A Systematic Review of Security Vulnerabilities in Smart Home Devices and Mitigation Techniques
- URL: http://arxiv.org/abs/2507.01018v1
- Date: Thu, 03 Apr 2025 00:03:53 GMT
- Title: A Systematic Review of Security Vulnerabilities in Smart Home Devices and Mitigation Techniques
- Authors: Mohammed K. Alzaylaee,
- Abstract summary: The study explores security threats in smart homes ecosystems, categorizing them into vulnerabilities at the network layer, device level, and those from cloud-based and AI-driven systems.<n>Research findings indicate that post-quantum encryption, coupled with AI-driven anomaly detection, is highly effective in enhancing security.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Smart homes that integrate Internet of Things (IoT) devices face increasing cybersecurity risks, posing significant challenges to these environments. The study explores security threats in smart homes ecosystems, categorizing them into vulnerabilities at the network layer, device level, and those from cloud-based and AI-driven systems. Research findings indicate that post-quantum encryption, coupled with AI-driven anomaly detection, is highly effective in enhancing security; however, computational resource demands present significant challenges. Blockchain authentication together with zero-trust structures builds security resilience, although they need changes to existing infrastructure. The specific security strategies show their effectiveness through ANOVA, Chi-square tests, and Monte Carlo simulations yet lack sufficient scalability according to the results. The research demonstrates the requirement for improvement in cryptographic techniques, alongside AI-enhanced threat detection and adaptive security models which must achieve a balance between performance and efficiency and real-time applicability within smart home ecosystems.
Related papers
- Enabling Security on the Edge: A CHERI Compartmentalized Network Stack [42.78181795494584]
CHERI provides strong security from the hardware level by enabling fine-grained compartmentalization and memory protection.<n>Our case study examines the trade-offs of isolating applications, TCP/IP libraries, and network drivers on a CheriBSD system deployed on the Arm Morello platform.
arXiv Detail & Related papers (2025-07-07T09:37:59Z) - Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation [42.350737545269105]
complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors.<n>We propose to meet those risks with a declarative approach to describe cyber power systems and automatically evaluate security and safety controls.
arXiv Detail & Related papers (2025-06-14T12:07:44Z) - Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection [38.083049237330826]
This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
arXiv Detail & Related papers (2025-06-11T18:43:51Z) - Offensive Security for AI Systems: Concepts, Practices, and Applications [0.0]
Traditional defensive measures often fall short against the unique and evolving threats facing AI-driven technologies.<n>This paper emphasizes proactive threat simulation and adversarial testing to uncover vulnerabilities throughout the AI lifecycle.
arXiv Detail & Related papers (2025-05-09T18:58:56Z) - 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) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI [0.0]
A key area in AI-based cybersecurity focuses on defending deep neural networks against malicious perturbations.
We attempt to validate results from prior work on certified robustness using the VeriGauge toolkit.
Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation.
arXiv Detail & Related papers (2024-05-29T04:37:19Z) - Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content.
In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks.
We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
arXiv Detail & Related papers (2024-02-21T06:22:41Z) - Blockchained Federated Learning for Threat Defense [0.0]
This research paper introduces the development of an intelligent Threat Defense system, employing Federated Learning.
The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms.
The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods.
arXiv Detail & Related papers (2021-02-25T09:16:48Z) - Robust Machine Learning Systems: Challenges, Current Trends,
Perspectives, and the Road Ahead [24.60052335548398]
Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT)
They are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy.
This paper summarizes the prominent vulnerabilities of modern ML systems, highlights successful defenses and mitigation techniques against these vulnerabilities.
arXiv Detail & Related papers (2021-01-04T20:06:56Z)
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.