By Spiros Kousouris, SUITE5
The Internet of Things (IoT) has revolutionised the way we interact with the world around us, from smart homes to self-driving cars; nevertheless, this technology brings new security challenges, as billions of devices are connected to the internet and each device has its own set of vulnerabilities. One of the biggest challenges in IoT security is misbehavior detection, where misbehavior refers to any abnormal or malicious activity perform on a device, such as sending unauthorized messages, tampering with data, or disrupting the network. Traditional security measures, such as firewalls and antivirus software are not always effective in detecting these types of attacks, especially when they are performed by insiders who have legitimate access to the network.
To address this challenge, researchers are turning to AI-based misbehavior detection, which uses machine learning (ML) algorithms to analyse the behavior of IoT devices and detect anomalies that may indicate misbehavior or security attacks; through training the algorithms on data from normal device behavior, the AI system can learn to recognize patterns and identify deviations from the norm, thus enabling the mitigation of the spread of an attack. One example of AI-based misbehavior detection is the use of anomaly detection algorithms, which are designed to identify patterns that deviate from the expected behavior of a device or network. These algorithms can be trained on a variety of data sources, such as network traffic, system logs, or sensor readings, and once trained, these algorithms can monitor the data in real-time and create alerts and flag situations to network operators, when an anomaly is detected. AI-based misbehavior detection has several advantages over traditional security measures. It is considered more effective at detecting insider threats, which are often the most difficult to detect; moreover, it can detect anomalies such as slight variations in device behavior over time that may be too subtle for humans to detect. Finally, it can adapt to new threats and learn from new data, making it more resilient to attacks. On the other hand, AI-based misbehavior detection comes also with limitations and challenges. It requires large amounts of data to effectively train the algorithms, while the training data must be representative of normal device behavior, which can be difficult to obtain in complex IoT environments. This approach can also generate false positives, leading to unnecessary alerts and increased workload to address them; while it can be vulnerable to adversarial attacks, in which attackers attempt to deceive the algorithms by feeding them misleading data.
In REWIRE, an AI-based misbehavior detection mechanism will be developed based on collaborative data sharing utilising blockchain, to collect and analyse threat intelligence information towards identifying anomalies from different parts of a distributed deployment and generate alerts so as to take corrective actions.
Overall, AI-assisted misbehavior detection is a promising approach to improving IoT security and as the IoT continues to grow and evolve, it is regarded as an increasingly important tool in securing these devices and the networks they operate on. By leveraging ML algorithms to analyse device behavior, AI-assisted misbehavior detection can detect subtle anomalies that traditional security measures may miss; however, it is important to understand the limitations of this approach and to use it in conjunction with other security measures to provide comprehensive protection against IoT threats.