# A Survey on Cybersecurity in IoT
## 6 Challenges and Future Directions  
The field of IoT cybersecurity is confronted with an array of multifaceted challenges, which are exacerbated by the rapid and often haphazard deployment of IoT devices across diverse environments. These challenges are not only technical in nature but also span regulatory, economic, and operational domains, each with its own set of complexities that must be navigated to secure the IoT ecosystem effectively [251; 252; 253]. One of the most pressing challenges is the heterogeneity of IoT devices, which vary widely in terms of hardware capabilities, communication protocols, and security requirements. This diversity is particularly evident across application domains. Industrial IoT systems require real-time protection mechanisms to secure critical operational data and \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **References** & **Summary** \\ \hline AI and ML for Anomaly Detection & [202; 203; 204; 205; 206; 207; 208; 209; 210] & These references discuss the use of AI and ML techniques, especially unsupervised learning, to detect anomalies in IoT networks. \\ \hline Supervised Learning for Intrusion Detection & [211; 212; 213; 214; 215; 216] & Focuses on supervised learning models to enhance IDS in IoT environments. \\ \hline AI in Threat Intelligence and Predictive Analytics & [217; 218; 219; 220; 221] & References cover AI and ML applications in threat intelligence and predictive analytics for IoT security. \\ \hline Secure Device Management with AI & [222; 223; 224; 225; 226; 227; 228] & These studies explore AI-driven solutions for secure device management, including automated updates and continuous authentication. \\ \hline AI for Cryptographic Security & [229; 230; 231; 232; 233; 234] & Focus on AI applications in developing resilient cryptographic schemes and optimizing key distribution in IoT. \\ \hline Challenges with Adversarial ML & [235; 236; 237; 238; 239; 240] & Discusses the risks and countermeasures associated with adversarial machine learning in IoT security contexts. \\ \hline XAI for IoT Security & [241; 242; 243; 244; 245] & References that delve into the importance of explainability in AI models used for IoT security. \\ \hline Scalability and Energy Efficiency in AI for IoT & [246; 247; 248; 249; 250] & Covers research on scaling AI solutions and improving energy efficiency in IoT, including edge AI techniques. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of studies related to AI and ML in IoT security. prevent downtime in predictive maintenance processes [254]. Wearable devices, used extensively in fitness and healthcare, demand advanced privacy mechanisms to safeguard sensitive personal data while respecting user consent [255]. Autonomous vehicles, meanwhile, require ultra-low-latency communication systems to ensure safe navigation and resilience against cyberattacks on sensor data or control systems [256]. These examples highlight the need for domain-specific security strategies that consider the unique operational and regulatory contexts of each IoT application [257; 258; 259]. Another significant challenge is the issue of legacy devices, which were not designed with security in mind and are often difficult or impossible to update. These devices represent a substantial portion of the current IoT landscape and pose serious security risks. The presence of these legacy systems creates a situation where new, secure devices must coexist with older, vulnerable ones, leading to a weakest-link problem in the network. Addressing this challenge requires innovative approaches such as retrofitting security mechanisms onto existing devices or developing gateway solutions that can act as a security intermediary between legacy devices and the broader network [260; 261; 262; 263]. Interoperability among IoT devices and systems is another critical challenge, particularly in environments where devices from multiple vendors must work together seamlessly.