AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices

• URL: http://arxiv.org/abs/2303.12367v1
• Date: Wed, 22 Mar 2023 08:06:41 GMT
• Title: AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices
• Authors: Volviane Saphir Mfogo, Alain Zemkoho, Laurent Njilla, Marcellin Nkenlifack, Charles Kamhoua
• Abstract summary: Honeypot is a popular deception technique that mimics interaction in real fashion. We propose a honeypot for IoT devices that uses machine learning techniques to learn and interact with attackers automatically.
• Score: 3.571367745766466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of the Internet of Things (IoT) has raised concerns about the security of connected devices. There is a need to develop suitable and cost-efficient methods to identify vulnerabilities in IoT devices in order to address them before attackers seize opportunities to compromise them. The deception technique is a prominent approach to improving the security posture of IoT systems. Honeypot is a popular deception technique that mimics interaction in real fashion and encourages unauthorised users (attackers) to launch attacks. Due to the large number and the heterogeneity of IoT devices, manually crafting the low and high-interaction honeypots is not affordable. This has forced researchers to seek innovative ways to build honeypots for IoT devices. In this paper, we propose a honeypot for IoT devices that uses machine learning techniques to learn and interact with attackers automatically. The evaluation of the proposed model indicates that our system can improve the session length with attackers and capture more attacks on the IoT network.

Related papers

• IoT-LM: Large Multisensory Language Models for the Internet of Things [70.74131118309967]
  IoT ecosystem provides rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, and audio. Machine learning presents a rich opportunity to automatically process IoT data at scale. We introduce IoT-LM, an open-source large multisensory language model tailored for the IoT ecosystem.
  arXiv  Detail & Related papers  (2024-07-13T08:20:37Z)
• 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)
• Classification of cyber attacks on IoT and ubiquitous computing devices [49.1574468325115]
  This paper provides a classification of IoT malware. Major targets and used exploits for attacks are identified and referred to the specific malware. The majority of current IoT attacks continue to be of comparably low effort and level of sophistication and could be mitigated by existing technical measures.
  arXiv  Detail & Related papers  (2023-12-01T16:10:43Z)
• Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges [55.82853124625841]
  Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
  arXiv  Detail & Related papers  (2023-09-14T05:43:36Z)
• An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices [0.7219077740523682]
  This work proposes a Host-based Intrusion Detection Systems that leverages Federated Learning and Multi-Layer Perceptron neural networks to detected cyberattacks on IoT devices with high accuracy and enhancing data privacy protection.
  arXiv  Detail & Related papers  (2023-06-23T11:26:00Z)
• HoneyIoT: Adaptive High-Interaction Honeypot for IoT Devices Through Reinforcement Learning [10.186372780116631]
  We develop an adaptive high-interaction honeypot for IoT devices, called HoneyIoT. We first build a real device based attack trace collection system to learn how attackers interact with IoT devices. We then model the attack behavior through markov decision process and leverage reinforcement learning techniques to learn the best responses to engage attackers.
  arXiv  Detail & Related papers  (2023-05-10T19:43:20Z)
• IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception [31.822346303953164]
  Honeypots are an important security tool to understand attacker intent and deceive attackers to spend time and resources. To build better honeypots and enhance cyber deception capabilities, IoT honeypots need to generate realistic network traffic flows. We propose a novel deep learning based approach for generating traffic flows that mimic real network traffic due to user and IoT device interactions.
  arXiv  Detail & Related papers  (2023-05-01T16:24:07Z)
• The Internet of Senses: Building on Semantic Communications and Edge Intelligence [67.75406096878321]
  The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors' We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
  arXiv  Detail & Related papers  (2022-12-21T03:37:38Z)
• What are Attackers after on IoT Devices? An approach based on a multi-phased multi-faceted IoT honeypot ecosystem and data clustering [11.672070081489565]
  Honeypots have been historically used as decoy devices to help researchers gain a better understanding of the dynamic of threats on a network. In this work, we presented a new approach to creating a multi-phased, multi-faceted honeypot ecosystem. We were able to collect increasingly sophisticated attack data in each phase.
  arXiv  Detail & Related papers  (2021-12-21T04:11:45Z)
• Reinforcement Learning for IoT Security: A Comprehensive Survey [4.0059435854780965]
  Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. In this paper, we present a comprehensive survey of different types of cyber-attacks against different IoT systems. We then present reinforcement learning and deep reinforcement learning based security solutions to combat those different types of attacks in different IoT systems.
  arXiv  Detail & Related papers  (2021-02-14T21:09:49Z)
• IoT Device Identification Using Deep Learning [43.0717346071013]
  The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers. The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization's network also increases the risk of attacks. In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network.
  arXiv  Detail & Related papers  (2020-02-25T12:24:49Z)

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.