The vast usage and development of web technology generate numerous types of web pages. Besides, not all these types are legitimate webpages. Phishing sites mislead web page users into taking harmful actions. However, there is a need for a tool to address this type of problem. Deep learning models are used in dealing with web technology to detect whether the webpage is either legitimate or phishing. Herein, a novel Tabular Multi-Head Attention (TabMHA) model is presented to perform a binary classification task. The main task is to classify whether the webpages are phishing or not. The proposed model is trained and tested on a benchmark dataset related to phishing detection. It contains 5000 legitimate web pages and 5000 phishing ones; the overall is 10,000. Also, the feature numbers in the dataset are out of 48 features. As a consequence, the proposed model achieved a powerful performance compared with other models in the literature; the model achieved an accuracy level of 99.6%. This result is considered a promising result and can be integrated into real-world detection models.

The intelligent autonomous systems need to be reliable in the situations when there is uncertainty as well as nonlinear dynamics and time-varying disturbances. Traditional model-driven controllers are not flexible and purely learning-based models can be unstable and not easily interpretable. The current hybrid techniques strive to unite these paradigms, but they are generally based on offline optimization or loosely coupled structures of learning and control. This paper offers a hybrid soft computing and adaptive learning model based on combining fuzzy inference with an online learning process to make decisions in real-time. The fuzzy aspect provides the ability to deal with uncertainty and nonlinear mappings whereas the adaptive learning aspect optimizes control parameters through performance feedback with limited updates. Experimental analysis shows the presented framework can reach control accuracy of 95.2 which is 3-5 points better than the representative hybrid and learning-based baselines, with adaptation time lowered to 2.6 s. Stability analysis indicates a much lower level of control signal variance than with the unconstrained learning strategies. The primary value of the research is the single hybrid architecture that maintains the interpretability and allows further adaptation, which is a feasible and reliable solution to intelligent autonomous control in continuously evolving environments.

Internet of Things (IoT) and Internet of Medical Things (IoMT) networks provide a massive amount of data. These types of data need a protection level, such as an intrusion detection framework. Deep learning models become a powerful tool for this purpose. Therefore, this work proposes an intrusion detection framework based on a deep learning technique which employs TabTransformer and self-attention mechanisms to imprison intricate dependencies among tabular features and detect abnormal attack behaviors. Precisely, each numerical feature is mapped into a learnable embedding vector and augmented with positional encodings to preserve feature identity and inter-feature relationships within the embedding space. The main task for the proposed model is to achieve binary classification tasks the model should classify the traffic data as either normal or abnormal. Furthermore, the model utilized a benchmark dataset such as the CICIoMT2024. Furthermore, this type of dataset faces issues, such as imbalance. So, the system integrates SMOTE-based data balancing, Stratified K-Fold Cross-Validation, and threshold optimization to ensure fairness and reproducibility to accomplish a binary classification task. As a consequence, experiments on the CICIoMT2024 dataset yield superior results, achieving a mean accuracy of 99.85. Through SHAP-based interpretability, key features influencing model predictions are identified, confirming the framework’s transparency, robustness, and suitability for real-world ARP intrusion detection.

Cyber-attacks become manifested as a series of behavioral patterns, but not as an event, and many current security regimes stay based upon a static detection and fixed trust implementation. Such incongruence restricts their capability to act in a dependable manner in fluctuating and unpredictable threat situations. The existing artificial intelligence-based cybersecurity products mainly focus on the detection performance. Due to this, such systems will still be vulnerable to false positives, erratic reactions, and degradation of performance over time. This paper proposes a behavior-sensitive cybersecurity model that brings together sequential behavioral modelling, risk-adaptive cryptography implementation, and integrity-guaranteed learning in an architecture with closed loops. The temporally structured patterns of activity are considered as behavioral risk, which allows making proportional, not binary, trust decisions. Cryptographic policies are adaptively changed based on the inferenced risk, whereas learning updates are explicitly secured to maintain the model reliability as time goes by. The experimental findings indicate that the proposed framework can obtain a detection accuracy of 96.7% and F 1-score of 96.0, as well as a false positive rate decreased to 3.1%. Moreover, the adaptive response latency is also decreased by a factor of about 20-30% relative to the representative baselines and also enhanced stability in response to adversarial noise. These results indicate behavior-based intelligence.

