The massive low earth orbit (LEO) satellite-empowered Internet of Things (MLS-IoT) network has many advantages including high capacity, low latency, large network coverage, and high reliability. However, the openness and broadcasting on satellite communication links pose huge challenges to protect the security of data delivery, especially to defend against eavesdropping. Leveraging the density of LEO satellite deployment and developing efficient secure communication schemes with the aid of physical layer security (PLS) technique deserve further exploration. To the best of our knowledge, it is an almost untouched issue to employ satellites to transmit jamming signal in MLS-IoT systems since lots of available researches in this line mainly considered cooperative jamming only on ground networks. For this purpose, we propose in this paper a PLS based secure communication enhancement scheme in MLS-IoT network. A jamming scheduling and resource allocation problem is presented for the secrecy rate maximization by dynamically switching the roles of satellites on sub-channels for transmitting private information or jamming signal. An iterative strategy is devised to jointly optimizing jamming scheduling, device association, channel assignment, and power allocation. Numerical results validate that our presented optimization approach could significantly improve the system secrecy rate and achieve the optimal jamming scheduling and resource allocation decisions.

In the new radio (NR) vehicle-to-everything (V2X), congestion control (CC) is adopted to reduce channel occupancy and improve delivery performance. However, most existing CC schemes decide to adjust the number of messages while ignoring the throughput requirements, which may lead to serious traffic accidents due to the loss of information. In light of this, we investigate the performance of cooperative awareness messages (CAMs) delivery via NR sidelink under different CC configurations, including the modulation and coding scheme, the semi-persistent scheduling-based resource allocation, as well as the CAM transmission period. Specifically, taking into account these configurations, we develop a theoretical framework to capture two metrics of CAM delivery, namely the effective throughput and the channel busy ratio, based on the stochastic geometry approach. With the help of this framework, we propose the CC-(m,n,$\tau $) scheme, which is able to optimize the two metrics simultaneously by properly tuning the parameters m, n, and$\tau $. Extensive numerical results show that the proposed scheme is highly beneficial for efficient and reliable CAM delivery via NR sidelink, particularly in scenarios with high vehicle densities.

As an essential technology for constructing green passive Internet of Things (IoT) network, backscatter communication (BackCom) enables battery-free IoT devices to deliver information by modulating and reflecting incident carriers. Nevertheless, the energy harvesting at an IoT device is constrained by its distance from the radio-frequency (RF) emitter, and the double-fading phenomenon significantly restricts the achievable performance of BackCom-based IoT network. Existing research has revealed that intelligent reflecting surface (IRS) and dynamic unmanned aerial vehicle (UAV) are promising solutions to overcome the current bottleneck, and their combined application in IoT network for BackCom remains in the preliminary exploration phase. Most studies focus more on traditional fixed RF emitters, which lack the flexibility needed for efficient energy transmission and are not suitable for infrastructure blank areas or hard-to-maintain regions. Motivated by this, an IRS-aided UAV-powered green IoT network is proposed in this paper, where the UAV acts as a mobile power beacon to energize multiple IoT nodes on the ground in a time-division multiple address manner, and an IRS is deployed in the scenario to enhance the BackCom performance. To guarantee reliable data transfer under energy harvesting constraint, we maximize the minimum average throughput among all IoT nodes for BackCom during the UAV flight duration by optimizing the node communication scheduling, power splitting coefficient, UAV trajectory and IRS phase shift. Specifically, we employ a four-stage alternating optimization method to decouple the optimization variables and solve for each variable independently. Finally, extensive experimental results verify the superior performance of our proposal in improving throughput over other benchmark schemes.

With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative viewing, traffic sensing, and metaverse entertainments in remote areas. However, the heterogeneous caching hardware, communication environments, and frequent network dynamics make the optimization of content caching policy highly complicated. Firstly, considering all LEO satellites as caching satellites can lead to content duplication and radio interference, causing storage waste and NTN transmission quality deterioration. Secondly, how to provide customized QoS by intra-layer and inter-layer cooperative caching in such complicated environments remains an open issue. Thus, we propose a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme to select caching LEO satellites with reduced system propagation delay. Then, the Multi-Agent Deep Reinforcement Learning-based Hierarchical Caching and Asynchronous Updating (MADRL-HCAU) strategy is designed to manage the caching capacity of LEO satellites and UAVs, providing customized services for CAVs and dispensing the peak traffic. Simulation results illustrate that the proposed scheme can not only effectively accelerate the caching refreshing and content downloading process but also significantly reduce the packet drop and improve the cache hit ratio.

