Mobile Ad-hoc Networks (MANET) are decentralized wireless networks that communicate without pre-existing infrastructure. MANETs are vulnerable to the most popular types of attacks and threats, such as wormhole attacks. A wormhole attacks is very challenging issues that records the packets from one location of the network and tunnels them to another location to undermines the performance of the wireless network and disrupt the most routing protocol. However, the existing solutions have been developed to overcome the wormhole attack, but still suffering from additional hardware, incur high delay delivery, or fail to provide high throughput, packet delivery ratio as well as consume higher energy. In this paper a hybrid wormhole attack detection (HWAD) algorithm is proposed, which is able to detect both in-band wormholes through performs round trip time (RTT) based on its hop count, and packet delivery ratio (PDR), also out-of-band wormholes through performs transmission range between successive nodes in a more optimistic manner than existing solutions. HWAD reduce the delay and energy through avoids performing wormhole detections for all available nodes in the network. HWAD does not rely on any special hardware and middleware. The proposed algorithm HWAD was executed using NS-2 network simulator. The performance metrics was taking into consideration to evaluate the performance of the proposed algorithm the throughput, end to end delay, packet delivery ratio, and consuming energy. The proposed algorithm utilized Ad-hoc On-Demand Distance Vector (AODV) routing protocol to improve the detection method. The experimental results have shown the performance metrics of the proposed approach HWAD outperformed in wormhole detection compared with other algorithms.

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%.

Wireless sensor networks (WSNs) are integral components of smart environments. These allow monitoring and communication to take place autonomously across distributed sensor nodes. Nevertheless, they suffer from constrained resources that make them susceptible to routine-layer attacks. These specifically involve blackhole, flooding, selective forwarding attack traffic and normal traffic. The conventional machine learning and deep learning methods employed are effective in catering to these attacks, yet they have generalization issues when the network conditions are dynamic. The models are generally trained on the local features that make them more dependable and less interpretable. To overcome these issues, this paper proposes an attention-driven transformer encoder for tabular WSN traffic, designed for robust and interpretable intrusion detection in WSNs. The model represents the WSN features as sequential tokens and employs multi-head self-attention to capture global and local dependencies among sensor attributes and employs a multi-head self-attention for capturing the local and global dependencies among the sensor attributes. The framework integrated several components, including normalization, chi-square-based feature selection, and positional embedding. These are followed by multi-layer transformer encoding blocks for the feature fusion and subsequent classification. The framework has been evaluated on the publicly available WSN dataset. Results have been shown to attain an accuracy of 99.37%, which makes it outperform the traditional deep learning baseline models. The comparative analysis has shown the model to be superior in terms of generalization and reduced convergence time. It further offers enhanced interpretability that makes it a good fit to be deployed in real-world scenarios where resources can be constrained.