Large-Scale (Semi-)Automated Security Assessment of Consumer IoT Devices -- A Roadmap
- URL: http://arxiv.org/abs/2504.06712v2
- Date: Thu, 10 Apr 2025 06:44:01 GMT
- Title: Large-Scale (Semi-)Automated Security Assessment of Consumer IoT Devices -- A Roadmap
- Authors: Pascal Schöttle, Matthias Janetschek, Florian Merkle, Martin Nocker, Christoph Egger,
- Abstract summary: The Internet of Things (IoT) has rapidly expanded across various sectors, with consumer IoT devices experiencing growth.<n>Common and easy-to-explore vulnerabilities make IoT devices prime targets for malicious actors.<n>This paper reviews current IoT security challenges and assessment efforts, identifies gaps, and proposes a roadmap for scalable, automated security assessment.
- Score: 1.4680035572775536
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Internet of Things (IoT) has rapidly expanded across various sectors, with consumer IoT devices - such as smart thermostats and security cameras - experiencing growth. Although these devices improve efficiency and promise additional comfort, they also introduce new security challenges. Common and easy-to-explore vulnerabilities make IoT devices prime targets for malicious actors. Upcoming mandatory security certifications offer a promising way to mitigate these risks by enforcing best practices and providing transparency. Regulatory bodies are developing IoT security frameworks, but a universal standard for large-scale systematic security assessment is lacking. Existing manual testing approaches are expensive, limiting their efficacy in the diverse and rapidly evolving IoT domain. This paper reviews current IoT security challenges and assessment efforts, identifies gaps, and proposes a roadmap for scalable, automated security assessment, leveraging a model-based testing approach and machine learning techniques to strengthen consumer IoT security.
Related papers
- Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway [45.70482328441101]
This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic by analyzing network behavior at the edge.
We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic.
This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
arXiv Detail & Related papers (2025-04-22T09:40:05Z) - Towards Trustworthy GUI Agents: A Survey [64.6445117343499]
This survey examines the trustworthiness of GUI agents in five critical dimensions.<n>We identify major challenges such as vulnerability to adversarial attacks, cascading failure modes in sequential decision-making.<n>As GUI agents become more widespread, establishing robust safety standards and responsible development practices is essential.
arXiv Detail & Related papers (2025-03-30T13:26:00Z) - AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.<n> AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.<n>We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
arXiv Detail & Related papers (2025-02-24T02:11:52Z) - Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection [0.0]
The proliferation of the Internet and smart devices has led to a rise in cybercrimes.<n>This paper introduces a deep learning model incorporating LSTM and attention mechanisms, a pivotal strategy in combating cybercrime in IoT networks.
arXiv Detail & Related papers (2024-12-11T11:31:05Z) - Securing Cloud-Based Internet of Things: Challenges and Mitigations [18.36339203254509]
The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape.<n> IoT devices, inherently connected to the internet, are susceptible to various forms of attacks.<n> IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers.
arXiv Detail & Related papers (2024-02-01T05:55:43Z) - 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) - Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future [6.422895251217666]
This paper reviews forensic and security issues associated with IoT in different fields.
Most IoT devices are vulnerable to attacks due to a lack of standardized security measures.
To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system.
arXiv Detail & Related papers (2023-09-06T04:41:48Z) - Machine and Deep Learning for IoT Security and Privacy: Applications,
Challenges, and Future Directions [0.0]
The integration of the Internet of Things (IoT) connects a number of intelligent devices with a minimum of human interference.
Current security approaches can also be improved to protect the IoT environment effectively.
Deep learning (DL)/ machine learning (ML) methods are essential to turn IoT systems protection from simply enabling safe contact between IoT systems to intelligence systems in security.
arXiv Detail & Related papers (2022-10-24T19:02:27Z) - 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) - Machine Learning Based Solutions for Security of Internet of Things
(IoT): A Survey [8.108571247838206]
IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services.
There are existing security measures that can be applied to protect IoT.
Traditional techniques are not as efficient with the advancement booms as well as different attack types and their severeness.
A huge technological advancement has been noticed in Machine Learning (ML) which has opened many possible research windows to address ongoing and future challenges in IoT.
arXiv Detail & Related papers (2020-04-11T03:08:24Z) - 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.