Intrusion Detection System in Internet of Things-Empowered Cybersecurity by Using Various Algorithms

Internet of Things (loT) is the new expertise for various significant application growth. To avoid adversarial attacks, network intrusion, and fraud, Intrusion Detection System (IDS) is one of a primary element of organization. An IDS in loT -empowered cybersecurity observes network traffic to alert and detect malicious activities or policy violations. It secures the devices of loT and networks by recognizing unauthorized access or anomalies. Recently, various researchers have identified significant ways for IDS by utilizing Artificial Intelligence (AI) techniques of Machine Learning (ML) and Deep Learning (DL) algorithms. This research represents several methodologies like Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), Decision Tree (DT), Bagging, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Hybrid Deep Belief Network (HDBN) that are employed for IDS. Accuracy, precision, sensitivity, specificity, and so on, are utilized as the parameters in this study.