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.

We present an investigation analysis approach for mining anonymous email content. The core idea behind our approach is concentrated on collecting various effective features from previous emails for all the possible suspects. The extracted features are then used with several machine learning algorithms to extract a unique writing style for each suspect. A sophisticated comparison between the investigated anonymous email and the suspects writing styles is employed to extract evidence of the possible email sender. Extensive experimental results on a real data sets show the improved performance of the proposed method with very limited number of features.

Position-based routing algorithms use the location information to reduce routing overhead in mobile ad-hoc networks. In this paper we propose two position-based routing algorithms which combine progress-based routing with restricted directional flooding-based routing algorithms for routing in 3 dimensional environments (3D). The first algorithm 3D_ABLAR(m) chooses m neighbors according to a space-partition heuristic and forwards the message to all these nodes. The second algorithm (C, G)-3D_ABLAR(m)-(C, G) uses progress-based routing until a local minimum is reached. The algorithm then switches to the first algorithm for one step and then progress-based routing is resumed. We evaluate our algorithm and compare it with current routing algorithms. The simulation results show a significant improvement in delivery rate (99% compared to 63%) and reduction in traffic (up to 50%)

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.