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Abstract Alzheimer’s disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer’s are similar throughout its development stages, which makes it difficult to diagnose manually. Therefore, artificial intelligence (AI) techniques address the limitations of manual diagnosis. In this study, the images were enhanced and the active contour algorithm (ACA) was used to extract regions of interest (ROI) such as soft tissue and white matter. Strategies have been developed to diagnose AD and differentiate its stages. The first strategy is using XGBoost and ANN networks with the features of MobileNet, DenseNet, and GoogLeNet models. The second strategy is by XGBoost and ANN networks with combined features of MobileNet-DenseNet121, DenseNet121-GoogLeNet and MobileNet-GoogLeNet. The third strategy combines XGBoost and ANN networks with combined features of MobileNet-DenseNet121-Handcrafted, DenseNet121-GoogLeNet-Handcrafted, and MobileNet-GoogLeNet-Handcrafted leading to improved accuracy of the strategies and improved efficiency. XGBoost with hybrid features of DenseNet-GoogLeNet-Handcrafted achieved an AUC of 98.82%, accuracy of 98.8%, sensitivity of 98.9%, accuracy of 97.08%, and specificity of 99.5%.

The quick progress of 5G networks has allowed for intelligent driving. The primary environment for intelligent driving is provided by vehicular ad hoc networks (VANETs), which relay real-time data and communications between moving vehicles and fixed infrastructure. Since the communication is open-access, the message exchanged is vulnerable to privacy and security attacks. To address with this challenge, several authentication schemes have proposed. Nevertheless, the complexity of current these schemes means that re-authenticating vehicle identities every time they reach a new area of infrastructure coverage significantly hampers the overall network’s efficiency. This paper has proposed a handover authentication, called HAFC scheme based on fog computing to achieve fastly re-authentication of vehicles via secure property transfer among infrastructures (fog servers) for 5G-assisted vehicular blockchain networks. The proposed HAFC scheme consists of both stages namely, initial-authentication stage and handover-authentication stage. In security analysis shows that the proposed HAFC scheme’s vehicle to fog server-for both stages is Computational Diffie-Hellma (CDH)-secure. According to the simulation results, the novel handover authentication stage takes only a fraction of the time required for the first one.

The fifth-generation (5G) technology-enabled vehicular network has been widely used in intelligent transportation in recent years. Since messages shared among vehicles are always broadcasted by openness environment’ nature, which is vulnerable to several privacy and security problems. To cope with this issue, several researchers have proposed pseudonym authentication schemes for the 5G-enabled vehicular network. Nevertheless, these schemes applied complected and time-consumed operations. Therefore, this paper proposes a fog computing-based pseudonym authentication (FC-PA) scheme to decrease the overhead of performance in 5G-enabled vehicular networks. The FC-PA scheme applies only one scalar multiplication operation of elliptic curve cryptography to prove information. A security analysis of our work explains that our scheme satisfies privacy-preserving and pseudonym authentication, which are resilient against common security attacks. With performance efficiency, our work can obtain better trade-offs between efficiency and security than the well-known recent works.

Vehicle in vehicular ad hoc networks (VANETs) broadcasts beacons about their traffic status wirelessly for improving traffic safety and efficiency. Before deployment of the VANET system, problems related to security and privacy should be carefully addressed. In this article, we propose a lightweight authentication with conditional privacy-preserving scheme for guaranteeing secure communication in VANET. The proposed scheme is suitable for addressing issues related to security and privacy because it combines the tamper-proof device (TPD) based schemes with the roadside unit (RSU) based schemes. Based on elliptic curve cryptography, the proposed scheme preloads the initial public parameters and keys of the system in each TPD of RSU instead of the TPD of the on-border unit (OBU). Furthermore, the proposed scheme not only achieve security and privacy requirements but also resists common security attacks. The performance evaluation shows that the proposed scheme has a lower cost compared with other existing schemes in terms of computation cost and communication cost.

Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems incur exceptionally high communication and processing costs. This study proposes a CPPA method based on fog computing (FC), as a solution for these issues in 5G-enabled vehicle networks. In our proposed FC-CPPA method, a fog server is used to establish a set of public anonymity identities and their corresponding signature keys, which are then preloaded into each authentic vehicle. We guarantee the security of the proposed FC-CPPA method in the context of a random oracle. Our solutions are not only compliant with confidentiality and security standards, but also resistant to a variety of threats. The communication costs of the proposal are only 84 bytes, while the computation costs are 0.0031, 2.0185 to sign and verify messages. Comparing our strategy to similar ones reveals that it saves time and money on communication and computing during the performance evaluation phase.

Internet of things (IoT) is one of the leading technologies that have been used in many fields, such as environmental monitoring, healthcare, and smart cities. The core of IoT technologies is sensors; sensors in IoT form an autonomous network that is able to route messages from one place to another to the base station or the sink. Recently, due to the rapid technological development of sensors, wireless sensor networks (WSNs) have become an important part of IoT. However, in applications such as smart cities, WSNs with one sink might not be suitable due to the limited communication range of sensors and the wide area to be covered. Therefore, multi-sink WSN solutions seem to be suitable for such applications. The multi-sink WSNs are gaining popularity because they increase network throughput, network lifetime, and energy usage. At the same time, multi-hop routing is essential for the WSNS to collect data from sensor nodes and route it to the sink node for decision-making. Many routing algorithms developed for multi-sink WSNs focus on being energy efficient to extend the network lifetime, but the delay was not the main concern. However, these algorithms are unable to deal with such applications in which the data packets have to reach sink nodes within predefined real-time information. On the other hand, in the most existing routing schemes, the effects of the external environmental factors such as temperature and humidity and the reliability of real-time data delivery have largely been ignored. These issues can dramatically influence the network performance. Therefore, this paper designs a routing algorithm that satisfies three critical conditions: energy-efficient, real-time, environment-aware, and reliable routing. Therefore, the routing decisions are made according to different parameters. Such parameters include environmental impact metrics, energy balance metrics to balance the energy consumption among sensor nodes and sink nodes, desired deadline time (required delivery time), and wireless link quality. The problem is formed in integer linear programming (ILP) for optimal solution. The problem formulation is designed to fully understand the problem with its major constraints by the sensor networks research community. In addition, the optimal solution for small-scale problems could be used to measure the quality of any given heuristic that might be used to solve the same problem. Then, the paper proposes swarm intelligence to solve the optimization problem for large-scale multi-sink WSNs as a heuristic algorithm. The proposed algorithm is evaluated and analyzed compared with two recent algorithms, which are the most related to our proposal, SMRP and EERP protocols using an extensive set of experiments. The obtained results prove the superiority of the proposed algorithm over the compared algorithms in terms of packet delivery ratio, deadline miss ratio, average end-to-end delay, network lifetime, and energy imbalance factor under different aspects. In particular, the proposed algorithm requires more computational energy compared to comparison algorithms.

Pulsed-Coupling Oscillators (PCOs) are recently considered as the best energy efficient source of unregulated syncs in Wireless Sensor Network (WSN). PCO utilizes firefly-sync to draw complices. In any case, for sensor networks, a PCO is not feasible, as synchronous transmission costs cannot be borne by WSNs and the processing of information. For certain situations, the exhaustion of a node's battery energy policy (due to the packet collision) is a nonsensical substitute of batteries. To prevent this, A novel process named this analysis the Desynchronization-Traveling-Wave-Pulse-Coupled-Oscillator (DTWPCO) algorithm, an energy-efficient WSN self-organizer that uses Travelling-Wave-Pulse-Coupled-Oscillator (TWPCO) phases locking and the PCO antiphase desynchronization process. The plan aims to reduce the high-power consumption within the network in order to display signs of improved data collection during data transmission for the sensor nodes (SNs). The results of the computer simulation showed that the proposed DTWPCO mechanism was able to achieve 50% and 58% reduction in energy by increasing the amount of transmitted data by SNs, in contrast to TWPCO and PCO methods. The method also increases the data processing ratio by up to 73% and 70% in comparison to the TWPCO and PCO methods, respectively.

