The widespread use of smart devices exposes them to hacking risks, with over 15 billion connected to the internet in 2023 and expected to double soon. Various cyberattacks threaten the security and privacy of users, including DDoS, backdoor access, MiTM interception, SQL injection, and XSS. To address these risks, Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) are being developed. These systems aim to detect and mitigate attacks by analysing network traffic patterns. Testing in real-world environments will validate their effectiveness. The widespread use of smart devices exposes them to hacking risks, with over 15 billion connected to the internet in 2023 and expected to double soon. Various cyberattacks threaten the security and privacy of users, including DDoS, backdoor access, MiTM interception, SQL injection, and XSS. To address these risks, Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) are being developed. These systems aim to detect and mitigate attacks by analyzing network traffic patterns. Testing in real-world environments will validate their effectiveness