The rise of connected and autonomous vehicles introduces significant cybersecurity challenges for embedded systems. One of the most vulnerable components is the Controller Area Network (CAN), which manages communication between a vehicle’s electronic units. Originally designed without built-in security mechanisms, the CAN bus is particularly susceptible to message injection attacks. This paper presents a hybrid anomaly detection framework that combines traditional models like Support Vector Machines (SVM) with recurrent neural networks such as Long Short-Term Memory (LSTM). We also evaluate additional algorithms, including Random Forest, Isolation Forest, and Autoencoders. The proposed architecture leverages the LSTM to extract temporal features from CAN traffic and uses the SVM for precise classification, balancing dynamic detection capabilities with real-time efficiency. Experimental results demonstrate that the hybrid model outperforms individual approaches in terms of precision, recall, F1-score, and Area Under the Curve (AUC), while also reducing false positive rates and increasing robustness. This makes the proposed framework a promising solution for enhancing the cybersecurity of modern in-vehicle networks.

Multi-label classification presents a complex computational challenge with broad applications in text categorization, image annotation, and bioinformatics. In this paper, we introduce a pioneering approach that merges Deep Extra-Trees with three transformation methods to tackle this intricate task. Through comprehensive evaluations conducted on a range of benchmark datasets, we meticulously compare our method against established algorithms. The results not only validate our approach but also reveal its superiority, demonstrating enhanced performance and robustness. Our approach utilizes transformation methods of multi-class classification in conjunction with Deep Extra-Trees. Specifically, we implement Binary Relevance, Classifier Chains, and Label Powerset as our transformation methods, which effectively convert the multi-label problem into multiple single-label problems, thereby leveraging the power of Deep Extra-Trees for improved prediction accuracy. This substantiates the robustness and adaptability of our proposed methodology across diverse datasets. By offering a compelling solution to the multi-label classification problem, our research contributes significantly to the advancement of machine learning techniques in various domains. Moreover, we apply this approach not only to classification tasks but also to anomaly detection, further demonstrating its versatility and practical utility.

In the ever-evolving landscape of real-time usage, there is a constant pursuit of advancements in infrastructures and technologies to meet the growing demand for network accessibility across a wide range of services. In this context, the advent of 5G technology has brought about transformative changes in the realm of networking. 5G, the fifth generation of wireless communication technology, offers unprecedented speed, low latency, and massive connectivity. It has become a critical enabler for applications ranging from autonomous vehicles to the Internet of Things (IoT). The integration of 5G into the networking infrastructure introduces new dimensions to the challenges and opportunities associated with hypercube routing. One significant area of interest in this regard is the implementation of hypercube routing, which poses notable challenges. Researchers and professionals are actively exploring various methods and techniques to achieve precise computations within optimal time limits. This paper specifically focuses on a fundamental concept: the use of hypercubes in networking and their application as a routing solution. Additionally, the paper aims to investigate existing proposals and techniques that are relevant to addressing concerns like fault-tolerant routing. Furthermore, the paper introduces a novel scheme for routing in hypercube networks, incorporating a multicluster node, thereby presenting an innovative approach to tackle these challenges within the 5G ecosystem. Taking advantage of 5G capabilities, this scheme could potentially improve the efficiency and fault tolerance of hypercube routing in modern network infrastructures.

Today’s cellular networks (known as 2G, 3G, and 4G) provide a solid foundation for connecting things. The Internet of Things (IoT) is helping people live and work smarter and take full control of their lives. In addition to providing smart devices for home automation, IoT is also critical for businesses. The security of classical cryptosystems is characterized by their simple arithmetic complexity. This type uses; shifts, arrangements, permutations, and substitutions of letters, words, or phrases to encrypt a given message. Moreover, this kind of cryptosystem is easy to break, because the complexity of their scheme is convergent. On the other hand, the security of modern cryptosystems is characterized by complex arithmetic, using the concept of keys; private keys to encrypt and public keys to decrypt. This type is hard to break because of the complexity of their scheme being divergent. This paper aims to secure the 5G networks with a new variant of the classical Polybius Checkerboard Cipher (HPCC). The system of letter substitution is based on the complexity and the divergence of private key generation. Filling a magic square with primes is an NP-hard task, which shows the complexity and divergence of the strategy adopted in this variant.

Over the last decade, the demand for greater security in 5G networks has grown significantly. Ensuring data security during transmission against external attacks has become a critical priority. However, existing security systems, which focus on attack identification, face limitations in terms of both security and performance. This requires the implementation of more rigorous measures. Meeting the need for improved security in 5G networks calls for advanced machine learning techniques. To tackle this challenge, a proposed hybrid mechanism employs various machine learning approaches to effectively classify threats, such as denial of service, detection denial, and resource misuse. The incorporation of the DET model improves the accuracy of decision making and improves attack classification for 5G networks. Key accuracy parameters, including recall, precision, and F-score, play a crucial role in ensuring the model’s reliability. Simulation results demonstrate the superiority of the proposed model compared to others, particularly in terms of accuracy. Our approach presents a promising solution for identifying and categorizing attacks in 5G networks. By prioritizing accuracy and providing superior performance, this research significantly contributes to ongoing efforts to improve 5G network security.

In this paper, we propose a novel approach for building and initializing deep neural networks based on extremely randomized trees (extra-trees) an ensemble learning method for both classification and regression and feature extraction techniques. We use convolutional neural networks (CNNs), a family of modern deep learning models, extensively used in the area of computer vision and image classification, to improve the accuracy and generalization performance of classifiers. First, a CNN model is built to automatically extract multi-level features from the data. Second, a random forest obtains the structures of the trees. Finally, the neural networks (MLP) are built. This hybrid method combines two standard adaptive methods: decision trees and artificial neural networks. In this article, we illustrate the structure of the hybrid method, the problems occurring during the building of the model, and the solutions for these problems. The experimental results indicate that the proposed approach achieves consistently high performance for a variety of regression and classification tasks. These results should motivate further studies seeking to develop accurate and efficient tree-based models.