Reconfigurable and Scalable Honeynet for Cyber-Physical Systems
- URL: http://arxiv.org/abs/2404.04385v1
- Date: Fri, 5 Apr 2024 20:06:47 GMT
- Title: Reconfigurable and Scalable Honeynet for Cyber-Physical Systems
- Authors: Luís Sousa, José Cecílio, Pedro Ferreira, Alan Oliveira,
- Abstract summary: 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.
- Score: 0.4545286225250997
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Industrial Control Systems (ICS) constitute the backbone of contemporary industrial operations, ranging from modest heating, ventilation, and air conditioning systems to expansive national power grids. Given their pivotal role in critical infrastructure, there has been a concerted effort to enhance security measures and deepen our comprehension of potential cyber threats within this domain. To address these challenges, numerous implementations of Honeypots and Honeynets intended to detect and understand attacks have been employed for ICS. This approach diverges from conventional methods by focusing 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. With the development of a scalable and reconfigurable Honeynet and automatic attack generation tools, it is also expected that the system will serve as a basis for producing datasets for training algorithms for detecting and classifying attacks in cyber-physical honeynets.
Related papers
- Countering Autonomous Cyber Threats [40.00865970939829]
Foundation Models present dual-use concerns broadly and within the cyber domain specifically.
Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations.
This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
arXiv Detail & Related papers (2024-10-23T22:46:44Z) - Smart Grid Security: A Verified Deep Reinforcement Learning Framework to Counter Cyber-Physical Attacks [2.159496955301211]
Smart grids are vulnerable to strategically crafted cyber-physical attacks.
Malicious attacks can manipulate power demands using high-wattage Internet of Things (IoT) botnet devices.
Grid operators overlook potential scenarios of cyber-physical attacks during their design phase.
We propose a safe Deep Reinforcement Learning (DRL)-based framework for mitigating attacks on smart grids.
arXiv Detail & Related papers (2024-09-24T05:26:20Z) - CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation [37.89720165358964]
This paper showcases CARACAS, a vehicular model, including component control via CAN messages and attack injection capabilities.
CarACAS showcases the efficacy of this methodology, including a Battery Electric Vehicle (BEV) model, and focuses on attacks targeting torque control in two distinct scenarios.
arXiv Detail & Related papers (2024-06-11T10:16:55Z) - GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints.
Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
arXiv Detail & Related papers (2024-05-20T14:43:46Z) - LLMPot: Automated LLM-based Industrial Protocol and Physical Process Emulation for ICS Honeypots [5.515499079485665]
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.
arXiv Detail & Related papers (2024-05-09T09:37:22Z) - FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks.
We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness.
Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
arXiv Detail & Related papers (2024-03-26T08:51:23Z) - Investigation of Multi-stage Attack and Defense Simulation for Data Synthesis [2.479074862022315]
This study proposes a model for generating synthetic data of multi-stage cyber attacks in the power grid.
It uses attack trees to model the attacker's sequence of steps and a game-theoretic approach to incorporate the defender's actions.
arXiv Detail & Related papers (2023-12-21T09:54:18Z) - A Variational Autoencoder Framework for Robust, Physics-Informed
Cyberattack Recognition in Industrial Cyber-Physical Systems [2.051548207330147]
We develop a data-driven framework that can be used to detect, diagnose, and localize a type of cyberattack called covert attacks on industrial control systems.
The framework has a hybrid design that combines a variational autoencoder (VAE), a recurrent neural network (RNN), and a Deep Neural Network (DNN)
arXiv Detail & Related papers (2023-10-10T19:07:53Z) - Adaptive Attack Detection in Text Classification: Leveraging Space Exploration Features for Text Sentiment Classification [44.99833362998488]
Adversarial example detection plays a vital role in adaptive cyber defense, especially in the face of rapidly evolving attacks.
We propose a novel approach that leverages the power of BERT (Bidirectional Representations from Transformers) and introduces the concept of Space Exploration Features.
arXiv Detail & Related papers (2023-08-29T23:02:26Z) - A Novel Online Incremental Learning Intrusion Prevention System [2.5234156040689237]
This paper proposes a novel Network Intrusion Prevention System that utilise a SelfOrganizing Incremental Neural Network along with a Support Vector Machine.
Due to its structure, the proposed system provides a security solution that does not rely on signatures or rules and is capable to mitigate known and unknown attacks in real-time with high accuracy.
arXiv Detail & Related papers (2021-09-20T13:30:11Z) - A Framework for Evaluating the Cybersecurity Risk of Real World, Machine
Learning Production Systems [41.470634460215564]
We develop an extension to the MulVAL attack graph generation and analysis framework to incorporate cyberattacks on ML production systems.
Using the proposed extension, security practitioners can apply attack graph analysis methods in environments that include ML components.
arXiv Detail & Related papers (2021-07-05T05:58:11Z)
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.