Biblio

Skyrmion racetrack memory (Sk-RM) is a new storage technology in which skyrmions are used to represent data bits to provide high storage density. During the reading procedure, the skyrmion is driven by a current and sensed by a fixed read head. However, synchronization errors may happen if the skyrmion does not pass the read head on time. In this paper, a polar coding scheme is proposed to correct the synchronization errors in the Sk-RM. Firstly, we build two error correction models for the reading operation of Sk-RM. By connecting polar codes with the marker codes, the number of deletion errors can be determined. We also redesign the decoding algorithm to recover the information bits from the readout sequence, where a tighter bound of the segmented deletion errors is derived and a novel parity check strategy is designed for better decoding performance. Simulation results show that the proposed coding scheme can efficiently improve the decoding performance.

Recently, vehicular communications have been the focus of industry, research and development fields. There are many benefits of vehicular communications. It improves traffic management and put derivers in better control of their vehicles. Privacy and security protection are collective accountability in which all parties need to actively engage and collaborate to afford safe and secure communication environments. The primary objective of this paper is to exploit the RSS characteristic of physical layer, in order to generate a secret key that can securely be exchanged between legitimated communication vehicles. In this paper, secret key extraction from wireless channel will be the main focus of the countermeasures against VANET security attacks. The technique produces a high rate of bits stream while drop less amount of information. Information reconciliation is then used to remove dissimilarity of two initially extracted keys, to increase the uncertainty associated to the extracted bits. Five values are defined as quantization thresholds for the captured probes. These values are derived statistically, adaptively and randomly according to the readings obtained from the received signal strength.

As we all know, network coding can significantly improve the throughput and reliability of wireless networks. However, in the high-speed mobile environment, the packet loss rate of different wireless links may vary greatly due to the time-varying network state, which makes the adjustment of network coding redundancy very important. Because the network coding redundancy is too large, it will lead to excessive overhead and reduce the effective throughput. If the network coding redundancy is too small, it will lead to insufficient decoding, which will also reduce the effective throughput. In the design of multi-path transmission scheduling scheme, we introduce adaptive redundancy network coding scheme. By using multiple links to aggregate network bandwidth, we choose appropriate different coding redundancy for different links to resist the performance loss caused by link packet loss. The simulation results show that when the link packet loss rate is greatly different, the mechanism can not only ensure the transmission reliability, but also greatly reduce the total network redundancy to improve the network throughput very effectively.

This note addresses the problem of distributed control for a class of nonlinear multi-agent systems over a communication graph. In many real practical systems, owing to communication limits and the vulnerability of communication networks to be overheard and modified by the adversary, consideration of communication delays and cyber-attacks in designing of the controller is important. To consider these challenges, in the presented approach, a distributed controller for a group of one-link flexible joint manipulators is provided which are connected via data delaying communication network in the presence of cyber-attacks. Sufficient conditions are provided to guarantee that the closed-loop system is stable with prescribed disturbance attenuation, and the parameter of the control law can be obtained by solving a set of linear matrix inequities (LMIs). Eventually, simulations results of four single-link manipulators are provided to demonstrate the performance of the introduced method.

Cyber ranges are valuable assets but have limitations in simulating complex realities and multi-sector dependencies; to address this, federated cyber ranges are emerging. This work presents the ECHO Federated Cyber Range, a marketplace for cyber range services, that establishes a mechanism by which independent cyber range capabilities can be interconnected and accessed via a convenient portal. This allows for more complex and complete emulations, spanning potentially multiple sectors and complex exercises. Moreover, it supports a semi-automated approach for processing and deploying service requests to assist customers and providers interfacing with the marketplace. Its features and architecture are described in detail, along with the design, validation and deployment of a training scenario.

To improve the safety precaution level of network system, a combined network intrusion detection method is proposed based on Supervised Locality Preserving Projections (SLPP) feature extraction and Extreme Learning Machine (ELM). In this method, the feature extraction capability of SLPP is first used to reduce the dimensionality of the original network connection and system audit data, and get a feature set, then, based on this, the advantages of ELM in pattern recognition is adopted to build a network intrusion detection model for detecting and determining intrusion behavior. Simulation results show that, under the same experiment conditions, compared with traditional neural networks and support vector machines, the proposed method has more advantages in training efficiency and generalization performance.

Nowadays, source location privacy in underwater acoustic sensor networks (UASNs) has gained a lot of attention. The aim of source location privacy is to use specific technologies to protect the location of the source from being compromised. Among the many technologies available are fake packet technology, multi-path routing technology and so on. The fake packet technology uses a certain amount of fake packets to mask the transmission of the source packet, affecting the adversary's efficiency of hop-by-hop backtracking to the source. However, during the operation of the fake packet technology, the fake packet, and the source packet may interfere with each other. Focus on this, a fake packet time slot assignment-based source location privacy protection (FPTSA-SLP) scheme. The time slot assignment is adopted to avoid interference with the source packet. Also, a relay node selection method based on the handshake is further proposed to increase the diversity of the routing path to confuse the adversary. Compared with the comparison algorithm, the simulation results demonstrate that the proposed scheme has a better performance in safety time.