Existing conditional privacy-preserving authentication schemes utilized in Vehicular Ad-hoc Networks (VANETs) to satisfy security and privacy requirements essentially depend on point multiplication operations. Achieving repaid verification method of the message is commonly suffer performance efficiency from resulting overheads. We propose a conditional privacy-preserving authentication scheme to secure communication and perform better performance efficiency in this article. The proposed scheme only depends on an elliptic curve cryptography (ECC) based on a point addition operation instead of a point multiplication operation during signing and verifying messages. In the joining phase of the proposed scheme, the vehicle requires the joining process for the broadcasting traffic-related message to others or nearby RSU within its communication range. After obtaining the pseudonym and secret key from RSU, the vehicle is considered as a registered node in VANET. This article utilizes a Burrows-Abadi-Needham (BAN) logic to evidence that the proposed scheme fulfill successfully mutual authentication. The formal security phase shows that security and privacy requirements are satisfied by the proposed scheme. The performance efficiency shows that our proposed scheme has lower overhead in terms of computation cost compared with other recent schemes since a point multiplication operations based o ECC are not used. Therefore, the computation costs of the message signing, individual-authentication and batch-authentication in our proposed scheme are decreased by 99.3%, 99.7% and 98.1%, respectively.

Several group signature or identity schemes have been proposed for addressing the issues of security in a vehicular ad hoc network (VANET). Nonetheless, none of these schemes suitably cope with the performance efficient during the signing and verifying safety-messages. Furthermore, adversaries could acquire sensitive data stored in a tamper-proof device (TPD) by utilizing side-channel attacks. An efficient conditional privacy-preserving authentication scheme is proposed for the prevention of side-channel attacks and reducing the performance efficiency of the system in this paper. Moreover, to resist side-channel attacks, critical data stored in the TPD is frequently and periodically updated. Lastly, due to our work employs the one-way hash function and the elliptic curve cryptography, its performance evaluation has lower computation and communication cost compared to other schemes.

With the emergence of one of this century’s deadliest pandemics, coronavirus disease (COVID-19) has an enormous effect globally with a quick spread worldwide. This made the World Health Organization announce it as a pandemic. COVID-19 has pushed countries to follow new behaviors such as social distancing, hand washing, and remote work and to shut down organizations, businesses, and airports. At the same time, white hats are doing their best to accommodate the pandemic. However, while white hats are protecting people, black hats are taking advantage of the situation, which creates a cybersecurity pandemic on the other hand. This paper discusses the cybersecurity issues at this period due to finding information or finding another related research that had not been discussed before. This paper presents the cybersecurity attacks during the COVID-19 epidemic time. A lot of information has been collected from the World Health Organization (WHO), trusted organizations, news sources, official governmental reports, and available research articles. This paper then classifies the cybersecurity attacks and threats at the period of COVID-19 and provides recommendations and countermeasures for each type. This paper surveys the cybersecurity attacks and their countermeasures and reports the ongoing cybersecurity attacks and threats at this period of time. Moreover, it is also a step towards analyzing the efficiency of the country’s infrastructure as well as hackers and criminals’ social behavior at the time of the pandemic.

Recently, the demand for reliable and high-speed wireless communication has rapidly increased. Orthogonal frequency division multiplexing (OFDM) is a modulation scheme that is the newest competitor against other modulation schemes used for this purpose. OFDM is mostly used for wireless data transfer, although it may also be used for cable and fiber optic connections. However, in many applications, OFDM suffers from burst errors and high bit error rates. This paper presents the utilization of a helical interleaver with OFDM systems to efficiently handle burst channel errors and allow for Bit Error Rate (BER) reduction. The paper also presents a new interleaver, FRF, the initial letters of the authors’ names, for the same purpose. This newly proposed interleaver summarizes our previous experience with many recent interleavers. Fast Fourier transform OFDM (FFT-OFDM) and Discrete Wavelet Transform OFDM (DWT-OFDM) systems are used to test the efficiency of the suggested scheme in terms of burst channel error removal and BER reduction. Finally, the general complexity of the FRF interleaver is different from that of the helical interleaver in terms of hardware requirements. The performance of the proposed scheme was studied over different channel models. The obtained simulation results show a noticeable performance improvement over the conventional FFT-OFDM and the FFT-OFDM systems with the helical interleaver. Finally, the disadvantage of the proposed FRF interleaver is that it is more complex than the helical interleaver.