Protection against unwanted intrusions is crucial for preserving the integrity and security of connected devices in the context of Internet of Things (IoT) networks. The growing number of IoT devices has made several industries more vulnerable to cyberattacks and security breaches, including smart homes, industrial automation, and healthcare. In response to this pressing dilemma, the goal of this project is to create a novel method for intrusion detection in Internet of Things systems utilizing Denoising Autoencoder (DAE) models. Traditional intrusion detection methods often prove inadequate in Internet of Things scenarios due to resource restrictions, dynamic network topologies, and a diversity of communication protocols. By utilizing DAEs’ unsupervised learning and feature extraction skills, our suggested approach creates a system that can identify and stop intrusion attempts in real-time. The evaluation of the study additionally makes use of the NSL-KDD and CICIDS 2017 datasets. DAE integration yields an unequaled accuracy of 99.991% when the CICIDS 2017 dataset is used, and an accuracy of 99.4% when the NSL-KDD dataset is used. The CICIDS 2017 dataset analysis reveals several notable performance measures, including an accuracy of 1.0, a precision of 0.995, and an F1-score of 0.998. Analyses of the NSL-KDD dataset also produce outstanding results, with an F1-score of 0.989, recall of 0.991, accuracy of 0.994, and precision of 0.984. The results also show how well the suggested DAE-based intrusion detection method works to stop unauthorized users from accessing IoT devices, which lowers the risk of issues with system integrity, privacy, and security. By strengthening resilience against evolving cyber threats in the networked Internet of Things landscape, this research enhances cybersecurity strategies tailored to address the unique challenges encountered by IoT ecosystems.

Today, protecting patient privacy and ensuring the accuracy and integrity of their data are the two most crucial concerns in the healthcare field. Unauthorized access or changes to patients' private health records can lead to serious issues. Moreover, if healthcare providers fail to update a patient's records quickly, it could result in dangerous, even life-threatening situations. Attacks on hospital computer systems also present a significant danger to patient care. Establishing strong security measures and procedures through cybersecurity frameworks can help protect sensitive patient information, known as electronic protected health information (ePHI). The Security Rule by Health Insurance Portability and Accountability Act (HIPAA), a well-established set of security guidelines, focuses on safeguarding ePHI held by healthcare organizations and their associates. This paper suggests creating a Data Cybersecurity Framework (DCF) specifically for the healthcare sector in Saudi Arabia. This framework aims to shield ePHI and align with the security recommendations of the HIPAA Security Rule. The development of this proposed framework involved consultations with healthcare cybersecurity experts and concentrated on the healthcare system in Saudi Arabia. The research concludes that enhancing the protection of patient information and raising public awareness requires the joint efforts of various entities, including government bodies.

The Internet of Medical Things (IoMT), a key application of the Internet of Things (IoT), has played a key role, especially during the COVID-19 pandemic. Real-time patient monitoring and remote diagnostics help improve medical services, but this increases the mammoth size of network traffic, which impacts the security quite a bit. However, traditional intrusion detection systems lack synchronization between accuracy and energy efficiency in resource-constrained IoMT environments. To address this issue, we present a Hybrid Cross-Temporal Contrastive Model coupled with a spiking energy-efficient network for intrusion detection. This approach uses contrastive learning to learn temporal dependencies in network traffic and Spiking Neural Networks (SNNs) for energy-efficient computations. We evaluated the model on the WUSTL-EHMS-2020 dataset, which consists of 44 features (35 of them are network flow measurements, and 8 are biometric patient features), as well as the NSL-KDD dataset to perform a comparative validation. Furthermore, the experiment results prove that our proposed model achieves 99.95% accuracy on the WUSTL-EHMS-2020 dataset with an F1 score of 99.89%, precision of 98.23%, and recall of 99.55%, outperforming conventional models. The model attained 98.2% accuracy, 97.6% precision, 98.5% F1 score, and 97.3% recall on the NSL-KDD dataset. Our approach shows that these results effectively secure IoMT networks at a low computational cost. Finally, the proposed hybrid model can achieve good performance and energy efficiency for intrusion detection in innovative healthcare systems. In future work, efforts will be made to improve the model’s generalization property in diverse IoMT environments and minimize the energy consumption of SNNs in real-time applications.

