: Chenyu Wang, Scientific Reports, USA
• Commissioning Editor: Quintina Dawson, Fully OA Brands, Springer Nature, UK
• Managing Editor: Eleanor Smith, Fully OA Brands, Springer Nature, UK

How can I submit my paper? 

Visit the Collection page to find out more about this Collection and how to submit your article.