The recent explosion of cyber threats in big data ecosystems has exposed the vulnerability of black-box machine learning models that prioritize accuracy over explainability. Traditional cybersecurity mechanisms do not create a sense as to why a prediction is provided and the analysts are left with doubts and the response mechanisms are slow. This is not very transparent and therefore compromises the operational trust and the traceability of high-risk decisions in real-time defense infrastructures. Current explainable AI (XAI) methods, despite their usefulness, are mostly stagnant, not connected to the changing situation of network behavior and human monitoring. They seldom inculcate the feedback mechanisms that can adjust the model explanations with new threat patterns. The research paper introduces an Explainable Artificial Intelligence and Data-Driven Security Analytics Framework that is hybrid and serves to combine global interpretability with SHAP, local reasoning with LIME, and adaptive refinement with an analyst-in-loop feedback layer. The architecture converts the threat detection to a self-fixing transparent process in which the analytical reasoning is dynamically developed with the data flow. In experimental tests of less than 10k concurrent traffic conditions, experimental results indicate that the system had a detection accuracy of 98.4 and Spearman correlation (0.91) between predicted risk scores and real levels of severity with a quantifiable 3.9% stability improvement over the similar XAI-based systems of intrusion detection. The combination of explainability with real-time analytics will further enhance the accuracy of detecting cyber threats and its interpretability reliability as well. The findings indicate the essential change to credible, self-explanatory, and adaptive security intelligence of the next-generation data networks.

The exponential growth of Internet of Things (IoT) devices integrated with fifth-generation (5G) wireless networks has created unprecedented opportunities for ultra-low-latency applications while introducing complex security vulnerabilities and computational challenges. This paper presents a comprehensive framework for deploying adaptive lightweight Convolutional Neural Networks (CNNs) in 5G-enabled IoT environments to address intrusion detection, intelligent traffic classification, and dynamic resource optimization. We propose a novel multi-objective optimization approach that integrates Adaptive Depthwise Separable Convolutions (ADSC), Dynamic Quantization-Aware Training (DQAT), and Real time Pruning Strategy (RPS) specifically designed for 5G network slicing architectures. Our methodology incorporates federated learning principles, edge-cloud collaboration, and context-aware adaptation mechanisms. Comprehensive evaluation on multiple datasets, including NF-ToN-IoT-v2, NSL-KDD, and CICIDS-2017, demonstrates superior performance with 97.8% accuracy in multi-class attack detection, 76% reduction in computational overhead, 71% decrease in energy consumption, and 42% improvement in network throughput. The framework achieves inference times under 8.5ms on edge devices while maintaining robust security postures across heterogeneous IoT deployments. Statistical significance testing and large-scale ablation studies verify the effectiveness of each of the suggested elements.

Current network security technologies face new threats from determined attackers employing advanced evasion techniques such as IP spoofing, tiny fragment attacks, tunneling, and HTML smuggling. Conventional intrusion detection and prevention systems (IDS/IPS) have significant limitations in detecting zero-day attacks and sophisticated threats that can continuously alter their attack vectors. This paper presents a novel deep learning-driven, multilayer intrusion detection and prevention framework that integrates network-based IDS/IPS, host-based intrusion detection systems (HIDS), and honeypot technologies with advanced machine learning models, including graph neural networks (GNNs), autoencoders, and transformers. The framework employs adaptive learning mechanisms to enhance resilience against evasion techniques while maintaining low false positive rates. Experimental evaluation using diverse attack datasets demonstrates superior performance, achieving 97.3% detection accuracy for zero-day attacks and 94.8% resilience against advanced evasion techniques, significantly outperforming existing state-of-the-art solutions. The proposed framework contributes to cybersecurity research by introducing innovative multilayer correlation mechanisms, adaptive threat modeling, and evasion-resilient detection algorithms.

Man-in-the-Browser (MitB) attacks represent a sophisticated class of web-based threats that manipulate browser functionality to intercept and modify user transactions in real-time. Traditional server-side detection mechanisms often fail to identify these attacks due to their client-side nature and encrypted communication channels. This paper presents a novel client-side runtime integrity agent that employs forensic monitoring and machine learning-based anomaly detection to identify MitB attacks at their source. The proposed system integrates DOM integrity verification, memory forensic analysis, and behavioral pattern recognition to detect malicious browser modifications before they can compromise user sessions. Our experimental evaluation demonstrates a detection accuracy of 97.3% with a false positive rate of 2.1%, significantly outperforming existing client-side detection methods. The system successfully identified various MitB attack vectors, including Zeus, SpyEye, and custom injection payloads, while maintaining a minimal computational overhead of less than 3% CPU utilization.