Phishing could be considered a worldwide problem; undoubtedly, the number of illegal websites has increased quickly. Besides that, phishing is a security attack that has several purposes, such as personal information, credit card numbers, and other information. Phishing websites look like legitimate ones, which makes it difficult to differentiate between them. There are several techniques and methods for phishing detection. The authors present two machine-learning algorithms for phishing detection. Besides that, the algorithms employed are XGBoost and random forest. Also, this study uses particle swarm optimization (PSO) and grey wolf optimizer (GWO), which are considered metaheuristic algorithms. This research used the Mendeley dataset. Precision, recall, and accuracy are used as the evaluation criteria. Experiments are done with all features (111) and with features selected by PSO and GWO. Finally, experiments are done with the most common features selected by both PSO and GWO (PSO ∩ GWO). The result demonstrates that system performance is highly acceptable, with an F-measure of 91.4%.

The security of the Internet of Things (IoT) has become a major research concern, particularly in the perception layer where wireless sensor networks (WSNs) operate and generate large amounts of data. Without encryption, transmitted data in WSN-IoT networks is vulnerable to security attacks. Conventional methods that decrypt, aggregate, and re-encrypt data consume excessive energy at the aggregator and increase end-to-end delay. To address these issues, this study proposes a secure data aggregation scheme based on end-to-end elliptic curve and homomorphic encryption (EEECHE). The scheme ensures data confidentiality from nodes to the server while minimizing energy consumption and latency. It also applies a message authentication code (MAC) to verify data authenticity and detect false data efficiently. Experimental results show that the proposed scheme achieves stronger security with lower energy usage and end-to-end delay compared to cluster-based semi-Homomorphic encryption aggregated data (CSHEAD) and cluster-based secure data aggregation (CSDA).