# A Survey on Cybersecurity in IoT
## 5 Artificial Intelligence and Machine Learning in IoT Security
AI and ML also play a crucial role in threat intelligence and predictive analytics within IoT security [217]. The vast amount of data generated by IoT devices can be leveraged to identify emerging threats and predict potential security incidents. Natural language processing (NLP) techniques are used to analyze unstructured data from threat reports, social media, and dark web forums, extracting actionable insights that can inform proactive defense strategies. Additionally, time-series forecasting models can be applied to predict the occurrence of security events based on historical data, enabling the development of predictive maintenance schedules and proactive security measures [218; 219; 220; 221]. Another significant application is in the domain of secure device management. AI-driven solutions are being developed to automate the process of device enrollment, configuration, and firmware updates in IoT ecosystems. These systems can identify vulnerabilities in device firmware and initiate automated updates, reducing the window of exposure to potential exploits [222; 223; 224; 225]. Furthermore, AI can enhance the security of device authentication processes by incorporating behavioral biometrics and continuous authentication mechanisms. These approaches analyze patterns in device usage and interactions, allowing for the dynamic assessment of trust and the detection of compromised devices [226; 227; 228]. In the context of cryptographic security, AI and ML are being employed to develop more resilient encryption schemes tailored to the resource constraints of IoT devices. For instance, AI-driven optimization techniques can be used to design lightweight cryptographic algorithms that balance security with computational efficiency [229; 230; 231]. Additionally, AI is being explored as a means to secure cryptographic keys through techniques such as quantum key distribution (QKD), where ML models are used to optimize key generation and distribution processes, enhancing the security of communication channels in IoT networks [232; 233; 234]. However, the integration of AI into IoT security is not without challenges. Adversarial ML is an emerging threat where attackers craft inputs specifically designed to deceive AI models. In the context of IoT, adversarial attacks can be particularly damaging, as they can lead to the misclassification of malicious activities as benign or vice versa. Research into robust AI models that can withstand such adversarial manipulations is critical. Techniques such as adversarial training, where models are trained on both legitimate and adversarial examples, are being explored to enhance the resilience of AI systems in IoT environments [235; 236; 237; 238; 239; 240]. Additionally, the use of AI in IoT security raises concerns about the interpretability and transparency of ML models. In critical applications such as healthcare or industrial automation, it is essential not only to detect security breaches but also to understand the reasoning behind the AI's decisions. Explainable AI (XAI) is a burgeoning field that seeks to make ML models more transparent, allowing security analysts to comprehend the basis for a model's predictions and to trust its outputs in high-stakes scenarios [241; 242; 243; 244; 245]. The deployment of AI in IoT security also intersects with issues of scalability and energy efficiency. IoT networks can comprise millions of devices, each generating data that need to be processed in real time. Scaling AI solutions to handle this data deluge without overwhelming the computational resources of IoT devices is a significant challenge. Edge AI, where ML models are deployed directly on IoT devices or nearby edge servers, is emerging as a solution to this challenge. By processing data closer to the source, edge AI reduces latency and bandwidth usage while maintaining robust security measures.