Recent improvements in self-driving and connected cars promise to enhance traffic safety by reducing risks and accidents. However, security concerns limit their acceptance. These vehicles, interconnected with infrastructure and other cars, are vulnerable to cyberattacks, which could lead to severe costs, including physical injury or death. In this article, we propose a framework for an intrusion detection system to protect internal vehicle communications from potential attacks and ensure secure sent/transferred data. In the proposed system, real auto-network datasets with Spoofing, DoS, and Fuzzy attacks are used. To accurately distinguish between benign and malicious messages, this study employed seven distinct supervised machine-learning algorithms for data classification. The selected algorithms encompassed Decision Trees, Random Forests, Naive Bayes, Logistic Regression, XG Boost, LightGBM, and Multi-layer Perceptrons. The proposed detection system performed well on large real-car hacking datasets. We achieved high accuracy in identifying diverse electronic intrusions across the complex internal networks of connected and autonomous vehicles. Random Forest and LightGBM outperformed the other algorithms examined. Random Forest outperformed the other algorithms in the merged dataset trial, with 99.9% accuracy and the lowest computing cost. The LightGBM algorithm, on the other hand, performed admirably in the domain of binary classification, obtaining the same remarkable 99.9% accuracy with no computing overhead.

Semantic-enhanced recommendation systems are promising approaches to overcome the sparsity and cold-start problems, which are hard to handle using the conventional collaborative filtering (CF) approaches. Further research is needed to effectively integrate ontologies into collaborative filtering recommender systems. This paper proposes an ontology-based semantic similarity measure to evaluate similarities between items and eventually generate accurate recommendations. The proposed semantic similarity measure termed fusion-based semantic similarity takes into account the semantics of ontological instances (i.e. items) inferred from a specific domain ontology, which is determined by analyzing the hierarchical relationships among the instances, as well as the features of the instances and their relationships to other instances. The new measure comprehensively captures the semantic knowledge associated with instances by exploiting all possible shared semantics between instances in a given domain ontology. Furthermore, this paper proposes a new semantic-enhanced hybrid recommendation approach as a result of combining the new semantic similarity measure with the standard item-based CF to enhance the quality of generated recommendations. In order to assess the effectiveness of our semantic-enhanced hybrid collaborative filtering method, a series of experiments were conducted to compare the performance of the proposed approach against well-established benchmark techniques. The reported experimental results consistently emphasize its superiority, demonstrating enhanced predictive abilities and a notable improvement in the quality of recommendations. More specifically, the proposed approach achieved notable 6% reduction in Mean Absolute Error (MAE) in certain cases, outperforming other benchmark techniques. Additionally, this study highlights the potential of using semantic-based similarity to enhance the performance of recommendation systems. Such enhancements address challenges within collaborative filtering, potentially leading to advancements in recommendation system design and optimization.

Electronic health records (EHRs) are widely used in healthcare institutions worldwide, containing vast amounts of unstructured textual data. However, the sensitive nature of Protected Health Information (PHI) embedded within these records presents significant privacy challenges, necessitating robust de-identification techniques. This paper introduces a novel approach, leveraging a Bi-LSTM-CRF model to achieve accurate and reliable PHI de-identification, using the i2b2 dataset sourced from Harvard University. Unlike prior studies that often unify Bi-LSTM and CRF layers, our approach focuses on the individual design, optimization, and hyperparameter tuning of both the Bi-LSTM and CRF components, allowing for precise model performance improvements. This rigorous approach to architectural design and hyperparameter tuning, often underexplored in the existing literature, significantly enhances the model’s capacity for accurate PHI tag detection while preserving the essential clinical context. Comprehensive evaluations are conducted across 23 PHI categories, as defined by HIPAA, ensuring thorough security across critical domains. The optimized model achieves exceptional performance metrics, with a precision of 99%, recall of 98%, and F1-score of 98%, underscoring its effectiveness in balancing recall and precision. By enabling the de-identification of medical records, this research strengthens patient confidentiality, promotes compliance with privacy regulations, and facilitates safe data sharing for research and analysis.

The lack of regulations and oversight on Online Social Networks (OSNs) has resulted in the rise of social spam, which is the dissemination of unsolicited and low-quality content that aims to deceive and manipulate users. Social spam can cause a range of negative consequences for individuals and businesses, such as the spread of malware, phishing scams, and reputational damage. While machine learning techniques can be used to detect social spammers by analysing patterns in data, they have limitations such as the potential for false positives and false negatives. In contrast, ontologies allow for the explicit modelling and representation of domain knowledge, which can be used to create a set of rules for identifying social spammers. However, the literature exposes a deficiency of ontologies that conceptualize domain-based social spam. This paper aims to address this gap by designing a domain-specific ontology called DSpamOnto to detect social spammers in microblogging that targes a specific domain. DSpamOnto can identify social spammers based on their domain-specific behaviour, such as posting repetitive or irrelevant content and using misleading information. The proposed model is compared and benchmarked against well-proven ML models using various evaluation metrics to verify and validate its utility in capturing social spammers.