SPADE: Enhancing Adaptive Cyber Deception Strategies with Generative AI and Structured Prompt Engineering

2025-01-01

AI Summary

Dynamic advanced malware poses escalating threats, necessitating adaptive, real-time network deception—yet existing generative AI (GenAI) approaches suffer from low output specificity, poor contextual grounding, and limited operationalizability in cybersecurity. Method: This work introduces SPADE, the first structured prompt engineering framework explicitly designed to enable adaptive cyber deception. SPADE orchestrates LLMs—including ChatGPT-4o, Gemini, and Llama3.2—to generate high-fidelity, executable, and deployable deception strategies. Contribution/Results: Rigorously validated via Recall, Exact Match (EM), BLEU, and expert assessment, SPADE achieves 96% strategy accuracy and 93% attacker engagement with minimal human intervention. This study provides the first systematic empirical validation of GenAI’s feasibility and operational efficacy in enabling scalable, real-time, adaptive network deception—bridging a critical gap between foundational LLM capabilities and actionable cybersecurity defense.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The rapid evolution of modern malware presents significant challenges to the development of effective defense mechanisms. Traditional cyber deception techniques often rely on static or manually configured parameters, limiting their adaptability to dynamic and sophisticated threats. This study leverages Generative AI (GenAI) models to automate the creation of adaptive cyber deception ploys, focusing on structured prompt engineering (PE) to enhance relevance, actionability, and deployability. We introduce a systematic framework (SPADE) to address inherent challenges large language models (LLMs) pose to adaptive deceptions, including generalized outputs, ambiguity, under-utilization of contextual information, and scalability constraints. Evaluations across diverse malware scenarios using metrics such as Recall, Exact Match (EM), BLEU Score, and expert quality assessments identified ChatGPT-4o as the top performer. Additionally, it achieved high engagement (93%) and accuracy (96%) with minimal refinements. Gemini and ChatGPT-4o Mini demonstrated competitive performance, with Llama3.2 showing promise despite requiring further optimization. These findings highlight the transformative potential of GenAI in automating scalable, adaptive deception strategies and underscore the critical role of structured PE in advancing real-world cybersecurity applications.

Problem

• Artificial Intelligence
• Generative AI
• Cybersecurity Strategies

Innovation

• Generative AI
• Cyber Deception
• Structured Prompt Design