Asguard: Adaptive Self-guarded Honeypot

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Résumé

Cybersecurity is of critical importance to any organisations on the Internet, with attackers exploiting any security loopholes to attack them. To combat cyber threats, a honeypot, a decoy system, has been an effective tool used since 1991 to deceive and lure attackers to reveal their attacks. However, these tools become increasingly easy to detect, which diminishes their usefulness. Recently, adaptive honeypots, which can change their behaviour in response to attackers, have emerged: despite their promise, however, they still have some shortcomings of their own. In this paper we survey conventional and adaptive honeypots and discuss their limitations. We introduce an approach for adaptive honeypots that uses Q-learning, a reinforcement learning algorithm, to effectively achieve two objectives at the same time: (1) learn to engage with attacker to collect their attack tools and (2) guard against being compromised by combining state environment and action to form a new reward function.
langue originaleAnglais
titreProceedings of the 17th International Conference on Web Information Systems and Technologies
EditeurSciTePress
Pages565-574
Nombre de pages10
Volume1
ISBN (Electronique)978-989-758-536-4, 2184-3252
Les DOIs
Etat de la publicationPublié - oct. 2021
Evénement17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS, - Online streaming
Durée: 26 oct. 202128 oct. 2021
Numéro de conférence: 17
https://webist.scitevents.org/?y=2021

Série de publications

NomProceedings of the 17th International Conference on Web Information Systems and Technologies

Une conférence

Une conférence17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,
Titre abrégéWEBIST 2021
période26/10/2128/10/21
Adresse Internet

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