The Internet of Things (IoT) connects billions of intelligent devices that can communicate with each other without human intervention. With an estimated 50 billion devices by the end of 2020, it is one of the fastest growing areas of computing history. On the other hand, IoT technology is essential to the advancement of various real-world intelligent applications that can improve people's quality of life. However, the interdisciplinary components involved in networking and deploying IoT systems raise new security concerns. Encryption, authentication, access control, network security, and application security solutions are worthless when it comes to IoT devices and their inherent shortcomings. Therefore, existing security measures need to be updated to properly protect the IoT environment. Machine learning and deep learning (ML / DL) have evolved dramatically in recent years, and machine intelligence has evolved from laboratory curiosity to viable machines. An important defense against new or zero-day attacks is the ability to intelligently monitor IoT devices. ML / DL is a powerful data exploration technique for revealing "normal" and "bad" behavior in the context of IoT components and devices. Therefore, machine learning and deep learning technologies are important not only to enable secure device communication, but also to transform IoT security into a security-based intelligence system. The purpose of this study is to provide a comprehensive overview of recent advances in machine learning and deep learning techniques that can be used to improve IoT security solutions. It describes the many possible attack surfaces of IoT systems, the potential risks associated with each surface, and lists unique or newly established IoT security threats. It then details the ML / DL approach to IoT security, highlighting the potential, strengths, and weaknesses of each method. Learn about the possibilities and challenges of using machine learning and deep learning for IoT security. These opportunities and challenges may be used as future research paths.

The importance of cybersecurity in safeguarding network traffic is crucial in our increasingly interconnected world. Our research investigates the significant impact of cybersecurity on network performance and integrity, revealing that various security protocols influence the dynamic nature of network traffic in the face of cyber threats. Using data from Kaggle, we conducted an analysis of suspicious activity patterns over time, the contribution of different network protocols, and the involvement of specific IP addresses in attacks. Our findings highlight that cybersecurity incidents notably alter traffic patterns, with peaks often coinciding with increased threat levels. Certain network protocols, such as ICMP and TCP, were identified as key factors influencing traffic and vulnerabilities. Particularly, there was a high frequency of attacks targeting Windows devices, emphasizing the need for specialized security measures. In the current era, characterized by advancing technologies like IoT and cloud computing, striking a balance between security and performance is a significant concern due to the expanded attack surface area. These results provide valuable insights for developing adaptive and resilient network infrastructures capable of withstanding the evolving landscape of cyber threats.