Call for papers: AI-driven threat detection and response Collection
Published in Sustainability and Computational Sciences
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 is this Collection important?
"From my perspective, this collection represents a critical frontier in cybersecurity — using AI not just to spot malicious activity, but to respond automatically and adaptively as digital threats evolve. As cyber-attacks grow more sophisticated, traditional rule-based defences struggle to keep up; AI offers the potential to continuously monitor vast network data, uncover zero-day exploits or subtle attack patterns, and even trigger mitigation steps in real time. I am excited about this collection because it brings together cutting-edge research that could transform cybersecurity from a reactive to a proactive and autonomous discipline. If broadly adopted, this could significantly strengthen resilience across industries, infrastructure, and national security — especially in complex, high-stakes environments. Researchers should submit to this collection to contribute toward building the next generation of intelligent, scalable, and adaptive defence systems that the world increasingly needs."
- Dr. Biju Issac, Guest Editor
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:
- Pathum Chamikara Mahawaga Arachchige, CSIRO, Australia
- Biju Issac, Northumbria University, United Kingdom
- 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 for more information on the Collection, and how to submit your article.
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Scientific Reports
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A Collection of original research articles on the development of tightly integrated, multi-agent autonomous defense frameworks capable of collaborating across networks and systems in a decentralized manner.
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From Counting Vulnerabilities to Calculating the Existential Net Security Balance
AI security tools announce: We discovered 500 vulnerabilities. We pose the existential question that the machine (B) cannot ask itself: What is the net security balance of your intervention?
We prove in this research that the technical system (B) is trapped within a limited existential space: it computes quantity (complexity, capacity) in linear mechanical time (t), yet remains blind to quality, place, and existential time (τ). It moves without awareness of the critical moment when it crosses its existential saturation threshold without knowing where it stands in the system's flow, for it calculates computational location while remaining ignorant of existential place:
ε_i(t) = max(ε_min, ε_0i · e^{−γ_i L_i(t)})
At this crossing, the system loses its internal logical coherence and hallucinates, generating new vulnerabilities as corrupted outputs:
f_i(t) ~ Poisson(λ_i(t)) where λ_i(t) = β_i · max(0, (S_i(t) − ε_i(t))/ε_i(t))
The existential catastrophe is this: these newly created vulnerabilities remain invisible to (B) itself. They emerge within a mathematically blind zone described by Bouzid's First Theorem:
f(B) ∉ 𝒪(B) ← Side effects lie outside the system's domain of self-knowledge
This is not a technical flaw to be patched, but an existential limit: any attempt to program a self-monitor inside (B) becomes part of the problem itself, subject to the same collapse threshold.
The Irrefutable Empirical Evidence: The Prevailing Methodology Creates the Vulnerabilities It Claims to Fix
Three rigorously documented studies confirm that 40% of automated fixes generate new vulnerabilities:
• Symbolic analysis tools (KLEE): Announced 56 vulnerabilities, yet created 17 new ones omitted from their report.
• Programming assistant (GitHub Copilot): While patching SQL injection flaws, introduced path traversal vulnerabilities in 40% of cases.
• Dynamic fuzzing tools: During filesystem testing, corrupted on-disk structures and triggered actual data loss.
The conventional methodology trusts naively in counting discoveries. We reject this illusory trust and shift to calculating the net security balance:
Net Balance = Discovered Vulnerabilities (D_i) − Created Vulnerabilities (C_i)
This calculation is self-impossible for (B), as it requires knowledge of C_i—a knowledge existentially forbidden to it.
The Structural Solution: Existential-Mechanical Integration B + F = N_f
The solution lies not in making (B) smarter, but in introducing the human sovereign factor (F) as an external existential event. (F) operates in contextually situated existential time (τ), possessing what the machine lacks:
• Knowledge of place: Understanding the system's holistic context and priorities
• Calculation of existential time: Recognizing the critical moment τ for intervention before collapse
• Vision of hallucination: Detecting corrupted data before it materializes as vulnerability
This translates into a three-layer dynamical model:
• Internal Alert (B): Alert_i(t) = 𝟙_{S_i(t) ≥ ε_i(t)}
• Existential Decision (F): d_i(τ) = F(Alert, Context, History)
• Normative Integrity (N_f): n_{f_i}(t) = ρ₁(t)·ρ₂(t)·ρ₃(t)·‖y_i(t)‖
Normative integrity (N_f) is not a simple transformation equation, but an existential state that accumulates only when (F) enforces three purity conditions:
• Absence of current hallucination (ρ₁)
• Effectiveness of prior preventive intervention (ρ₂)
• Contextual alignment with pre-established ethical values (ρ₃)
The Existential Conclusion: Redefining Security as Relationship, Not Technical Property
We do not offer yet another technical improvement in the security arms race. We propose a radical re-foundation:
True security is not an internal property of the machine (B), but an existential relationship between human will (F) and execution mechanism (B).
Current systems ask: How do we make it smarter?
We ask: How do we ensure it collapses responsibly when it exceeds its existential limits?
This research transforms philosophical critique into a practical mathematical model offering:
• Quantitative fragility metrics (γ_i, ε_min)
• Programmable protocols for preventive intervention
• A computational framework for net security balance
The Challenge We Pose
Any security system that fails to disclose its methodology for calculating vulnerabilities it generates during its own search builds security on shifting sands. True security integrity begins not by denying the existential limits of our technology, but by constructing sovereign bridges (F) across these abysses—not by pretending they do not exist.
You announce: 'We discovered 500 vulnerabilities.'
We ask: How many vulnerabilities did your intervention add to the total system?
(F) knows (f)—it knows when (B) hallucinates.
(B) cannot know this—it is trapped within a closed circle.
This is not an opinion. This is an existential mathematical limit.
https://www.academia.edu/164572948/The_Net_Security_Balance_Why_B_Cannot_Compute_the_Vulnerabilities_it_Generates_During_Discovery_