LLMPot: Automated LLM-based Industrial Protocol and Physical Process Emulation for ICS Honeypots

• URL: http://arxiv.org/abs/2405.05999v1
• Date: Thu, 9 May 2024 09:37:22 GMT
• Title: LLMPot: Automated LLM-based Industrial Protocol and Physical Process Emulation for ICS Honeypots
• Authors: Christoforos Vasilatos, Dunia J. Mahboobeh, Hithem Lamri, Manaar Alam, Michail Maniatakos,
• Abstract summary: Honeypots play a vital role by acting as decoy targets within ICS networks or on the Internet. Deploying ICS honeypots is challenging due to the necessity of accurately replicating industrial protocols and device characteristics. We propose LLMPot, a novel approach for designing honeypots in ICS networks harnessing the potency of Large Language Models.
• Score: 5.515499079485665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Industrial Control Systems (ICS) are extensively used in critical infrastructures ensuring efficient, reliable, and continuous operations. However, their increasing connectivity and addition of advanced features make them vulnerable to cyber threats, potentially leading to severe disruptions in essential services. In this context, honeypots play a vital role by acting as decoy targets within ICS networks, or on the Internet, helping to detect, log, analyze, and develop mitigations for ICS-specific cyber threats. Deploying ICS honeypots, however, is challenging due to the necessity of accurately replicating industrial protocols and device characteristics, a crucial requirement for effectively mimicking the unique operational behavior of different industrial systems. Moreover, this challenge is compounded by the significant manual effort required in also mimicking the control logic the PLC would execute, in order to capture attacker traffic aiming to disrupt critical infrastructure operations. In this paper, we propose LLMPot, a novel approach for designing honeypots in ICS networks harnessing the potency of Large Language Models (LLMs). LLMPot aims to automate and optimize the creation of realistic honeypots with vendor-agnostic configurations, and for any control logic, aiming to eliminate the manual effort and specialized knowledge traditionally required in this domain. We conducted extensive experiments focusing on a wide array of parameters, demonstrating that our LLM-based approach can effectively create honeypot devices implementing different industrial protocols and diverse control logic.

Related papers

• Reconfigurable and Scalable Honeynet for Cyber-Physical Systems [0.4545286225250997]
  Honeypots and Honeynets intended to detect and understand attacks have been employed for ICS. This paper focuses on making a scalable and reconfigurable honeynet for cyber-physical systems. It will also automatically generate attacks on the honeynet to test and validate it.
  arXiv  Detail & Related papers  (2024-04-05T20:06:47Z)
• Real-time Threat Detection Strategies for Resource-constrained Devices [1.4815508281465273]
  We present an end-to-end process designed to effectively address DNS-tunneling attacks in a router. We demonstrate that utilizing stateless features for training the ML model, along with features chosen to be independent of the network configuration, leads to highly accurate results. The deployment of this carefully crafted model, optimized for embedded devices across diverse environments, resulted in high DNS-tunneling attack detection with minimal latency.
  arXiv  Detail & Related papers  (2024-03-22T10:02:54Z)
• On Practicality of Using ARM TrustZone Trusted Execution Environment for Securing Programmable Logic Controllers [8.953939389578116]
  This paper investigates the application of ARM TrustZone TEE technology for enhancing the security of PLC. Our aim is to evaluate the feasibility and practicality of the TEE-based PLCs through the proof-of-concept design and implementation using open-source software such as OP-TEE and OpenPLC.
  arXiv  Detail & Related papers  (2024-03-08T16:55:20Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing [57.49021927832259]
  Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in many scenarios. However, their intricate designs and lack of transparency raise safety concerns when applied in real-world applications. Formal Verification (FV) of DNNs has emerged as a valuable solution to provide provable guarantees on the safety aspect.
  arXiv  Detail & Related papers  (2023-12-10T13:51:25Z)
• Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments [18.348489257164356]
  We propose a system for efficient skill acquisition that leverages an object-centric generative model (OCGM) for versatile goal identification. OCGM enables one-shot target object identification and re-identification in new scenes, allowing MP to guide the robot to the target object while avoiding obstacles.
  arXiv  Detail & Related papers  (2023-03-06T18:49:59Z)
• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection [64.67495502772866]
  Large Language Models (LLMs) are increasingly being integrated into various applications. We show how attackers can override original instructions and employed controls using Prompt Injection attacks. We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
  arXiv  Detail & Related papers  (2023-02-23T17:14:38Z)
• Unifying Synergies between Self-supervised Learning and Dynamic Computation [53.66628188936682]
  We present a novel perspective on the interplay between SSL and DC paradigms. We show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-01-22T17:12:58Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 code [0.0]
  Industrial Control Systems (ICS) have played a catalytic role in enabling the 4th Industrial Revolution. The convergence of traditional Operational Technology (OT) with Information Technology (IT) has opened a new and unique threat landscape. This has inspired defense research that focuses heavily on Machine Learning (ML) based anomaly detection methods that run on external IT hardware. We introduce the ICS machine learning inference framework (ICSML) which enables executing ML model inference on the PLC.
  arXiv  Detail & Related papers  (2022-02-21T09:37:28Z)
• Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT [51.68933585002123]
  We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
  arXiv  Detail & Related papers  (2021-10-28T08:14:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.