Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

Federated Learning (FL) has rapidly emerged as a transformative machine learning approach, enabling healthcare institutions to collaboratively build predictive models without compromising patient data privacy. As healthcare increasingly adopts digital technologies, federated learning offers promising solutions to critical issues such as data privacy, security, data poisoning, and adversarial attacks. Despite the recognized potential of FL, significant gaps persist in existing research, particularly concerning comprehensive security frameworks and practical healthcare applications. This bibliometric analysis systematically explores the research landscape from 2021 to 2024, explicitly focusing on data privacy, security threats, and adversarial attacks within federated learning in healthcare. Utilizing bibliometric data from the Scopus database, the study identifies key thematic trends, evaluates global collaborative networks, and assesses contributions from leading institutions and countries. Findings reveal rapidly growing scholarly interest, robust international collaboration, and notable institutional contributions, with a specific emphasis on privacy-preserving techniques, healthcare-specific applications, and emerging technologies such as blockchain and edge computing. The analysis also highlights critical limitations due to incomplete bibliographic metadata. This research provides a comprehensive understanding of current trends and identifies future directions to enhance the security and privacy framework of federated learning in healthcare.

With the exponential rise in the adoption of the Internet of Things (IoT), sensors have become an essential part of smart systems, enabling real-time monitoring and control in applications such as energy management, security, and safety. Among these, early fire detection is a critical application to prevent devastating consequences. This paper introduces a novel Federated Learning (FL) framework designed for the rapid detection of forest fires within smart and sustainable environments using the Green Internet of Things (GIoT). The proposed framework integrates distributed learning across multiple edge devices to detect fire incidents without compromising data privacy. It leverages a modified DenseNet121 architecture enhanced with a soft attention mechanism, capable of accurately classifying fire and no-fire scenarios even under challenging weather conditions. The dataset was augmented to simulate fog and haze, ensuring model robustness in real-world environments. The experimental results demonstrate that the proposed system achieves outstanding performance with a training accuracy of 97.8% and a validation accuracy of 97.06%, confirming its effectiveness and scalability in edge-enabled fire detection systems.

Memory Denial of Service (M-DoS) attacks refer to a class of cyber-attacks that aim to exhaust the memory resources of a system, rendering it unavailable to legitimate users. This type of attack is particularly dangerous in cloud computing environments, where multiple users share the same resources. Detection and mitigation of M-DoS attacks in real-time is a challenging task, as they often involve a large number of low-rate requests, making it difficult to distinguish them from legitimate traffic. Several real-time detection schemes have been proposed to identify and mitigate M-DoS attacks in cloud computing environments. These schemes can be broadly classified into two categories: signature-based and anomaly-based detection. Signature-based detection methods rely on the identification of specific patterns or characteristics of known M-DoS attack techniques, while anomaly-based detection methods identify abnormal behaviour that deviates from the normal pattern of usage. This study presents a hybrid model for real-time detection of cloud and MDOS attacks using SVM-KNN-LR. The dataset used in this study was collected from various sources and pre-processed to extract relevant features for attack detection. A feature selection process was also applied to identify the most important features for attack detection. The hybrid model achieved an accuracy of 96%, outperforming other individual models such as SVM, KNN, LR, Naive Bayes, Decision Trees, Extra Trees, Bagging Trees, and Random Forests. Confusion matrices were also used to evaluate the performance of each model. In the discussion section, we examined the performance of the hybrid model in detecting MDOS attacks and found that it had a high precision score of 0.97. However, the recall score was lower at 0.87, indicating that the model was not able to detect all instances of MDOS attacks.

With the increasing adoption of Internet of Medical Things (IoMT) devices, modern healthcare systems face persistent challenges related to data privacy, device heterogeneity, communication overhead, and scalability especially in consumer electronics environments. This paper hypothesizes that a hierarchical and privacy-preserving federated learning architecture can address these challenges and enable efficient, secure, and personalized healthcare insights across distributed IoMT networks. To validate this, we present the Adaptive Federated Learning Framework (AFLF), a novel framework tailored for resource-constrained consumer devices such as wearable ECG monitors, glucose trackers, and portable neuro-sensors. AFLF incorporates four core components: (1) a hierarchical edge–fog–cloud model for scalable and latency-aware training, (2) a Secure Data Collaboration Protocol (SDCP) that combines blockchain-based audit trails with differential privacy to ensure integrity and confidentiality, (3) the Adaptive Personalized Federated Learning Algorithm (APFLA) that dynamically tunes learning rates and model weights for personalized inference, and (4) a ternary threshold-based gradient compression technique that reduces communication overhead by 45.3%. The framework is deployed on Raspberry Pi 4 (edge) and NVIDIA Jetson AGX Xavier (fog) platforms, using PyTorch Mobile for implementation. Experimental validation using four real-world datasets, CardioFit (ECG), GlucoWatch (glucose), PulseOx (PPG), and NeuroMotion (EEG), demonstrates that AFLF improves model accuracy by up to 12% reduces training time by 38% and preserves user privacy with a differential privacy budget of ε = 2.1. These results confirm that AFLF offers a robust and scalable privacy-preserving federated learning solution for next-generation consumer-centric smart healthcare applications.

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.