Defensive technique
Deliberate environment changes that can hinder reconnaissance. Treated as a related area when no explicit deception is involved.
Research question
Definitions and technical considerations should be checked against the primary source. Atlas records are a selection tagged with this topic; a matching tag does not demonstrate operational outcomes.
Cross-reference
These counts describe tagged Atlas records, not the worldwide number of studies or offerings.
Linked library
The paper proposes a framework combining game theory with transformer-based components to quantify, model, and optimize cyber deception. The authors define deception attack-surface metrics and embed them in a signaling game, aiming to guide adaptive deception strategies.
The authors propose an antifragile framework that uses cyber deception with adaptive decoys and dynamic network movements to generate adversarial signals, infer threat actor profiles and support proactive decisions. A cyber threat intelligence ontology and a knowledge graph with semantic attributes represent tactical and strategic relationships between attackers and targets.
This paper, according to its authors, reviews AI-based solutions for building network- and device-level cyber deception methods in contested environments. It focuses on combining large language models (LLMs) and reinforcement learning (RL) to optimize deception strategies such as honeypots and honeynets, and examines their validation against stealthy attacks on operational technology (OT) systems reported in the literature.
The authors propose SecuPi, a compact Raspberry Pi-based system that combines real-time traffic monitoring, hybrid intrusion detection (signature-based and machine learning) and automated threat response. As deception and proactive defense mechanisms, they propose the use of honeypots together with automatic firewall updates and Moving Target Defense (MTD).
The authors propose a proactive cyber-defense system that combines cyber deception, adversarial environment simulation and deep reinforcement learning, with GAN-generated attack vectors emulating zero-day and polymorphic threats. The aim is to train context-aware defense policies that detect malicious behavior, re-route detection vectors and trigger deceptive traps, evaluated with benign and adversarial traffic on a synthetic smart-grid network.
This paper presents ShellGames, an SSH shell simulator based on large language models (LLMs) designed to sustain credible interactive deception sessions with adversaries. According to the authors, the system combines techniques such as chain-of-thought reasoning, memory management, speculative command execution, sandbox routing, and subversion detection to address common LLM limitations. The authors report improvements in accuracy, consistency, and state tracking over baselines, along with a user study indicating perceived realism comparable to a real shell.