Antonio Lara Gutiérrez
PhD Candidate & FPU Predoctoral Researcher. AI for Cybersecurity
NICS Lab. Dept. of Languages and Computer Science. ETSI Informática, University of Málaga
My research lies at the intersection of Artificial Intelligence and Cybersecurity. I work on concept-drift detection and adaptation in AI-driven threat detection systems, multimodal malware classification and attribution through static, dynamic and visual feature fusion, generative models (GANs and LLMs) for adversarial robustness of intrusion detection and adaptive cyber deception, and LLM agents for automated threat intelligence.
My goal is to build robust, verifiable and maintainable AI defenses against advanced cyber threats.
- Concept drift
- Intrusion detection
- Malware attribution
- GANs
- LLM agents
- Cyber deception
Activities
All activities →- GenPot published in Applied IntelligenceGenPot, our generative honeypot architecture, published in Applied Intelligence (Springer, JCR Q2).
- AgentAPT presented at the Google Safety Engineering CenterPresented AgentAPT with the other awarded projects at the Google Safety Engineering Center (GSEC) Málaga.
- Paper published in Artificial Intelligence ReviewNew paper in Artificial Intelligence Review (Springer, JCR Q1, IF 18.8) on maintainable AI-driven network threat detection.
- AgentAPT awarded by the UMA–Google Cybersecurity ChairAgentAPT awarded in the competitive call of the UMA–Google (VirusTotal) Cybersecurity Chair.
Selected Publications
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GenPot: a generative honeypot architecture for adaptive web and API interaction
Applied Intelligence (Springer), 56, 419, 2026.
Adversarial red teaming and imbalance correction in heterogeneous NIDS: a class-specific adaptive GAN framework
Journal of Information Security and Applications (Elsevier), 102, 104584, 2026.
Towards maintainable AI-driven network anomaly and threat detection: a comparative analysis of datasets, preprocessing techniques, and model trade-offs
Artificial Intelligence Review (Springer), 2026.
A framework for drift detection and adaptation in AI-driven anomaly and threat detection systems
International Journal of Information Security (Springer), 24, 199, 2025.