# A Systematic Review of Security Vulnerabilities in Smart Home Devices and Mitigation Techniques

arXiv: https://arxiv.org/abs/2507.01018 · HTML: https://mlsanity.com/paper/2507.01018/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Cyber-Security in Smart Grid: Survey and Challenges](https://mlsanity.com/paper/1809.02609/) (2018)
- [Survey of Security and Privacy Issues of Internet of Things](https://mlsanity.com/paper/1501.02211/) (2015)
- [IoT Security: Botnet detection in IoT using Machine learning](https://mlsanity.com/paper/2104.02231/) (2021)
- [IoTSan: Fortifying the Safety of IoT Systems](https://mlsanity.com/paper/1810.09551/) (2018)
- [Smart Homes: Security Challenges and Privacy Concerns](https://mlsanity.com/paper/2010.15394/) (2020)
- [The digital harms of smart home devices: A systematic literature review](https://mlsanity.com/paper/2209.05458/) (2022)

## Abstract (arXiv)

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.