Is Your Kettle Smarter Than a Hacker? A Scalable Tool for Assessing Replay Attack Vulnerabilities on Consumer IoT Devices

• URL: http://arxiv.org/abs/2401.12184v2
• Date: Sat, 20 Apr 2024 13:04:40 GMT
• Title: Is Your Kettle Smarter Than a Hacker? A Scalable Tool for Assessing Replay Attack Vulnerabilities on Consumer IoT Devices
• Authors: Sara Lazzaro, Vincenzo De Angelis, Anna Maria Mandalari, Francesco Buccafurri,
• Abstract summary: ENISA and NIST security guidelines emphasize the importance of enabling default local communication for safety and reliability. We propose a tool, named REPLIOT, able to test whether a replay attack is successful or not, without prior knowledge of the target devices. We find that 75% of the remaining devices are vulnerable to replay attacks with REPLIOT having a detection accuracy of 0.98-1.
• Score: 1.5612101323427952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consumer Internet of Things (IoT) devices often leverage the local network to communicate with the corresponding companion app or other devices. This has benefits in terms of efficiency since it offloads the cloud. ENISA and NIST security guidelines underscore the importance of enabling default local communication for safety and reliability. Indeed, an IoT device should continue to function in case the cloud connection is not available. While the security of cloud-device connections is typically strengthened through the usage of standard protocols, local connectivity security is frequently overlooked. Neglecting the security of local communication opens doors to various threats, including replay attacks. In this paper, we investigate this class of attacks by designing a systematic methodology for automatically testing IoT devices vulnerability to replay attacks. Specifically, we propose a tool, named REPLIOT, able to test whether a replay attack is successful or not, without prior knowledge of the target devices. We perform thousands of automated experiments using popular commercial devices spanning various vendors and categories. Notably, our study reveals that among these devices, 51% of them do not support local connectivity, thus they are not compliant with the reliability and safety requirements of the ENISA/NIST guidelines. We find that 75% of the remaining devices are vulnerable to replay attacks with REPLIOT having a detection accuracy of 0.98-1. Finally, we investigate the possible causes of this vulnerability, discussing possible mitigation strategies.

Related papers

• Enhancing IoT Malware Detection through Adaptive Model Parallelism and Resource Optimization [0.6856683556201506]
  This study introduces a novel approach to malware detection tailored for IoT devices. Based on resource availability, ongoing workload, and communication costs, the malware detection task is dynamically allocated either on-device or offloaded to neighboring IoT nodes. Experimental results demonstrate that this proposed technique achieves a significant speedup of 9.8 x compared to on-device inference.
  arXiv  Detail & Related papers  (2024-04-12T20:51:25Z)
• 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)
• Tamper-Evident Pairing [55.2480439325792]
  Tamper-Evident Pairing (TEP) is an improvement of the Push-Button configuration (PBC) standard. TEP relies on the Tamper-Evident Announcement (TEA), which guarantees that an adversary can neither tamper a transmitted message without being detected, nor hide the fact that the message has been sent. This paper provides a comprehensive overview of the TEP protocol, including all information needed to understand how it works.
  arXiv  Detail & Related papers  (2023-11-24T18:54:00Z)
• SyzTrust: State-aware Fuzzing on Trusted OS Designed for IoT Devices [67.65883495888258]
  We present SyzTrust, the first state-aware fuzzing framework for vetting the security of resource-limited Trusted OSes. SyzTrust adopts a hardware-assisted framework to enable fuzzing Trusted OSes directly on IoT devices. We evaluate SyzTrust on Trusted OSes from three major vendors: Samsung, Tsinglink Cloud, and Ali Cloud.
  arXiv  Detail & Related papers  (2023-09-26T08:11:38Z)
• ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks [18.819756176569033]
  IoT devices control functions in smart homes and buildings, smart cities, and smart factories. Existing approaches for detecting attacks are mostly limited to attacks directly compromising individual IoT devices. We propose ARGUS, the first self-learning intrusion detection system for detecting contextual attacks on IoT environments.
  arXiv  Detail & Related papers  (2023-02-15T11:05:45Z)
• Lightweight IoT Malware Detection Solution Using CNN Classification [2.288885651912488]
  The security aspect of IoT devices is an infant field, which is why it is our focus in this paper. We developed a system that can recognize malicious behavior of a specific IoT node on the network. Through convolutional neural network and monitoring, we were able to provide malware detection for IoT using a central node that can be installed within the network.
  arXiv  Detail & Related papers  (2020-10-13T10:56:33Z)
• The Dark (and Bright) Side of IoT: Attacks and Countermeasures for Identifying Smart Home Devices and Services [4.568911586155096]
  We build up a model describing the traffic patterns characterizing three popular IoT smart home devices. We prove that it is possible to detect and identify with overwhelming probability their presence and the services running by the aforementioned devices.
  arXiv  Detail & Related papers  (2020-09-16T13:28:59Z)
• Smart Home, security concerns of IoT [91.3755431537592]
  The IoT (Internet of Things) has become widely popular in the domestic environments. People are renewing their homes into smart homes; however, the privacy concerns of owning many Internet connected devices with always-on environmental sensors remain insufficiently addressed. Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices.
  arXiv  Detail & Related papers  (2020-07-06T10:36:11Z)
• 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)
• Anomalous Communications Detection in IoT Networks Using Sparse Autoencoders [0.0]
  We present a method to detect anomalous network communications in IoT networks using a set of sparse autoencoders. The proposed approach allows us to differentiate malicious communications from legitimate ones. Depending on the value of N, the developed model achieves attack detection rates ranging from 86.9% to 91.2%, and false positive rates ranging from 0.1% to 0.5%.
  arXiv  Detail & Related papers  (2019-12-26T10:47:35Z)

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.