The Internet of Things (IoT) gains diverse impacts due to its fast propagation in many fields such as wearable devices, smart sensors, and home appliances. The detonation of IoT devices in various fields causes the IoT system to be vulnerable to several cyber attacks. Intrusion detection systems are infused in IoT-based applications to identify and detect cyber threats. For the reliable IoT scenario, the intrusion detection system incorporates incremental learning to effectively recognize the attacks based on the existing knowledge about historical attack incidents. However, incremental learning-based intrusion detection is incapable of managing a high detection rate while lowering the false alarm rate.
   Thus, incremental learning utilizes deep learning models to perform effective intrusion detection for IoT-based applications. The deep incremental learning model employs deep neural networks to automatically extract the complex patterns with long-range dependencies concerning detecting cyber-attacks in the IoT environment. Intrusion detection in an IoT environment using deep incremental learning produce potent security solutions.