In-vehicle communication systems are usually managed by controller area networks (CAN). By broadcasting packets to their bus, the CAN facilitates the interaction between Electronic Control Units (ECU) that coordinate, monitor and control internal vehicle components. With no authentication mechanism for identifying the legitimacy and source of packets, CAN are vulnerable to cyber-attacks. An Intrusion Detection System (IDS) can detect attacks on CAN and machine learning can be used to create the models for the IDSs to detect non-linear attack patterns. However, car manufacturers and owners might want to keep the sensitive information required for training the models confidential. Therefore, we proposed a Blockchain-based Federated Forest Software-Defined Networking (SDN)-enabled IDS (BFF-IDS) to address the problem of data sharing the sensitive CAN data. To ensure scalability, we used InterPlanetary File System (IPFS) to host the models, and the blockchain is designed to store only a hash of the model and a pointer to its location. The SDN provides the dynamic routing of packets and model exchanges. We used Federated Learning (FL) to create a random forest model. Individuals provide partially trained models, allowing them to keep the underlying data confidential. Using Fourier transform, we decomposed the CAN IDs cycle from CAN bus traffic in the frequency domain for better generalization in multiclass detection of attacks. Multiple statistical and entropy features were extracted to handle the high complexity and non-linearity in CAN bus traffic. The proposed system allows manufacturers and car owners to contribute to the training of the models, as their sensitive data is protected. By storing hashes of the models on a blockchain, the risk of adversaries poisoning the models is reduced and a single point of failure is avoided. We evaluated the proposed system by conducting experiments on a testbed. We found that the proposed system has efficient use of memory and CPU resources and that the detection rate of closely related attacks was high. We recorded the highest model attack detection rate of about 0.981.

Epilepsy remains one of the most common chronic neurological disorders; hence, there is a need to further investigate various models for automatic detection of seizure activity. An effective detection model can be achieved by minimizing the complexity of the model in terms of trainable parameters while still maintaining high accuracy. One way to achieve this is to select the minimum possible number of features. In this paper, we propose a long short-term memory (LSTM) network for the classification of epileptic EEG signals. Discrete wavelet transform (DWT) is employed to remove noise and extract 20 eigenvalue features. The optimal features were then identified using correlation and P value analysis. The proposed method significantly reduces the number of trainable LSTM parameters required to attain high accuracy. Finally, our model outperforms other proposed frameworks, including popular classifiers such as logistic regression (LR), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT).

The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models are still vulnerable to evasion, poisoning and exploratory attacks by adversarial examples. The BFF-IDS offers partial defence against poisoning but has no measure for evasion attacks, the most common attack/threat faced by ML models. Besides, the impact of adversarial examples transferability in CAN IDS has largely remained untested. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We also investigated the statistical adversarial detector’s effectiveness and resilience in detecting the attacks and subsequent countermeasures by augmenting the model with detected samples. Our investigation results established that BFF-IDS is very vulnerable to adversarial examples attacks. The statistical adversarial detector and the subsequent BFF-IDS augmentation (BFF-IDS(AUG)) provide an effective mechanism against the adversarial examples. Consequently, integrating the statistical adversarial detector and the subsequent BFF-IDS augmentation with the detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks.

Manta ray foraging optimization algorithm (MRFO) is a novel metaheuristic optimization algorithm based on the smart conduct of manta rays. MRFO algorithm is among the most newly developed swarm based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of their food. MRFO algorithm has been found to be efficient in resolving a number of optimization difficulties in various domains, due to its ability in providing an equilibrium between global and local search during the search procedure, resulting in near optimal results. As a result, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO growths. First, the paper is introduced on natural inspiration context of MRFO and its conceptual optimization framework. Further, MRFO is discussed in the aspect of its modifications, hybridizations, and applications across different domain. Then, the meta-analysis of the developments of MRFO since its introduction is presented. Thereafter, the paper presented a discussion on possible future research directions. This study will be useful for researchers and practitioners in fields such as optimization, engineering design, machine learning, scheduling, and image processing.

The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. The proposed method utilizes the data generated in the OMNeT++ environment and verifies that the learning time is reduced by approximately 25% depending on the feature extraction interval, while the accuracy remains similar.

The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks to meet computation-demanding applications, such as the metaverse. In this vein, we consider the partial computation offloading problem in the metaverse for multiple subtasks in a COIN environment to minimize energy consumption and delay while dynamically adjusting the offloading policy based on the changing computational resource status. The problem is NP-hard, and we transform it into two subproblems: the task-splitting problem (TSP) on the user side and the task-offloading problem (TOP) on the COIN side. We model the TSP as an ordinal potential game and propose a decentralized algorithm to obtain its Nash equilibrium (NE). Then, we model the TOP as a Markov decision process and propose the double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, the COIN agent explores the NE of the TSP and the deep neural network. Finally, the simulation results reveal that the proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline.