Tackling Okiru Attacks in IoT with AI-Driven Detection and Mitigation Strategies

Mirai botnet variations and their influence on IoT device security suggest that unreal Okiru attacks would likely have similar traits. Mirai is known for compromising IoT devices and merging them into a botnet via weak credentials and unpatched software. DDoS attacks may be launched from this network of hacked devices. A proactive cybersecurity approach that uses IoT anomaly detection and behavioral analysis to identify Okiru attacks early on is needed. In the dynamic world of IoT security, real-time network monitoring may quickly spot abnormal activity and illegal access, preventing Okiru threats. Regular software upgrades and strong authentication prevent unauthorized access. IoT IDSs and algorithms like the Simple Neural Network detect Okiru attacks in network traffic. Information exchange, user education, cybersecurity community participation, and IoT incident response help identify and respond to Okiru. A proactive, multi-layered strategy must protect IoT networks from growing threats. Using the Aposemat IoT-23 dataset gives you a lot of different ways to attack. This dataset has different kinds of attacks, so the experiment can be done in a real-world setting with lots of different types of attacks. The use of Logistic Regression, GaussianNB, and the Simple Neural Network shows a wide and resilient methodology. These algorithms are good at pattern identification and anomaly detection, which prevent Okiru attacks.