A smart model integrating LSTM and XGBoost for improving IoT-enabled smart cities security

The start of smart cities has transformed urban living by using modern technology to improve effectiveness, sustainability, and overall quality of life. The Internet of Things (IoT) is an important aspect of this transition, since it links diverse devices and systems to enable smooth data sharing and intelligent decision-making. However, the rise of IoT devices in smart cities has posed substantial security and privacy risks, needing strong safeguards. Intrusion Detection Systems (IDS) serve an important role in protecting smart city infrastructures through analyzing network activity, detecting potential vulnerabilities, and neutralizing cyberattacks. This study examines the significance of IDS in the setting of smart cities, emphasizing their critical roles in identifying anomalies, avoiding breaches of information, and maintaining the security and reliability of city electronic ecosystems. IDS, using modern machine learning and deep learning algorithms, is capable of responding to the changing nature of cyber threats, offering an additional layer of protection that supplements standard security measures. The continual development and deployment of effective IDS is critical for the long-term security and growth of smart cities, ensuring that advances in technology do not jeopardize urban people' safety and privacy.