System, Apparatus, and Method to Generate Decoy Honeypots by Using Generated …

Description (excerpt)

STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH AND DEVELOPMENT The United States Government has ownership rights in this invention. Licensing inquiries may be directed to Office of Research and Technical Applications Naval Information Warfare Center Pacific, Code 72120, San Diego, CA, 92152; telephone (619) 553-5118; email: niwc_patent.fct@us.navy.mil, referencing Navy Case 211,727. FIELD OF USE The present disclosure pertains generally to cyber-security defense techniques including generating decoy honeypots with generative adversarial networks that include, but are not limited to, imitating network device configurations. BACKGROUND The field of cybersecurity constantly faces the challenge of defending networks and systems against malicious attacks. One effective approach to deceive adversaries and gather intelligence about their tactics is through the use of decoy systems, which are commonly known as decoys or decoy honeypots. By strategically deploying decoy honeypots at different stages of the cyber kill chain, organizations can gain valuable insights into the attacker's methods, motives, and vulnerabilities. Honeypots can provide early warning signs, capture attack tools or malware samples, and gather valuable threat intelligence that can enhance overall security. Generative Adversarial Networks Generative adversarial networks (GANs) have gained significant attention due to their ability to generate synthetic data simulating realistic media such as images, text, audio and videos. Unlike traditional generative models which are typically trained by maximizing a log likelihood, GANs possess a unique architectural setup comprising two neural networks: the generator and the discriminator. The generator network takes random noise as input and generates synthetic samples, such as images, text, or even audio. The discriminator network, on the other hand, receives both real and generated samples and tries to distinguish be-tween them. The two networks are trained together in a competitive setting, constantly improving and challenging each other's performance. More specifically, the discriminator, denoted here as D, and generator, denoted here as G, play the following two-player min-max game with value function in Eq. 1-V (G, D): min G max D ⁢ V ⁢ ( D , G ) = E x ∼ p r ( x ) [ log ⁢ D ⁢ ( x ) ] + E z ∼ p g ( z ) <