## 2 Related Work
The simulation outcomes demonstrate excellent performance, featuring a latency of 3 ms, an F1 score of 99.53%, an accuracy of 99.59%, and energy consumption of 55 W. The model demonstrates marked improvement over current methods in terms of accuracy, speed, and energy efficiency, establishing it as a robust solution for secure H-CIoT environments. Another study [9] introduced a hybrid intrusion detection system (IDS) that integrates Support Vector Machines (SVMs), Principal Component Analysis (PCA), Deep Belief Networks (DBNs) and adversarial learning to improve the detection of both known and zero-day cyber threats. Utilizing adversarial samples enhances the model's capacity to identify developing attack patterns, and PCA diminishes feature dimensions by 40%, thereby boosting efficiency. With the help of SVM, threats can be classified accurately, and the system shows outstanding performance on the NSL-KDD and CICIDS2017 datasets, attaining an accuracy of as much as 99.73% and exhibiting a low false-positive rate. It is particularly effective at identifying sophisticated attacks such as botnets, R2L, and U2R. The model, which has an inference time of only 9 ms, is appropriate for real-time use in environments with heavy traffic and provides strong, scalable, and adaptable cybersecurity protection. However, the hybrid teacher learning optimization technique imposes excessive computation requirements, making it unfit for real-time intrusion detection in systems with limited computing capabilities. The system fails to accommodate dynamic attack patterns because of its cluster-based feature selection strategy. Because limited-resource environments necessitate cybersecurity, the development of small IDSs must be prioritized. To meet this need, the IDS framework uses the OCSVM and PIO algorithms, as presented in [10]. The system detects sophisticated anomalies with minimal resources, making it suitable for IoT networks and edge computing. OCSVM is the system's principal anomaly detection utility because it detects dimensional deviations in multivariate data spaces well. Pigeon-inspired optimizer is used by the PIO technique to improve OCSVM parameter selection sensitivity. Multiple detection systems provide accurate findings without high system processing speeds. The proposed model passed f-measure, precision, false alarms, detection rate, and accuracy tests on the NSL-KDD and UNSW-NB15 datasets. Small systems can operate in real time, since this solution keeps the required execution time minimal. Intelligent IDSs can meet modern cybersecurity needs and operate efficiently with an effective adaptive strategy. However, a network intrusion detection system using lightweight technology achieves limited accuracy in detecting complex or new types of attacks because the OCSVM model operates at a basic level. The pigeon-inspired optimizer demonstrates difficulties when used to optimize security in extensive network environments. Another scheme was proposed in [11], incorporating a novel approach to intrusion detection that employs unsupervised learning to recognize various types of cyber attacks, such as DoS, DDoS, botnet, and infiltration attacks. The suggested system incorporates basic autoencoders (bAEs) for dimensionality reduction, along with a three-stage detection mechanism that includes a one-class SVM (OCSVM), deep autoencoders (dAEs), and DBSCAN clustering to map attack behaviors. It employs the MITRE ATT&CK framework for response prioritization and creates a cyber threat repository. It trains on normal, unlabeled network traffic, allowing it to effectively recognize new attacks and reduce problems arising from imbalanced datasets. When evaluated using CSECIC-IDS2018 and CIC-IDS2017 datasets, the model demonstrated an accuracy exceeding 98%, surpassing current methods in terms of scalability and precision. Modern industrial facilities need advanced intelligent cybersecurity solutions to safeguard operational areas from IoT expansion.