The development of 6G should enable users in remote and harsh areas to enjoy computation-intensive services including metaverse entertainment, intelligent transportation, and immersive communications. Low Earth Orbit (LEO) satellite constellations widely constructed in recent years have been recognized as an efficient solution to complement the terrestrial infrastructure with seamless coverage and decreasing expenses for both communication and computation services. However, the widely studied Federated Reinforcement Learning (FRL) based task offloading strategies neglect the potential trust concerns like malicious satellites and buffer pollution, while 6G service providers may rent the LEO satellites belonging to different companies to minimize the expense. To address these issues, blockchain has been considered in the Zero Trust (ZT) scenario, with the group consensus mechanism through the smart contract. Moreover, we propose a Constrained Correction Voting Mechanism (CCVM) to give punishing correction to the aggregation weight of malicious voting satellites. Furthermore, a Cold Start Reputation Aggregation (CSRA) scheme is adopted to first severely degrade and then gradually recover the weight of Federated Learning (FL) sub-models trained by malicious satellites. Thus, the Blockchain-enabled Cold Start Aggregation FRL (BCSA-FRL) scheme is proposed to make effective and secure offloading decisions in the ZT LEO satellite Networks. The numerical results illustrate the advantages of our proposal.

Hybrid Automatic Repeat reQuest (HARQ) feedback and retransmission, as one of the crucial technologies for reliability enhancement, may cause a security-reliability tradeoff in New Radio (NR) Vehicle-to-Everything (V2X) communications. Specifically, for NR V2X messages, the HARQ feedback and retransmission improve its success delivery probability but endanger its confidentiality. This paper proposes a secure scheme, namely SecHARQ, to maximize the security performance of HARQ-assisted NR V2X while satisfying the reliability requirement. To describe the performance of SecHARQ accurately, a stochastic geometric based theoretical framework is developed with the consideration of three specified factors, namely transmission rounds, resource allocation, and feedback option selection. With the help of this framework, we can formulate the SecHARQ scheme as a tractable optimization problem. Extensive numerical results show that the proposed scheme is beneficial for secure communication in HARQ-assisted NR V2X by carefully tuning the optimal parameters of SecHARQ.

In the context of 5G and beyond 5G, radio access network (RAN) slicing emerges to enable differentiated services via the instantiation of virtualized logical networks. Despite its promising potential, the resource optimization of RAN slicing confronts significant challenges stemming from the scarcity of spectrum resources, the intricacies of tradeoff between slice service quality and slice security, the mobility of users, and the complexity of wireless interference in multi-cell environments. To address these challenges, we propose a heuristic-assisted multiagent deep reinforcement learning-based resource allocation scheme for RAN slicing. This scheme aims to augment inter-slice resource isolation for security (quantified as isolation rate) while efficiently accommodating diverse requirements across slices (quantified as satisfaction rate). Through extensive numerical results, we exhibit that our proposed scheme adeptly adapts to multiple user mobility patterns, achieving superior performances in terms of satisfaction rate and isolation rate.

In New Radio (NR) Vehicle-to-Everything (V2X), the congestion control is adopted to reduce channel occupancy and improve delivery performance. However, most available congestion control schemes choose to reduce the number of messages while ignoring the throughput requirements, which may lead to serious traffic accidents due to the loss of information. This paper investigates the delivery performance of Cooperative Awareness Messages (CAMs) in NR V2X and proposes an Optimizing Throughput Congestion Control (OTCC) scheme to ensure that more information is successfully exchanged between vehicles. The OTCC is able to analytically provide the optimal CAM generation frequency and maximize the effective throughput of CAM delivery with the help of a carefully derived stochastic geometry based theoretical framework. Extensive numerical results show that the OTCC scheme is highly beneficial for effective information exchange, especially in complex traffic environments with highly dense vehicles.

