# Cybersecurity Risk Analysis in the IoT: A Systematic Review
## 5 Results and Discussion
### New Trends in IoT Cybersecurity  
The literature review revealed several emerging trends and future directions in IoT cybersecurity. While the available literature provided valuable insights, it is important to note that the number of studies explicitly focusing on specific trends was limited. Nonetheless, the identified trends are indicative of the potential advancements in securing IoT systems. We discuss the prominent trends below:  
* Integration of artificial intelligence (AI): artificial intelligence has emerged as a promising technique in addressing the challenges of IoT cybersecurity. Several studies [19; 38; 40] highlighted the role of AI, particularly machine learning algorithms, in detecting and mitigating cybersecurity threats in IoT environments. AI-based solutions offer the ability to analyze vast amounts of data from IoT devices, identify patterns, and proactively respond to potential attacks. Future research in this area should focus on refining AI algorithms, exploring ensemble learning approaches, and implementing real-time adaptive cybersecurity systems.
* Blockchain technology for enhanced security: blockchain technology has gained significant attention for its potential to enhance the security and privacy of IoT devices and data [12]. By providing decentralized and tamper-resistant data storage and communication, blockchain can reduce the risk of data manipulation and unauthorized access. Research efforts should concentrate on optimizing blockchain solutions for IoT, addressing scalability issues and ensuring interoperability with existing IoT architectures.
* Dynamic adaptive cybersecurity frameworks: as the IoT ecosystem evolves, static cybersecurity measures may become inadequate to defend against constantly evolving threats. Dynamic adaptive cybersecurity frameworks, as proposed by some studies [9], offer the ability to continuously assess and adjust security measures based on real-time threat intelligence. Future research should focus on developing intelligent and context-aware cybersecurity frameworks that can adapt to the changing IoT environment while minimizing the impact on system performance.
* Privacy-preserving techniques: with increasing concerns over data privacy in IoT, several studies [6; 11; 37] emphasized the need for privacy-preserving techniques. These techniques aim to protect sensitive user data while still enabling meaningful data analysis for IoT applications. Future research should explore novel cryptographic protocols, privacy-enhancing technologies, and privacy-aware data sharing mechanisms to strike a balance between data privacy and utility.
* Secure firmware and hardware design: the security of IoT devices heavily depends on the integrity of their firmware and hardware components [39]. Studies emphasized the importance of implementing secure development practices and utilizing hardware security modules to safeguard against physical attacks and firmware tampering. Future research should address the challenges of secure firmware updates, hardware-based attestation, and supply chain security.  
Figure 2: Percentages of reviewed attacks.  
Despite the identified trends, it is essential to acknowledge that the field of IoT cybersecurity is continually evolving, and new trends may emerge over time. Additionally, the limited number of studies addressing specific trends indicates the need for further research to explore and validate the effectiveness of proposed solutions in real-world IoT scenarios.  
Therefore, the identified trends in IoT cybersecurity hold significant potential in addressing the challenges posed by the expanding IoT landscape. Research efforts should continue to focus on these trends, exploring innovative approaches, and collaborating across disciplines to create a robust and resilient IoT security ecosystem.