<p>This study focuses on the development of new algorithms and models <br /> for detecting and mitigating cyberattacks targeting Internet of Things (IoT) <br /> systems. The widespread use of IoT devices poses a serious threat to information <br /> security, as most of these devices are resource-constrained and lack modern <br /> security mechanisms. The paper carefully looks at the main dangers and kinds <br /> of attacks in IoT systems (like DoS, spoofing, and sniffing), along with the <br /> methods used to detect them, which include detection models and machine <br /> learning algorithms. In particular, the advantages of artificial intelligence and <br /> deep learning-based algorithms over traditional statistical approaches are <br /> highlighted. We propose a hybrid approach for anomaly detection, network <br /> traffic analysis, and real-time threat identification. The research utilizes a 100 <br /> GB dataset collected over 12 months from IoT devices. The proposed hybrid <br /> model demonstrated a 27% improvement in accuracy compared to basic machine <br /> learning methods, achieving an accuracy rate of 94.7%. Additionally, our model <br /> reduced false-positive rates by up to 35% and increased real-time processing <br /> speed by a factor of 2.3. The results of this research represent a major advance <br /> in IoT security and introduce novel methods suitable for practical application in <br /> industrial environments.</p>