Network slicing is a virtualization paradigm that divides multiple isolated logical networks on the physical infrastructure to meet different service requirements. Due to the dynamic changes in service requirements, reconfiguration of slice resources is essential to ensure the performance of slicing. However, most of the existing work focuses on reconfiguring slice through the migration of virtual network functions (VNFs, the virtual nodes that constitute the slice), which leads to large reconfiguration overhead. In view of this, we concentrate on the vertical scaling of VNF and propose a dynamic slice resource reconfiguration scheme based on multi-agent deep deterministic policy gradient (MADDPG), which can flexibly adjust multidimensional slice resources, as well as reduce or avoid costly VNF migration operations. In addition, we improve the proposed scheme using incremental learning to adapt to the different number of VNFs on each physical node and accelerate the training speed. Experimental results show that our proposed scheme has significant advantages over several benchmark schemes in VNF and slice resource satisfaction ratio, and can converge quickly through incremental learning.

To further enhance the reliability and throughput of the Vehicle-to-Everything (V2X) communications via New Radio (NR) sidelink, the flexible Modulation and Coding Scheme (MCS) was approved by 3GPP in Release 16. However, most available works neglect the variations in message data size and adopt fixed MCS, which unavoidably limits the NR sidelink spectral efficiency. Using the theory of stochastic geometry, we are able to present a mathematical framework and analytically investigate the impact of MCS selection on the achievable message delivery probability in NR sidelink, following the general message data size in 3GPP. Then, the optimal MCS is selected by enlarging the surplus between the Signal to Interference-plus-Noise Ratio (SINR) measured at the receiver and the SINR Threshold Required for Successful Delivery (TRSD). The numerical results prove that the optimal MCS is extremely beneficial for the V2X message transmission.

Network slicing, as the backbone technique of evolving B5G/6G, consists of Network Function Virtualization Infrastructure (NFVI) layer, network slice instance layer, service instance layer and management and orchestration module. Given the lessons learned from recently reported nationwide and long-lasting global telecommunication disasters with severe service degradation or even outages, we find that many of them orig-inated from a simple faulty entity in the NFVI layer at the very beginning. To the best of our knowledge, as the very first attempt, we propose Zoom-inRCL, which enables us to quickly and accurately find the root cause entity at the NFVI layer once observing the slice service degradation. Specifically, it first filters abnormal NF call graphs at the graph level based on deep support vector data description (Deep SVDD) algorithm and then filters faulty entity candidates at the node level by novelly designed rules, and finally infers the suspicious entity rank list according to the ratio of affected NFs. Evaluations on a real-world dataset show that for over 80% of fault cases, the top-ranked entity identified by Zoom-inRCL is the actual root cause of the service degradation. We believe that our work can provide useful guidance for the design of future B5G/6G networks.

Hybrid Automatic Repeat reQuest (HARQ) feedback is one of the key technologies for enhancing the performance of New Radio (NR) Vehicle-to-Everything (V2X) communication. However, for NR V2X messages, HARQ-based retransmission not only improves its delivery performance, but also poses a significant threat to its confidentiality. This paper investigates the Security-Reliability Tradeoff (SRT) of HARQ-assisted NR V2X. To accurately describe the SRT behavior in the V2X scenario, a theoretical framework based on stochastic geometry is developed, carefully considering the random device distributions and physical layer settings of NR V2X. Numerical results prove that this theoretical framework can effectively capture the success probability and secrecy probability of V2X communications under different configurations, including transmission rounds, resource selection, and power control. Furthermore, our analysis shows that the SRT of HARQ-assisted NR V2X can be adjusted by finely tuning the considered configurations.