Digital forensics has gained a high degree of attention recently due to the benefits it has to offer in investigating cybercrimes by analyzing and presenting digital evidence. It aims to highlight any correlations related to such crimes. Since digital forensics techniques are capable of providing robust and credible evidence, they have been used in different contexts and are accepted by law enforcement agencies. Social media forensics, a subdomain of digital forensics, has also been widely used to investigate digital crimes and suspicious activities on social media platforms. In addition to the challenges associated with digital forensics, such as anti-forensics, social media forensics also faces several obstacles, such as privacy and big data issues. This research proposes a dynamic methodology that utilizes digital forensics techniques to improve the investigation of cybercrimes committed on social media platforms. The proposed methodology offers a high level of flexibility, including choosing the type of crime, the platform used, and the investigation scenario. As part of the contribution of this research, the proposed methodology is used to detect suspicious activities associated with the COVID-19 pandemic. Various techniques and tools are used to evaluate the methodology such as VADER for tweet classification and other tools for collecting and extracting tweets’ information and correlation. The results indicate that the methodology is simple and capable of handling the digital evidence collected, as well as identifying credible relations between the data. The proposed methodology could also be extended in many ways to handle other challenges and limitations associated with digital forensics in general and social media forensics in particular.

The Internet of Medical Things (IoMT), a key application of the Internet of Things (IoT), has played a key role, especially during the Covid 19 pandemic. Real-time patient monitoring and remote diagnostics help improve medical services, but this increases the mammoth size of network traffic, which impacts the security quite a bit. However, traditional intrusion detection systems lack synchronization between accuracy and energy efficiency in resource-constrained IoMT environments. To address this issue, we present a hybrid cross-temporal contrastive model coupled with a spiking energy-efficient network for intrusion detection. This approach uses contrastive learning to learn temporal dependencies in network traffic and spiking neural networks (SNNs) for energy-efficient computations. We evaluated the model on the WUSTL-EHMS-2020 dataset, which consists of 44 features (35 of them are network flow measurements, and 8 are biometric patient features), as well as the NSL-KDD dataset to perform a comparative validation. Furthermore, the experiment results prove that our proposed model achieves 99.95% accuracy on the WUSTL-EHMS-2020 dataset with an F1 score of 99.89%, precision of 98.23%, and recall of 99.55%, outperforming conventional models. The model attained 98.2% accuracy, 97.6% precision, 98.5% F1 score, and 97.3% recall on the NSL-KDD Dataset. Our approach shows that these results effectively secure IoMT networks at a low computational cost. Finally, the proposed hybrid model can achieve good performance and energy efficiency for intrusion detection in innovative healthcare systems. In future work, efforts will be made to improve the model's generalization property in diverse IoMT environments and minimize the energy consumption of spiking neural networks in real-time applications.

The classification of bacterial colony types is crucial for microbiology and medical diagnostics because it provides essential information on pathogen identification and treatment planning. Traditional classification methods are resource intensive and require specialized equipment and expertise. This study explores the integration of smartphone imaging and transfer learning techniques for bacterial colony classification. We expanded an existing dataset of Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) bacteria grown on MacConkey (MAC) agar by incorporating Pseudomonas aeruginosa (P. aeruginosa) on the same medium, as well as Staphylococcus aureus (S. aureus), P. aeruginosa, K. pneumoniae, and E. coli on brain–heart infusion (BHI) agar. The total numbers of images are 9819 and 12348 in the MAC agar and BHI agar datasets, respectively. Using state-of-the-art convolutional neural networks (CNNs) such as SqueezeNet1.0, MobileNetV3 Small, ShuffleNetV2 X1.5, EfficientNetB0, DenseNet121, and ResNet18, we evaluated classification performance across uncontrolled and controlled imaging environments. We also compared the results for the same bacterial type on different media (MAC agar and BHI agar). The results show that the lightweight models EfficientNetB0 and ShuffleNetV2 X1.5 consistently perform well across datasets, reaching 100% accuracy under controlled conditions. We believe that this work provides a foundation for cost-effective bacterial colony classification on smartphones using transfer learning techniques, ultimately enabling practical implementation. Future work will focus on enhancing model explainability and evaluating deployment feasibility on smartphone devices.