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Public Acceptance is a prerequisite forgaining the intended benefits of Connected and Au-tonomous Vehicles (CAVs). This paper proposes anidea of digital labels for CAVs so as to increaseconsumers trust and awareness regarding the se-curity level of their vehicle. We come up with anarchitecture named Cybersecurity Box (CSBox) inwhich digital labels display and inform the CAVconsumers and passengers about its cybersecurityhealth status. We also highlight requirements andchallenges in developing digital security labels forCAVs

The booming Internet of Things makes smart healthcare a reality, while cloud-based medical storage systems solve the problems of large-scale storage and real-time access of medical data. The integrity of medical data outsourced in cloud-based medical storage systems has become crucial since only complete data can make a correct diagnosis, and public auditing protocol is a key technique to solve this problem. To guarantee the integrity of medical data and reduce the burden of the data owner, we propose an efficient privacy-preserving public auditing protocol for the cloud-based medical storage systems, which supports the functions of batch auditing and dynamic update of data. Detailed security analysis shows that our protocol is secure under the defined security model. In addition, we have conducted extensive performance evaluations, and the results indicate that our protocol not only remarkably reduces the computational costs of both the data owner and the third-party auditor (TPA), but also significantly improves the communication efficiency between the TPA and the cloud server. Specifically, compared with other related work, the computational cost of the TPA in our protocol is negligible and the data owner saves more than 2/3 of computational cost. In addition, as the number of challenged blocks increases, our protocol saves nearly 90% of communication overhead between the TPA and the cloud server.

Software-defined networking (SDN) plays a crucial role in the enterprise and wide-area networking. The increasing demand for strict service-level agreement applications on the Internet requires networks to be scalable and resilient in the face of link and switch failure. However, there is a lack of systematic reviews on SDN data plane failure recovery techniques. This review article assesses SDN current state-of-the-art link and switches failure recovery solutions. We cover the root causes of failures in the traditional core network and their detection and classify the current failure recovery techniques for SDN into two categories: traditional and artificial intelligence (AI) approaches. AI-based techniques enable efficient failure recovery and enhance the quality of service. We also consider performance measure metrics to evaluate and determine the limitations of existing solutions. This study reviews 188 papers from 2010 to 2021, selecting 70 articles that are highly relevant to our work. All articles are written in English. Our research aims to collect a large amount of evidence that will assist the industry and academic researchers in net- working to address current research gaps in failure recovery solutions for the SDN data plane.

Software defined networking (SDN) is an emerging architecture used in many applications because of its flexible architecture. It is expected to become an essential enabler for the Internet of Things (IoTs). It decouples the control plane from the data plane, and the controller manages the whole underlying network. SDN has been used in wireless sensor networks (WSNs) for routing. The SDN controller uses some algorithms to calculate the routing path; however, none of these algorithms have enough ability to obtain the optimized routing path. Therefore, reinforcement learning (RL) is a helpful technique to select the best routing path. In this paper, we optimize the routing path of SDWSN through RL. A reward function is proposed that includes all required metrics regarding energy efficiency and network quality-of-service (QoS). The agent gets the reward and takes the next action based on the reward received, while the SDWSN controller improves the routing path based on the previous experience. However, the whole network is also controlled remotely through the Web. The performance of the RL-based SDWSN is compared with SDN-based techniques, including Traditional SDN and Energy-Aware Software Defined Networking (EASDN), QR-SDN, TIDE and non SDN-based techniques, such as Q-learning and RL-based Routing (RLBR). The proposed RL-based SDWSN outperforms in terms of lifetime from 8% to 33% and packet delivery ratio (PDR) from 2% to 24%. It is envisioned that this work will help the engineers for achieving the desired WSN performance through efficient routing.

Security and efficiency are crucial considerations for Long Term Evolution for Railway (LTE-R) which bears real-time transmission of train control information for future high-speed railway. In this article, we first analyze the vulnerabilities of LTE-R access authentication protocol, and then propose a proxy signature based authentication scheme to enhance the security of LTE-R without sacrificing efficiency. The proposed scheme consists of three main security mechanisms: a novel Elliptic Curve Cryptosystem based Certificateless Proxy Signature (ECC-CLPS) designed for authentication security, a hash-based puzzle introduced to defend against denial of service (DoS) attack and a key pre-gxeneration mechanism used to improve the efficiency of fast handover authentication. The security analysis and performance simulation show that our scheme has advantages over existing LTE-based authentication schemes in terms of security, functionality, computation and communication costs. Moreover, the authentication requirements for security and efficiency in different LTE-R communication scenarios are fully considered, which makes our scheme more suitable for the access authentication in the future LTE-R based high-speed railway.

Internet of Things (IoT) enables terminal devices connecting with the internet and provides various intelligent applications by analyzing devices data. As a typical IoT technique, edge computing provides a three-tier architecture to reduce communications and improve efficiency. Specifically, edge nodes are responsible for collecting and aggregating device data, and then send processed results to the cloud for subsequent analysis. However, the data aggregation function will compromise the privacy of device data. In this paper, we proposed an efficient privacy-preserving multi-dimensional data aggregation scheme for IoT, called PMDA. The scheme uses the Chinese remainder theorem to design a homomorphic encryption method that encryptes a multiple-dimensional small integer vector into one ciphertext and keeps linear homomorphic properties per dimension. Combining with the signature mechanism and the batch verification method, the scheme guarantees non-repudiation of device data and enhance verification efficiency at edge nodes. Through theoretical analysis, we demonstrate that the proposed scheme can achieve correctness, privacy, authentication, and integrity. After performance evaluation, we demonstrate that our scheme is superior to other schemes in terms of computation and communication costs. In particular, as the message dimension increases, our scheme computation costs almost a tenth of others at the 80-bits security level.