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.

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.

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 concept of an Internet of Drones (IoD) is becoming increasingly important in various domains, including surveillance and logistics. Effective communication between the interconnected systems is the essence of the Internet of Drones, however, due to the resource constraints of drones and the dynamic nature of the operating environment, security of communication within IoD networks is indeed the top priority. Considering these challenges on the part of IoD communication, a novel Hyperelliptic Curve Cryptography (HECC)-based authentication protocol is proposed in this paper to secure the data exchange between drones and their control servers and to ensure efficient communication. The proposed HECC protocol is compared with three existing protocols, Elliptic curve cryptography-based protocol with public secure authentication scheme (ECCCPSAS), Non-linear wireless unmanned aerial systems authentication scheme (NLWUAS), and the Multi-Agent System (MAS). The comparison is made among average performance metrics such as communication overhead, packet delivery ratio, throughput, and end-to-end delay. The experimental outcome assures that the HECC protocol outperforms the other protocols in terms of all metrics. The HECC-based authentication protocol reduces communication overhead by up to 30% and end-to-end delay to 75 ms while achieving a 95% packet delivery ratio and 8 Mbps throughput, outperforming ECCCPSAS, NLWUAS, and MAS across all evaluated metrics. These results confirm that HECC is a viable cryptographic solution for resource-constrained IoD deployments. Future work will focus on real-world protocol deployment and integration with quantum-resistant algorithms.