Card fraud detection refers to the process of identifying unauthorized or suspicious transactions made using credit or debit cards. It employs machine learning models, rule-based systems, and anomaly detection techniques to detect patterns indicating potential fraud. There is a growing need for systems that can accurately predict and prevent fraudulent transactions. Reducing financial loss by Implementing advanced detection models to safeguard it from fraud or malicious transactions. Therefore, we proposed machine learning models that will predict credit card fraud at an early stage. Also, the study used feature scaling, Principal Component Analysis (PCA), and the Synthetic Minority Over-sampling Technique (SMOTE) to deal with the class imbalance on the dataset. Moreover, SMOTE is applied to balance the classes by synthesizing examples of the minority class, making classifiers more robust. The results show that LR, SVM, KNN, and XGBoost models correctly predict 97\% of fraudulent and non-fraudulent cases. The Decision Tree and the Random Forest models are capable of achieving at least 96\%, respectively. This research combines advanced machine learning methodologies with real-time processing to give insights into predictive analytics in financial fraud detection, which may enhance accuracy and efficiency in financial security systems.

To address evolving security challenges in cloud computing, this study proposes a hybrid deep learning architecture integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) for cloud intrusion detection. The BiLSTM-BiGRU model synergizes BiLSTM's long-term dependency modeling with BiGRU's efficient gating mechanisms, achieving a detection accuracy of 96.7% on the CIC-IDS 2018 dataset. It outperforms CNN-LSTM baselines by 2.2% accuracy, 3.3% precision, 3.6% recall, and 3.6% F1-score while maintaining 0.03% false positive rate. The architecture demonstrates operational efficiency through 20% reduced computational latency and 15% lower memory footprint compared to conventional models, enabled by residual memory preservation and parallel processing capabilities. Experimental results validate its dual competence in detecting both known attack patterns (98.1% recognition rate) and zero-day threats (93.4% anomaly identification), establishing a methodological framework for real-time cloud security services. This work advances hybrid deep learning applications in trusted computing environments through optimized temporal feature extraction and resource-aware threat detection.

The Internet of Vehicles (IoV) has greatly influenced transportation by allowing autonomous vehicles to interact and communicate with other cars as well as with the surrounding traffic system. Even so, being interconnected comes with risks in terms of cyber attacks, for example, by injecting messages or fooling sensors through CAN systems. The study, consequently, suggests an Intrusion Detection System (IDS) that uses Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and RoBERTa, to properly detect and handle these cyber threats. To solve the problem of unbalanced data, we use Random Over-Sampling (ROS), NearMiss, and Tomek Links, which makes the model work more effectively. Results from experiments show that the IDS suggested here is better than traditional machine learning models. Adopting cloud computing helps make the system more flexible and lets it be watched continually to keep AVs well protected. An IDS in smart cities greatly benefits vehicle networks, as it hinders and stops possible cyberattacks. Related work will aim to improve IDS performance for big organizations and cope with new types of attacks.

In recent years, vehicular ad hoc networks (VANETs) have faced growing security concerns, particularly from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks flood the network with malicious traffic, disrupting services and compromising resource availability. While various techniques have been proposed to address these threats, this study presents an optimized framework leveraging advanced deep-learning models for improved detection accuracy. The proposed Intrusion Detection System (IDS) employs Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Belief Networks (DBN) alongside robust feature selection techniques, Random Projection (RP) and Principal Component Analysis (PCA). This framework extracts and analyzes significant features using a publicly available application-layer DoS attack dataset, achieving higher detection accuracy than traditional methods. Experimental results indicate that combining CNN, LSTM networks, and DBN with feature selection techniques like Random Projection (RP) and PCA results in improved classification performance, achieving an accuracy of 0.994, surpassing the state-of-the-art machine learning models. This novel approach enhances the reliability and safety of vehicle communications by providing efficient, real-time threat detection. The findings contribute significantly to VANET security, laying a robust foundation for future advancements in connected vehicle protection.

The Internet of Vehicles (IoVs) is an emerging technology that enhances transportation systems by enabling interactions between vehicles, infrastructure, and other entities. Securing IoV networks from cyber threats like eavesdropping, data tampering, and intrusions is a major challenge. This research presents a Blockchain-Enabled Secure Authentication Protocol for IoVs (BESA-IOV), which leverages blockchain’s decentralized and tamper-resistant nature for secure communication in vehicular networks. By utilizing ECC-based lightweight cryptography and blockchain-based public key management, it ensures strong authentication, confidentiality, and integrity. The results show that BESA-IOV significantly reduces authentication delay and computational cost compared to protocols such as NOTSA, RVAC, VANET-Auth, and SecureIoV. Extensive simulations indicate that BESA-IOV reduces authentication delay by 35% and computational overhead by 40%, enhancing real-time communication in the IoV environment. BESA-IOV is secure, efficient, and scalable for next-generation IoV systems.