In recent years, with the rapid update of wireless communication technologies such as 5G and the Internet of Things, as well as the explosive growth of wireless intelligent devices, people's demand for radio spectrum resources is increasing, which leads spectrum scarcity is becoming more serious. To address the scarcity of spectrum, the Internet of Things based on cognitive radio (CR-IoT) has become an effective technique to enable IoT devices to reuse the spectrum that has been fully utilized. The frequency band information is transmitted through wireless communication in the CR-IoT network, so the node is easily to be eavesdropped or tampered with by attackers in the process of transmitting data, which leads to information leakage and wrong perception results. To deal with the security problem of channel data transmission, this paper proposes a chaotic compressed spectrum sensing algorithm. In this algorithm, the chaotic parameter package is utilized to generate the measurement matrix, which makes good use of the sensitivity of the initial value of chaotic system to improve the transmission security. And the introduction of the semi-tensor theory significantly reduces the dimension of the matrix that the secondary user needs to store. In addition, the semi-tensor compressed sensing is used in the fusion center for parallel reconstruction process, which effectively reduces the sensing time delay. The simulation results show that the chaotic compressed spectrum sensing algorithm can achieve faster, high-quality, and low-energy channel energy transmission.

The data transmitted via the network may be vulnerable to cyber attacks in networked inverted pendulum system (NIPS), how to detect cyber attacks is a challenging issue. To solve this problem, this paper investigates a honeypot-based attack detection method for NIPS. Firstly, honeypot for NIPS attack detection (namely NipsPot) is constructed by deceptive environment module of a virtual closed-loop control system, and the stealthiness of typical covert attacks is analysed. Secondly, attack data is collected by NipsPot, which is used to train supported vector machine (SVM) model for attack detection. Finally, simulation results demonstrate that NipsPot-based attack detector can achieve the accuracy rate of 99.78%, the precision rate of 98.75%, and the recall rate of 100%.

Adaptive feedback cancellation (AFC) method is widely adopted for the purpose of reducing the adverse effects of acoustic feedback on the sound reinforcement systems. However, since the existence of forward path results in the correlation between the source signal and the feedback signal, the source signal is mistakenly considered as the feedback signal to be eliminated by adaptive filter when it is colored, which leads to a inaccurate prediction of the acoustic feedback signal. In order to solve this problem, prediction error method is introduced in this paper to remove the correlation between the source signal and the feedback signal. Aiming at the dilemma of Modified Least Mean Square (MLMS) algorithm in choosing between prediction speed and prediction accuracy, an improved MLMS algorithm with a variable step-size scheme is proposed. Simulation examples are applied to show that the proposed algorithm can obtain more accurate prediction of acoustic feedback signal in a shorter time than the MLMS algorithm.

Internet of Things (IoT) revolutionizes cutting-edge technologies by enabling smart sensing, and actuation of the physical world. IoT enables cooperation between numerous heterogeneous smart devices to exchange and aggregate data from the surrounding environment through the internet. Recently, the range of IoT technology could be utilized in the real world by the rapid spread of sensor devices. These capabilities open the door for vital challenges. Security is the major challenge that faces the IoT networks. Traditional solutions cannot tackle smart and powerful attackers. Moving Target Defense (MTD) deploys mechanisms and strategies that increase attackers' uncertainty and frustrate their attempt to eavesdrop the target to be protected. In addition, Steganography is the practice of concealing a message within another message. For security proposes, Steganography is used to hide significant data within any transmitted messages, such as images, videos, and text files. This paper presents Stegano-MTD framework that enables combination between MTD mechanisms with steganography. This combination offers a lightweight solution that can be implemented on the IoT network. Stegano-MTD slices the message into small labeled chunks and sends them randomly through the network's nodes. Steganography is used for hide the key file that used to reconstruct the original data. Simulation results show the effectiveness of the presented solution.

Acoustic tomography experiments for ocean observation require low-frequency, broadband, high power, small size underwater acoustic transducer, but there are contradictions between the performance of the transducer, therefore a longitudinal-bending fluid-cavity coupled broadband underwater acoustic transducer is presented. The difference between the transducer and the traditional JH transducer is that the opening position of the Helmholtz resonant cavity is arranged between the radiation cover plate and the cylindrical cavity. Based on the optimization results of the finite element software ANSYS produced a transducer test prototype. The test results show that the simulation results and experimental results are basically consistent, and the transmitting voltage response can reach 136dB, the transmitting voltage response fluctuation shall no more than 6dB through the range of 700-1200Hz in the horizontal direction, verified the longitudinal-bending mode and the fluid-cavity mode of the transducer are well coupled, and the transducer is an ideal low-frequency, broadband, high power, small size underwater acoustic transducer.

