D. Sumathi

Modern phishing pages are challenging to detect because they can appear like legitimate brand sites using dynamic document object model (DOM) structures, misleading visual displays, and changing hyperlinks. Most conventional blacklists and URL-based methods do not capture the relationships at multiple levels of the web page structure. In this paper, we introduce a new graph-based phishing detection method to represent a web page with a Multi-Level Web Page Graph (MLWPG). MLWPGs incorporate DOM hierarchies, rendered visual blocks, and hyperlink relations into one heterogeneous graph. We use a novel Deep Learning Method called DHGAN that has Type-Aware Attention and Dynamic Convolution to learn how to differentiate discriminative interaction amongst structural, spatial, and navigation elements. APDA will be used in feature space to enhance robustness to evasive phishing versions. Using a balanced data set of 50,000 web pages, our complete pipeline showed a 97.0% accuracy, 96.8% F1-Score, 3.0% False Positive Rate, and 95.5% Robustness to Adversaries. The proposed MLWPG-DHGAN-APDA framework outperformed all baseline models. Our experimental results demonstrate that multi-level graph models provide improvements to both detection accuracy and reliability of operation for real-time phishing defenses.

In this paper, the integration of the Internet of Things (IoT) in consumer electronics has significantly improved the ability to monitor environmental conditions in real time. This advancement is particularly crucial in patient care and medical data management. However, the vast volume of data generated by these devices demands advanced optimization algorithms to efficiently manage and evaluate this information. This research proposes a next-generation environmental monitoring system designed with a novel Federated Learning-Based Multi-Objective Optimization (FL-MOO) algorithm. This algorithm introduces specific learning parameters designed to enhance data processing quality, ensuring real-time monitoring with minimal latency and optimal resource allocation. By balancing the computational load and maintaining the accuracy of the data across devices enabled by distributed IoT, the proposed FL-MOO algorithm achieves superior performance in terms of speed, accuracy, and resource utilization compared to conventional methods. Simulation experiments demonstrate the efficiency of the proposed algorithm in optimizing data processing within a distributed environment, effectively managing the load distribution between devices. The algorithm also significantly reduced environmental overhead, achieving an average energy consumption of 0.35145, a latency of 0.17376, and a load balancing value of 0.43452. This work paves the way for more scalable and powerful IoT-based solutions in smart health, driving advances in healthcare delivery, patient outcomes, and the effective integration of consumer electronics in environmental monitoring systems.

Phishing attacks are among the persistent threats that are dynamically evolving and demand advanced detection mechanisms to counter more sophisticated techniques. Traditional detection approaches are usually based on single-modal features or static analysis, failing to capture the complex, multi-faceted nature of phishing websites and their dynamic behaviors. Thus, we present a robust Multi-Modal and Temporal Graph Fusion Framework integrating advanced learning paradigms that enhance accuracy and adaptability in phishing detection. Our work proposes four brand-new methods: Multi-Modal Hypergraph Fusion Network (MM-HFN), Temporal Graph Neural Network with Attention (TGNN-Att), Federated Graph Contrastive Learning Network (FGCL-Net), and Multi-Modal Temporal Hypergraph Fusion Network (MMTHF-Net). MM-HFN leverages hypergraphs to capture complex, high-order relationships at textual levels (BERT) and graph-based features versus visual ones (CNNs) for an accuracy in the 95–97% range. TGNN-Att addresses temporal variations in phishing behavior by using attention-enhanced temporal graph networks and LSTMs, providing dynamic detection with 94–96% accuracy. FGCL-Net ensures privacy-preserving learning across decentralized datasets through federated contrastive learning, achieving 93–95% accuracy while safeguarding data privacy. Finally, MMTHF-Net fuses multi-modal and temporal features into a dynamic hypergraph framework, achieving state-of-the-art accuracy of 96–98% with an F1-score of 0.97. These approaches together allow for exact, real-time phishing detection by capturing static and temporal behaviors, high-order relationships, and cross-modal features. The framework proposed demonstrates significant improvements compared to the state of the art, eliminating the shortcomings of single-modality and static analysis while offering scalability, privacy, and adaptability levels.

The escalating threat of phishing attacks poses significant challenges to cybersecurity, necessitating innovative approaches for detection and mitigation. This paper addresses this need by presenting a comprehensive review of state-of-the-art methodologies for phishing detection, spanning traditional machine learning techniques to cutting-edge deep learning frameworks. The review encompasses a diverse range of methods, including list-based approaches, machine learning algorithms, graph-based analysis, deep learning models, network embedding techniques, and generative adversarial networks (GANs). Each method is meticulously scrutinized, highlighting its rationale, advantages, and empirical results. For instance, deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), demonstrate superior detection performance, leveraging their ability to extract complex patterns from phishing data. Ensemble learning techniques and GANs offer additional benefits by enhancing detection accuracy and resilience against adversarial attacks. The impact of this review extends beyond academic discourse, informing practitioners and policymakers about the evolving landscape of phishing detection. By elucidating the strengths and limitations of existing methods, this paper guides the development of more robust and effective cybersecurity solutions. Moreover, the insights gleaned from this review lay the groundwork for future research endeavors, such as integrating contextual information, user behavior analysis, and explainable AI techniques into phishing detection systems. Ultimately, this work contributes to the collective effort to fortify digital defenses against sophisticated phishing threats, safeguarding the integrity of online ecosystems.

With the rapid increase in IoT devices and advanced machine learning and deep learning techniques, there has been a growing concern about computational cost and data privacy issues since the data coming from IoT devices is non-independent identically distributed (non-IID). However, the implementation of the federated learning algorithm has proven to be a booster in the performance and a solution to the existing data privacy concerns. This paper gives insight into topics such as Blockchain, Unmanned Aerial Vehicles (UAV), Wireless communication, Vehicular Internet of Things, Healthcare, and Cloud Computing and how they have been implemented and co-related to federated Learning and the application and the emerging use cases in the field of federated learning (FL) with respect to the above-mentioned topics have also been discussed. This paper uniquely shows how federated learning has an edge over the traditional machine learning and deep learning techniques in IoT infrastructure since computing nodes are trained using local models on the devices and then these local models are uploaded to the central global server instead of data directly into a global model on a central server ensuring data privacy.

Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on ones health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121µW power and 89ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power.

In the global scenario, a variety of wireless access networks are available. Different types of applications such as real time, nonreal time, and high bandwidth availability are used for heterogeneous