Hate speech is rapidly spreading across digital platforms, appearing in diverse forms driven by regional, cultural, and linguistic differences. This growing trend presents serious challenges to social harmony and online safety. Existing hate speech detection models often fall short because they rely on limited and homogeneous datasets, making them less effective in real-world, culturally diverse settings. Handling large-scale, diverse datasets adds notable complexity to capturing contextual nuances, as different populations and cultures demonstrate unique language patterns and expressions. This study addresses the necessity for a more universal solution by proposing a deep learning model trained on an extensive and diverse dataset comprising 842,000 samples collected from various digital platforms. The approach combines a Bidirectional Long Short-Term Memory (BiLSTM) model with a self-attention mechanism to capture contextual depth. Various data embedding techniques were used to assess their impact, along with data resampling and standard Natural Language Processing (NLP) pre-processing steps. The proposed model achieved 93% accuracy with an F1-score of 0.92, outperforming several baseline and state-of-the-art models. This work provides a comprehensive and scalable framework for the detection of hate speech across various online platforms.

As the world suffers from intrusions and malware extensively nowadays, intru-sion detection systems (IDS) play a critical role in protecting cyberspace from attacks. However, attacks become more complex every day, leading to the neces-sity of developing new techniques that can protect our digital infrastructure from cyber-attacks. Deep learning (DL) is one of the techniques that are investigated to fight against cyber-attacks. However, due to the nature of traffic data, most of the techniques focus on the deep neural network (DNN) as the performance of the DNN dependsonthetraining data. In this paper, we investigate the effective-ness of using convolutional neural networks (CNN) to detect malware apps and network intrusions. The cybersecurity datasets are converted from tabular data into images using the DeepInsight technique. Experiments are conducted using two datasets, NSL-KDD and CICMaldroid20 datasets. The proposed method demonstrates that converting cybersecurity datasets from tabular data into im-ages may decrease the model’s accuracy. Furthermore, this approach introduces additional challenges in the detection of network intrusions and malware. More-over, the added architectural complexity may cause a dilution or distortion of feature representations, making it harder for the model to preserve the original semantic meaning of critical features.

Smart water infrastructure increasingly relies on distributed Industrial Internet of Things (IIoT) and SCADA architectures for real-time monitoring, supervisory control, and operational decision-making. However, this integration also creates cyber-physical vulnerabilities in which attackers can maintain plausible communication and supervisory feedback while the underlying process gradually deviates from its true state. This paper presents a distributed smart water IoT-SCADA simulation testbed for investigating Cross-Zone Process–Perception Dissociation Attacks (CZPPDAs), where apparently normal SCADA/HMI information conceals harmful physical-process drift. The testbed spans external threat, perimeter, DMZ, enterprise IT, supervisory control, OT control, field process, and monitoring/data-acquisition layers, supporting realistic attacker progression, telemetry interception, semantic manipulation, supervisory deception, and concealed process deviation. A hybrid cyber-physical dataset is generated from communication descriptors, protocol variables, supervisory states, controller attributes, process measurements, and cross-zone consistency indicators. For detection, CZSD-Net models SCADA, HMI, PLC, RTU, and gateway components as an industrial dependency graph and learns evolving process–perception inconsistency through relation-aware graph encoding, dissociation reasoning, and temporal fusion. Experimental evaluation shows progressive improvement over baseline graph-learning configurations. The final model achieves 97.94% testing accuracy, 97.62% precision, 97.29% recall, and 97.43% F1-score. These results show that the proposed testbed and graph-temporal framework provide an effective approach for studying and detecting stealthy cyber-physical deception in smart water IoT-SCADA environments. It also supports reproducible evaluation of detection behavior across synchronized supervisory, control, network, and physical-process observations.

IJEETC is a scholarly peer-reviewed international scientific journal, focusing on theories, systems, methods, algorithms, and applications in electrical and electronic engineering & telecommunications.

Network intrusion detection systems (NIDS) increasingly rely on deep learning to cope with evolving cyber attacks, yet they typically require large amounts of labeled data per attack type and exhibit poor generalization to novel threats. Recent advances in meta-learning and few-shot learning have shown promise for alleviating data scarcity, while depthwise separable convolutions (DSC) offer a computationally efficient means of extracting fine-grained spatial–temporal features from network traffic. In this paper, we propose a meta-learning framework for few-shot network intrusion detection that combines a model-agnostic meta-learning (MAML) objective with a one-dimensional DSC backbone. Tasks are defined episodically across attack families and datasets, enabling the meta-learner to acquire an initialization that rapidly adapts to unseen attack types using only a handful of labeled flows. We derive the corresponding bilevel optimization problem, analyze the computational benefits of DSC compared with standard convolutions, and present an end-to-end architecture tailored for flow-based NIDS. Experiments on CICIDS2017, NSL-KDD and UNSW-NB15 under challenging 5-shot and 10-shot settings demonstrate consistent improvements in accuracy and macro-\(F_1\) over strong convolutional, metric-based and attention-enhanced baselines inspired by recent literature. The proposed approach reduces parameters by up to 65% relative to conventional convolutions while maintaining or improving detection performance. We conclude with a discussion of limitations and directions for integrating cross-domain adaptation and self-attention into meta-learned DSC architectures.

Wireless Sensor Networks (WSNs) have become a cornerstone of modern technology, especially in applications like the Internet of Things (IoT), where they provide scalable and effective solutions for communication and monitoring applications. However, the finite energy of sensor nodes introduces a significant challenge, as it directly influences the network's lifespan and reliability. Routing protocols and clustering solutions have emerged as ideal ways to address these energy concerns. By forming sensors into clusters and enhancing data transmission routes, these protocols reduce energy consumption and improve network efficiency. Modern approaches incorporate techniques such as metaheuristics, fuzzy logic, and machine learning to address issues like load balancing, node mobility, and network topology changes. Despite considerable progress, gaps remain in scalability, Quality of Service (QoS) integration, and the adaptability of clustering protocols for dynamic environments. This research reviews the state of the art in optimizing energy efficiency in Wireless Sensor Networks. It addresses routing approaches and clustering solutions and highlights performance and quality key metrics and future trends.