5G core network slicing is an important part of 5G end - to-end slicing, which can provide customized services by tailoring network functions for diverse application scenarios. In order to implement core network slicing efficiently and make full use of network resources, slice admission control that selectively accepts or rejects slice establishment requests is crucial. However, existing related works mainly focus on optimizing the revenue of mobile operators, and lack consideration of dynamic resource scheduling and recycling under limited resources. To this end, we propose a dynamic slice admission control mechanism with a warm-up resource recycling method, which uses the adaptability of reinforcement learning to improve the slice admission rate and ensure the efficient utilization of resources. Also, considering the differentiated demands of typical application scenarios, a slice request dataset construction rule is designed and a dataset is established to evaluate the effectiveness of the proposed mechanism. Experimental results demonstrate the superiority of the proposed core network slice admission control mechanism in guaranteeing high slice admission rate under various simulation settings.

Backscatter communication, as a key technique to achieve low-power device transmission in Internet of Things, has aroused wide concern all over the world in recent years. Since the double fading effect limits the backscatter communication range and the achievable system performance, intelligent reflecting surface (IRS) appears to be introduced into backscatter communication to raise the propagation quality. However, the signal model considered in the available researches with only a single-reflection path is not complete. Motivated by this, we first formulate the achievable rate maximization problem for IRS-enabled backscatter communication system in this work, with consideration of two-reflection characteristic at the IRS through designing the beamforming vector of carrier transmitter, the phase shift of IRS, and the power reflection coefficient of backscatter device jointly. Note that the complex superposition of two-reflection signal links makes the formulated problem highly non-convex, we utilize an iterative algorithm combined with alternating optimization approach and minorization-maximization method to derive the approximate solution to each optimization variable. Simulation and experimental results corroborate the remarkable performance improvements created by the considered signal model, as well as the excellence of our proposed scheme with joint optimization over other benchmarks.

5G and beyond are required to support a variety of emerging services that impose different requirements on the network. Radio Access Network (RAN) slicing is a promising candidate technology, and resource allocation issues related to it have received extensive attention. However, most existing literature on RAN slicing resource allocation either ignores actual network interference or focuses primarily on the QoS of slices but not slicing security. Towards this end, we propose a QoS and security-oriented slicing resource allocation scheme in a multi-cell and multi-slice scenario, where actual link interference is carefully considered. We then formulate a problem of maximizing security (i.e., isolation rate) subject to QoS (i.e., satisfaction rate) constraint. Finally, a multi-agent deep reinforcement learning-based solution is designed to solve this problem, and extensive numerical results demonstrate the superior performance of the proposed scheme.

In this paper, we introduce two double-IRS strategies to enhance proactive eavesdropping, namely Double-IRS Enhancing Strategy (DES) and Double-IRS Enhancing-Jamming Strategy (DEJS). In the former strategy, both cooperative IRSs reflect the incident signal passively. In the latter strategy, one IRS performs passive reflection while the other IRS acts as a passive jammer to modulate the incident signal into the jamming signal to deteriorate the suspicious link. Based on these two strategies, the semidefinite relaxation (SDR) technique and the bisection search method are applied to optimize the reflection coefficients at two IRSs, aiming at maximizing the effective eavesdropping rate. Finally, numerical evaluations are conducted to verify the effectiveness of our proposed schemes.

Sidelink relay enhancement, as a promising item in 3rd Generation Partnership Project (3GPP) R18, can support further vertical domain expansion of New Radio (NR). However, existing research on performance analysis of NR sidelink relay, either considered simple cases of single lane model or partially (if not totally) ignored the important factors of NR sidelink, such as resource allocation, power control, and coexistence with the cellular network. In this paper, a general stochastic geometry theoretical framework for performance analysis is developed to evaluate the success probability, time cost, and transmission distance of NR sidelink multi-hop relay in a typical unicast scenario, closely following the 3GPP specifications in both physical layer characteristics and parameters. As verified by extensive experiments, the considered performance metrics under different parameter settings can be accurately described through this theoretical framework, which holds great prospects for NR sidelink network design and deployment.

