A Comparative Study Between Solutions Proposed to Secure IoT Networks
Mohamed Bachar, Azeddine Khiat, Ayoub Bahnasse · 2025
As the Internet of Things (IoT) continues to develop rapidly, securing IoT devices has become a critical concern. This paper explores various solutions proposed to enhance the security of IoT devices. The study focuses on classical solutions, as well as solutions utilizing machine learning(ML) and deep learning (DL) techniques. The advantages and disadvantages of each approach are analyzed, considering factors such as threat detection, real-time monitoring, adaptability to new threats, scalability, and complex pattern recognition. The paper discusses the importance of further research in developing tailored ML and DL algorithms for IoT security, addressing limitations such as resource constraints and adversarial attacks. In general, this study provides valuable information on the current state of the security of IoT devices and paves the way for future advance-ments in IoT security solutions. The article concludes by detecting anomaly traffic on IoT devices and assessing accuracy and time cost in data testing using machine learning and deep learning methods, providing valuable insights into the performance of these technologies in IoT applications.