AETHER: Autonomous, Evolving, Tamper-proof Honeypot Ecosystem with Reactive Intelligence
Gurarpit Singh, Mehardeep Singh · 2025
Background: Modern cybersecurity landscapes are increasingly challenged by adversaries deploying sophisticated attack vectors such as Advanced Persistent Threats (APTs), polymorphic and metamorphic malware, supply chain infiltrations, and zero-day exploits. Traditional defense mechanisms-comprising Signature-based Intrusion Detection Systems (IDS), Security Information and Event Management (SIEM) platforms, firewalls, and static honeypots-operate under passive paradigms that predominantly react postincident, leading to high detection latency, false positives, and poor adaptability against novel threats. Problem: Static defenses inherently lack the capability to dynamically interact with, learn from, or mislead attackers. They often fail to capture subtle behavioral nuances or evolving adversary tactics, resulting in suboptimal situational awareness and defense postures that degrade rapidly in efficacy against polymorphic and coordinated attacks. Contribution: We propose AETHER-a fully autonomous, AI-driven, deception-centric cyber defense organism inspired by principles of evolutionary biology, game theory, and adversarial machine learning. AETHER redefines cyber defense as an active, adaptive predatory system designed to detect, deceive, dissect, and ultimately neutralize adversarial threats in real-time. Technical Framework: The system architecture incorporates several key innovations: • Generative Deception via Large Language Models (LLMs): Formally, given an attacker interaction state vector st ∈ R n , the generative model Gϕ synthesizes contextually rich digital illusions It = G ϕ (st, z), where z is a latent noise vector, producing dynamic decoy assets and interaction histories that scale in complexity proportional to engagement metrics e(t).