Network virtualization technology is envisioned as the new paradigm for modern Internet by virtue of its flexible management and allocation of physical resources, as well as fast provisioning of customized network services. Virtual network embedding (VNE), one of the main issues faced by network virtualization, has attracted interests of numerous researches due to its importance and proven NP-hardness. However, existing works addressing this issue have limitations such as inadequate generality, heavily relying on hand-craft features, inefficient to the changeable action space of the VNE problem, etc. Towards this end, we propose a VNE scheme in this paper with a new environment interpretation mechanism and a duel network based decision making architecture, which has the automatically feature extraction ability for both physical and virtual networks, and the capability of adapting to the VNE environment with changeable action space. Comparison results with existing works demonstrate the superiority of our proposal, which can bring higher acceptance ratio and larger average revenue on both synthetic and real physical networks.

Benefiting from New Radio (NR) sidelink based Vehicle-to-Everything (V2X) communication, Vulnerable Road Users Protection (VRUP) can be further developed in intelligent transport systems. However, considering limited resources in the V2X network, the interference caused by resource collisions may restrict this development. This paper propose a spatial reuse scheme of frequency resources, namely the Per-Zone Resource Reuse (PZRR) scheme, for NR sidelink based VRUP enhancement. To efficiently characterize the VRUP behavior in the PZRR scheme, a stochastic geometry based theoretical framework is provided by carefully taking into account interference from multiple zones. With the help of this framework, the delivery probability of the VRUP message can be accurately described under various parameter settings. Furthermore, numerical results show that the VRUP message delivery probability can be significantly improved by properly tuning parameter settings.

To meet the future demands for ubiquitous communication coverage and temporary / unexpected computing resources, aerial computing networks have been envisioned as a new paradigm. Nevertheless, dynamic changes on the network make it particularly challenging to achieve global optimal resource allocation. As an emerging technology, digital twin (DT) can represent real objects in physical network by creating virtual models. With the help of DT, we can easily obtain comprehensive real-world high-fidelity state information for model training, so as to achieve intelligent efficient decision-making. Accordingly, DT-based aerial computing networks have emerged as a potential solution. Note that available researches mostly assumed simple ground user distribution like uniform distribution, and adopted binary / partial offloading in task processing, neglecting the task separability and data inter-dependency among subtasks. Toward this end, we introduce DT into aerial computing networks, and study the problem of intelligent UAV deployment and resource allocation. Specifically, we firstly propose a DT-assisted UAV deployment strategy and model the data inter-dependency among subtasks. After that, two DT-assisted hybrid (binary and partial) task offloading schemes are presented, i.e., heuristic greedy and DQN-based schemes. Extensive analysis and numerical results confirm the effectiveness of our proposed DT-assisted UAV deployment and hybrid task offloading strategies.

With the fast development of emerging Internet of Everything applications, human-type communications (HTC) and machine-type communications (MTC) will inevitably coexist in future 6G cellular networks. To support massive connectivity while fulfilling diverse requirements of both HTC and MTC, we present a device-to-device aided rate splitting multiple access (RSMA) scheme by encoding both the MTC devices’ messages and HTC users’ common messages into a general common data stream in each cell (or group). Nevertheless, such deploying strategy may bring challenges in complex resource allocation and transmission modes selection. In view of this, we introduce a simple received signal strength (RSS) based transmission modes selection scheme, through which the RSS-threshold selection problem is formulated to maximize the HTC and MTC sum rates. Considering the computational complexity and scalability, we employ a multi-agent reinforcement learning based algorithm for each small base station to choose the optimal RSS threshold thus to achieve maximum sum rate while maintaining massive connectivity. Simulation results reveal our proposed RSMA deploying strategy outperforms non-orthogonal multiple access (NOMA) in system coverage while maintaining high-level system rate. Besides, the proposed MARL based scheme can further improve the system sum rate and coverage for both the HTC and MTC.

Being capable of controlling the radio environment well, the contemporary advanced technique, namely reconfigurable intelligent surface (RIS), has been expected to facilitate unmanned aerial vehicle (UAV) communications. The existing studies mainly focus on terrestrial RIS to realize communication performance improvement, which can achieve only half-space reflection and may experience serious signal attenuation brought by several reflections. Motivated by this reason, this paper proposes to install multiple aerial RISs on the balloons to cooperatively boost the received signal power at ground users. Based on this architecture, we jointly optimize UAV trajectory, active beamforming, and passive beamforming, so as to maximize the average system sum rate. Considering that the composite channel gain becomes a complex function of UAV trajectory in the presence of multiple RISs, the block coordinate descent method is developed to address the formulated optimization problem. The mathematical treatment involves three stages, which can be executed by an iterative algorithm. Finally, numerical results verify the competitive superiorities of multiple aerial RISs and the excellence of joint optimization design compared with other benchmark ones.

