The healthcare industry is one of the most vulnerable to cybercrime and privacy violations because health data is very sensitive and spread out in many places. Recent confidentiality trends and a rising number of infringements in different sectors make it crucial to implement new methods that protect data privacy while maintaining accuracy and sustainability. Moreover, the intermittent nature of remote clients with imbalanced datasets poses a significant obstacle for decentralized healthcare systems. Federated learning (FL) is a decentralized and privacy-protecting approach to deep learning and machine learning models. In this paper, we implement a scalable FL framework for interactive smart healthcare systems with intermittent clients using chest X-ray images. Remote hospitals may have imbalanced datasets with intermittent clients communicating with the FL global server. The data augmentation method is used to balance datasets for local model training. In practice, some clients may leave the training process while others join due to technical or connectivity issues. The proposed method is tested with five to eighteen clients and different testing data sizes to evaluate performance in various situations. The experiments show that the proposed FL approach produces competitive results when dealing with two distinct problems, such as intermittent clients and imbalanced data. These findings would encourage medical institutions to collaborate and use rich private data to quickly develop a powerful patient diagnostic model.

Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks, including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the targeted device with numerous network packets, making its resources inaccessible to authorized users. Such attacks may comprise attack references, attack types, sub-categories, host information, malicious scripts, etc. These details assist security professionals in identifying weaknesses, tailoring defense measures, and responding rapidly to possible threats, thereby improving the overall security posture of IoT devices. Developing an intelligent Intrusion Detection System (IDS) is highly complex due to its numerous network features. This study presents an improved IDS for IoT security that employs multimodal big data representation and transfer learning. First, the Packet Capture (PCAP) files are crawled to retrieve the necessary attacks and bytes. Second, Spark-based big data optimization algorithms handle huge volumes of data. Second, a transfer learning approach such as word2vec retrieves semantically-based observed features. Third, an algorithm is developed to convert network bytes into images, and texture features are extracted by configuring an attention-based Residual Network (ResNet). Finally, the trained text and texture features are combined and used as multimodal features to classify various attacks. The proposed method is thoroughly evaluated on three widely used IoT-based datasets: CIC-IoT 2022, CIC-IoT 2023, and Edge-IIoT. The proposed method achieves excellent classification performance, with an accuracy of 98.2%. In addition, we present a game theory-based process to validate the proposed approach formally.

Vehicle Ad Hoc Networks (VANET) are a technology that allows existing transportation systems to deliver safety and critical information. Due to real-time communication, it generates massive amounts of data, making it vulnerable to eavesdropping and interference. Intrusion Detection Systems (IDS) are crucial devices that detect and respond to malicious activity. This paper develops a novel network intrusion detection system for VANET that leverages Spark-based big data optimization and transfer learning (NIDS-VSB). First, a packet parser is used to crawl network traffic and filter required flow events. Next, the Spark-based optimization method is implemented to process large amounts of data efficiently. Furthermore, a transfer learning strategy is designed to learn the comprehensive feature representation using their semantic anchors. A Convolutional Neural Network (CNN) model is used to extract deep features from semantic anchors. Finally, a stacking generalization ensemble model uses deep features to detect various attacks. The proposed approach is tested on three standard and publicly available datasets, namely the CIC-IoT 2022, CIC-IDS2017, and NSL_KDD datasets. Our findings show that the proposed approach delivers the best detection results for a variety of attacks including audio, HTTP, BruteForce, web, and others.

Obfuscated and malicious URLs may lead to harmful content or actions to the system, such as downloading malware, phishing, scams, or adware. In the domain of cybersecurity, the identification of the obfuscated Uniform Resource Locator (URL) is a concerning facet. This study proposes a Robust unified TabNet ensemble model for the identification of Malicious URLs with feature extraction based on the computation of features’ importance for classification. A fine-tuned attention-based deep neural network TabNet is used to extract the features of the URL. The customized data with the most important features is generated, and a Machine Learning (ML) ensemble model is developed for the classification of the URLs. The evaluation parameters accuracy, Precision, Recall, and F1-score are measured to look at the performance of the TabNet ensemble model. Accuracy of 97.8%, precision of 0.978, recall of 0.976, and F1-score of 0.978 reflect the outperforming results of the proposed model while classifying the five URL classes. The model is further validated through statistical analysis by measuring the Kappa value, which comes up as 0.968 for the proposed model. With a 10-fold cross-validation model, we attained a mean accuracy of 97.27% and a confidence interval of 0.004. The Local Interpretable Model-agnostic Explanations (LIME) explainable AI model is used to validate the model to perceive the contributing features towards the classification model. The results are compared with the state-of-the-art ML classifiers and the previous studies, and the whole validation process favors the proposed model’s efficacy.