In this modern age of technology, the Internet of things has covered all aspects of life including smart situations, smart homes, and smart spaces. Smart homes have a large number of IoT objects that are working continuously without any interruption. Better security and authentication of these smart devices can provide peaceful environments to live in such spaces. It is important to monitor the activities of smart IoT devices to make them work fault free. Such devices are small, consume relatively less power and resources, and are easily attackable by attackers. It is crucial to protect the integrity and characteristics of the smart home environment from external attacks. Machine Learning played a vital role in recognizing such malicious activities and attempts. Several Machine Learning approaches are available to detect the normal and abnormal behavior of IoT device traffic. This study proposed a machine learning-based anomaly detection approach for smart homes using different classifiers. Testing and evaluation are performed using the University of New South Wales (UNSW) BoT IoT dataset. Machine learning models based on four classifiers are built using an IoT devices dataset. For the Test dataset, the Weighted Precision, Recall and F1 score of Random forest, decision tree, and AdaBoost is 1 as compared to ANN which has 0.98, 0.96, and 0.96 respectively Results show that high performance, precision, and robustness can be achieved using the proposed methodology. In this way, smart homes’ security and identity of devices can be monitored and anomalies can be detected with high accuracy. Attack categories include binary class, multiclass class, and subclasses. Results show Random Forest algorithm outperforms enough to use this methodology in smart environments.

The Wireless Sensor Network in the Internet of Things (WSN-IoT) has been flourishing as another global breakthrough over the past few years. The WSN-IoT is reforming the way we live today by spreading through all areas of life, including the dangerous demographic aging crisis and the subsequent decline of jobs. For a company to increase revenues and cost-effectiveness growth should be customer-centered and agile within an organization. WSN-IoT networks have simultaneously faced threats, such as sniffing, spoofing, and intruders. However, WSN-IoT networks are often made up of multiple embedded devices (sensors and actuators) with limited resources that are joined via various connections in a low-power and lossy manner. However, to our knowledge, no research has yet been conducted into the security methods. Recently, a Contiki operating system’s partial implementation of Routing Protocol for Low Power & Lossy Network RPL’s security mechanisms was published, allowing us to evaluate RPL’s security methods. This paper presents a critical analysis of security issues in the WSN-IoT and applications of WSN-IoT, along with network management details using machine learning. The paper gives insights into the Internet of Things in Low Power Networks (IoT-LPN) architecture, research challenges of the Internet of Things in Low Power Networks, network attacks in WSN-IoT infrastructures, and the significant WSN-IoT objectives that need to be accompanied by current WSN-IoT frameworks. Several applied WSN-IoT security mechanisms and recent contributions have been considered, and their boundaries have been stated to be a significant research area in the future. Moreover, various low-powered IoT protocols have been further discussed and evaluated, along with their limitations. Finally, a comparative analysis is performed to assess the proposed work’s performance. The study shows that the proposed work covers a wide range of factors, whereas the rest of the research in the literature is limited.

The Internet of Things (IoT) is transforming everyday objects. However, the limited memory, processing power, and network capabilities of its devices make them susceptible to security breaches. The Routing Protocol for Low-Power and Lossy Networks (RPL) is a promising IoT protocol but faces significant security challenges. Existing research often focuses on individual attacks, utilizing various mitigation strategies, including machine learning and deep learning for detection. This paper proposes an Intrusion Detection System (IDS) using the ROUT-4-2023 dataset, which encompasses Black Hole, Flooding, DODAG Version Number, and Decreased Rank attacks. The study investigates network traffic features encompassing all four attacks, utilizing statistical information graphs. Additionally, it experiments with various machine learning models and deep learning architectures for comparative analysis, focusing on confusion matrix outcomes and computational efficiency. Results indicate that Random Forest classifier achieves 99% accuracy, while Transformers reach 97% F1-Score with training time of only 16.8 minutes over 5 epochs.