Unmanned aerial vehicles (UAVs) have shown great potential in benefiting industries due to their good features, such as the ease of deployment and low maintenance cost. However, the communication security issue remains a serious challenge before the large-scale application of industrial UAVs. The untrusted communication environment can cause the leakage of valuable industrial data or the losing of important cargos that carried by UAVs. Traditional authentication mechanisms for protecting communications include public-key infrastructure-based, ID-based, and certificateless authentication. These mechanisms rely on a central authority and some of them may introduce high-complexity computation that is not suitable for industrial drones. Therefore, aiming at these challenges, we design a blockchain-assisted distributed and lightweight authentication service for industrial UAVs. The blockchain technology supports the distributed and immutable storage of industrial UAVs’ authentication information, and smart contracts enable convenient operations for drones to acquire or update the corresponding information. Security evaluation demonstrates that our scheme is resistant to various attacks and can guarantee trustworthy communications for industrial drones. Extensive experiments also show that our designed authentication service can not only achieve low computation and communication cost for industrial UAVs but also remain robust even if a small proportion of drones are compromised.

Internet of Vehicles (IoV) is an emerging technology in automotive field, in which vehicles can communicate with other vehicles and roadside infrastructures to improve information acquisition ability as well as obtain various services to elevate the security and comfort level. To cope with the increasingly complex vehicular network, software-defined networking (SDN) architecture with advantages of centralized management and flexible control becomes a promising solution. However, in application scenarios, the security of SDN is rarely concerned. If attackers exploit the vulnerabilities of SDN to hijack the network location of the servers or vehicles, vehicles may not be able to access the services they need timely and effectively, which will pose a great threat to the benefit of vehicle users. In light of this, we focus on location hijacking attack against SDN in vehicular network. We perform this attack on five mainstream SDN controller platforms and analyse its impacts from multiple perspectives. As far as we know, this is the first study of such attack in vehicular network. Furthermore, using the advantages of the software-defined space–air–ground-integrated vehicular network and the characteristics of high altitude platform (HAP), such as wide coverage and high load capacity, we put forward the attack recovery scheme based on deep$Q$-learning (DQL) to supplement existing defence mechanisms that always have counter attacks and endow the vehicular network with a certain resilience.

Software-defined networking (SDN) has become an attractive solution to carry out centralized and efficient control in Industrial Internet of Things (IIoT). However, its security has received little attention when applied to IIoT, and no comprehensive consideration has been given to attacks against forwarding nodes (FNs), the basic elements of the data plane. Therefore, in this article, we aim to investigate attacks against FNs from multiple perspectives in software-defined IIoT. To the best of our knowledge, we are the first to systematically consider this kind of attacks. Since it is difficult to predeploy all defense methods against various attacks, we propose a deep reinforcement learning (DRL)-based general attack tolerance scheme to guide the benign traffic flow bypass the attacked FNs. Furthermore, in view of the situation that the real data set is rare and the standard model-based data set is likely to be impractical, we use generative adversarial network (GAN), a representative deep generative model (DGM), to flexibly generate real-like network traffic for more sufficient and effective experimental verification on the attack tolerance scheme. Experimental results show that our proposed scheme can significantly improve the successful arrival rate of IIoT traffic and achieve near-optimal results.

The development of software-defined industrial networks (SDIN) promotes the programmability and customizability of the industrial networks and is suitable to cope with the challenges brought by new manufacturing modes. For building more scalable and reliable SDIN, a distributed control plane with multicontroller collaboration becomes a promising option. However, as the brain of SDIN, the security of the distributed control plane is rarely considered. In addition to suffering direct attacks, each controller is also subjected to attacks propagated by other controllers because of information sharing or management domain takeover, resulting in the spread of attacks in a wider range than a single controller. Therefore, in this article, we study attacks against SDIN with distributed control plane, demonstrate their propagation across multiple controllers, and analyze their impacts. To the best of our knowledge, we are the first to study the security of SDIN with distributed control plane. In addition, since the existing defense mechanisms are not specifically designed for distributed SDIN and cannot defend it perfectly, we propose an attack mitigation scheme based on deep reinforcement learning to adaptively prevent the spread of attacks. Specifically, the novelty of our scheme lies in its ability of learning from the environment and flexibly adjusting the switch takeover decisions to isolate the attack source, so as to tolerate attacks and enhance the resilience of SDIN.

The development of vehicular networks spawns various service scenarios, whether safety-related or infotainment-related, making people's life and travel more comfortable and efficient. As an innovative network architecture to realize centralized control, Software-Defined Networking (SDN) is very beneficial to the management of complex vehicular networks. Nevertheless, its security has received little attention. If the core SDN controller is threatened, the entire vehicular network can be seriously affected. To this end, we focus on the vulnerability of SDN controller, successfully perform topology poisoning attacks on four mainstream controllers, analyze the attack impacts hierarchically, and discuss the countermeasures for security improvement. As far as we know, we are the first to explore the security of SDN controller in vehicular networks.

Map information is of crucial importance to ensure the safety and reliability of vehicle, no matter indoor or outdoor, it should reflect the real-time changes of environment. Existing indoor map update mechanisms have several common limitations such as small update range, long cycle, large amount of update data, high cost and poor currency. Therefore, we present a multi-vehicle collaborative indoor map update scheme based on edge-cloud architecture to realize real-time autonomous map updating. This scheme can be achieved through continuous monitoring, tagging, identification, and layering of the environment during driving process. Compared with traditional map update schemes, experimental results show that our scheme can effectively realize the collaborative map update in indoor environment, enhance the map update efficiency, reduce the update delay, and improve the adaptability of vehicles.

With the development of Internet and communication technologies, various information can easily spread to children via applications (Apps) on Internet-of-Things (IoT) devices (e.g., emerging smart toys, watches, and phones), especially, the Apps on smart phones based on Android. While greatly bringing up convenience for children's lives and studies, these Apps also make illegal and inappropriate contents (such as violence, pornography, gambling, and drug) more accessible to kids, which is harmful to minors' growth. To keep children away from inappropriate contents in applications, previous researches mainly focused on detecting unsuitable videos and advertisements in children applications or designing App maturity rating methods and parental control software. There are few literature that specially investigate the inspection of inappropriate contents in children Android Apps. Toward this end, we propose a novel automatic content inspection and the forensics framework to identify children Android Apps which are not proper for kids under 12. In addition, this framework offers evidence to make users understand why the inspected App is judged as unsuitable. In experiments, we apply this framework on some specially chosen Android Apps which distinctly include inappropriate contents to verify its performance. The results show that it can successfully identify those applications with high precision that reaches 85.7%. Besides, by analyzing the collected children's Android Apps through our framework, we find that 40% of them are identified to be improper, which illustrates the serious issue of unsuitable children Android Apps.

The development of the Internet of Vehicles (IoV) has made people's lives and travels safer, more efficient, and more comfortable. The combination of edge computing and IoV can provide processing and storage capabilities close to vehicles, thus becoming a potential paradigm. At this time, the software-defined networking (SDN) architecture is extremely necessary to realize centralized control and convenient management for complex and dynamic vehicular edge networks. However, as the brain of the SDN architecture, little attention has been paid to the security of the SDN controller. Once the controller is threatened, severe global chaos may happen. Therefore, in this article, we study the attack against the SDN controller, which is the topology poisoning attack. We successfully implement this attack in four mainstream controllers and analyze its impact from multiple levels. To the best of our knowledge, we are the first to study this attack in the vehicular edge network. In addition, in view of the counter-attacks of the existing defence mechanisms, we propose an attack-tolerance scheme based on deep reinforcement learning (DRL) to enhance the vehicular edge network with a certain degree of self-recovery.