In recent years, research has combined the connection of agricultural equipment to increase crop growth rates and lower planting costs by refining the entire planting process. IT‐enabled agriculture has beneficial effects on this industry and is yet a source of debate in academic circles. Trending network technologies like WSN and IoT have never been easy to develop and use in agriculture. The growth rate was not increased using outdated, conventional methods and technologies. Additionally, the rapid population expansion cannot meet human demands and expectations. Survey Methodology. The existence of IoT in agriculture was investigated and reported in this review. The paper describes the different IoT‐agriculture network protocols. This study clarifies how the Internet affects agriculture and its underlying mechanisms. It also discusses how the growth rate is boosted when both sectors work together. This study intends to explore a platform that offers an infrastructure to link devices using the network protocol used in agriculture. In this study, several contemporary network difficulties relating to agriculture are also covered. Conclusion. The results of this study can be used as a guide for creating particular network protocols for the agriculture industry.

Wireless Sensor Networks (WSNs) and Internet of Things (IoT) infrastructures remain critically vulnerable to wormhole attacks, which establish covert tunnels between colluding adversaries and bypass conventional cryptographic defenses. Such attacks severely degrade network availability, privacy, and routing integrity, making them one of the stealthiest and most disruptive threats in modern distributed systems. This research introduces a novel, deployment-ready intrusion detection system (IDS) that integrates audit-driven preprocessing, dataset shift analysis, and hybrid deep–machine learning architectures with conformal calibration. The proposed pipeline employs Principal Component Analysis (PCA) for dimensionality reduction, Population Stability Index (PSI) and Maximum Mean Discrepancy (MMD) for dataset shift auditing, and Correlation Alignment (CORAL) for unsupervised domain adaptation. Six classical machine learning baselines—Decision Tree (DT), Random Forest (RF), K-Nearest Neighbour (KNN), Naïve Bayes (NB), Extreme Gradient Boosting (XGBoost), and CatBoost—were benchmarked against deep learning architectures, including standalone CNNs and hybrid fusion models (CNN+DT and CNN+RF). Validation employed stratified k-fold cross-validation, bootstrap confidence intervals, and conformal prediction, reporting not only accuracy but also risk-calibrated uncertainty estimates. Experimental results on three benchmark datasets (Wormhole, UNSW-NB15, CICIDS2017) reveal that CNN+Random Forest consistently outperforms all baselines, achieving 100% training accuracy, 99.56% validation accuracy, and 99.40% testing accuracy on the Wormhole dataset, 0.922 accuracy with balanced F1=0.94 on UNSW-NB15, and near-perfect F1=0.9996 with AUC ≈ 1.0 on CICIDS2017. Transfer learning experiments demonstrated resilience under domain adaptation, with mixed training achieving 94.2% F1 on UNSW and 99.9% on CICIDS, while deep domain adaptation methods (DANN, MMD, DeepCORAL) provided additional insights into cross-dataset generalization. By embedding feature leakage audits, dataset drift quantification, and conformal calibration, this work advances IDS research toward transparent, explainable, and reproducible models suitable for real-world IoT/WSN deployment.

In the modern digital market flooded by nearly endless cyber-security hazards, sophisticated IDS (intrusion detection systems) can become invaluable in defending against intricate security threats. Sybil-Free Metric-based routing protocol for low power and lossy network (RPL) Trustworthiness Scheme (SF-MRTS) captures the nature of the biggest threat to the routing protocol for low-power and lossy networks under the RPL module, known as the Sybil attack. Sybil attacks build a significant security challenge for RPL networks where an attacker can distort at least two hop paths and disrupt network processes. Using such a new way of calculating node reliability, we introduce a cutting-edge approach, evaluating parameters beyond routing metrics like energy conservation and actuality. SF-MRTS works precisely towards achieving a trusted network by introducing such trust metrics on secure paths. Therefore, this may be considered more likely to withstand the attacks because of these security improvements. The simulation function of SF-MRTS clearly shows its concordance with the security risk management features, which are also necessary for the network’s performance and stability maintenance. These mechanisms are based on the principles of game theory, and they allocate attractions to the nodes that cooperate while imposing penalties on the nodes that do not. This will be the way to avoid damage to the network, and it will lead to collaboration between the nodes. SF-MRTS is a security technology for emerging industrial Internet of Things (IoT) network attacks. It effectively guaranteed reliability and improved the networks’ resilience in different scenarios.