The randomness, ambiguity and some other uncertainties of trust relationships in Wireless Sensor Networks (WSNs) make existing trust management methods often unsatisfactory in terms of accuracy. This paper proposes a trust evaluation method based on cloud model for malicious node detection. The conversion between qualitative and quantitative sensor node trust degree is achieved. Firstly, nodes cooperate with each other to establish a standard cloud template for malicious nodes and a standard cloud template for normal nodes, so that malicious nodes have a qualitative description to be either malicious or normal. Secondly, the trust cloud template obtained during the interactions is matched against the previous standard templates to achieve the detection of malicious nodes. Simulation results demonstrate that the proposed method greatly improves the accuracy of malicious nodes detection.

The model-free adaptive security tracking control (MFASTC) problem of nonlinear networked control systems is explored in this paper with DoS attacks and delays consideration. In order to alleviate the impact of DoS attack and RTT delays on NCSs performance, an attack compensation mechanism and a networked predictive-based delay compensation mechanism are designed, respectively. The data-based designed method need not the dynamic and structure of the system, The MFASTC algorithm is proposed to ensure the output tracking error being bounded in the mean-square sense. Finally, an example is given to illustrate the effectiveness of the new algorithm by a comparison.

The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.

Adhoc On-demand Distance Vector (AODV) is the well-known reactive routing protocol of Mobile Adhoc Network (MANET). Absence of security mechanism in AODV disturbs the routing because of misbehavior of attack and hence, degrades MANET's performance. Secure and efficient routing is a need of various commercial and non-commercial applications of MANET including military and war, disaster and earthquake, and riot control. This paper presents a design of important network layer attacks include black-hole (BH), gray-hole (GH) and worm-hole (WH) attacks. The performance analysis of AODV protocol is carried out under the influence of each designed attack by using the network simulator, NetSim. Simulation results show that, the network layer attacks affect packet delivery ability of AODV protocol with low energy consumption and in short time. Design of attacks helps to understand attack's behavior and hence, to develop security mechanism in AODV.

In this paper, we propose a deep learning based detection for multiple input multiple output (MIMO) physical-layer network coding (DeepPNC) over two way relay channels (TWRC). In MIMO-PNC, the relay node receives the signals superimposed from the two end nodes. The relay node aims to obtain the network-coded (NC) form of the two end nodes' signals. By training suitable deep neural networks (DNNs) with a limited set of training samples. DeepPNC can extract the NC symbols from the superimposed signals received while the output of each layer in DNNs converges. Compared with the traditional detection algorithms, DeepPNC has higher mapping accuracy and does not require channel information. The simulation results show that the DNNs based DeepPNC can achieve significant gain over the DeepNC scheme and the other traditional schemes, especially when the channel matrix changes unexpectedly.

We consider that in order to improve the utilization rate of spectrum resources and the security rate of unmanned aerial vehicle (UAV) Communication system, a secure transmission scheme of UAV relay assisted cognitive radio network (CRN) is proposed. In the presence of primary users and eavesdroppers, the UAV acts as the decoding and forwarding mobile relay to assist the secure transmission from the source node to the legitimate destination node. This paper optimizes the flight trajectory and transmission power of the UAV relay to maximize the security rate. Since the design problem is nonconvex, the original problem is approximated to a convex constraint by constructing a surrogate function with nonconvex constraints, and an iterative algorithm based on continuous convex approximation is used to solve the problem. The simulation results show that the algorithm can effectively improve the average security rate of the secondary system and successfully optimize the UAV trajectory.

Non-orthogonal multiple access (NOMA) is emerging as a popular radio access technique to serve multiple users under the same resource block to improve spectral efficiency in 5G and 6G communication. But the resource sharing in NOMA causes concerns on data security. Since power domain NOMA exploits the difference in channel properties for bandwidth-efficient communication, it is feasible to ensure data confidentiality in NOMA communication through physical layer security techniques. In this work, we propose to ensure resistance against internal eavesdropping in NOMA communication through a secret key derived from channel randomness. A unique secret key is derived from the channel of each NOMA user; which is used to randomize the data of the respective user before superposition coding (SC) to prevent internal eavesdropping. The simulation results show that the proposed system provides very good security against internal eavesdropping in NOMA.

This paper investigates the optimization of physical layer security in multiple-input multiple-output (MIMO) enabled visible light communication (VLC) networks. In the considered model, one transmitter equipped with light-emitting diodes (LEDs) intends to send confidential messages to legitimate users while one eavesdropper attempts to eavesdrop on the communication between the transmitter and legitimate users. This security problem is formulated as an optimization problem whose goal is to minimize the sum mean-square-error (MSE) of all legitimate users while meeting the MSE requirement of the eavesdropper thus ensuring the security. To solve this problem, the original optimization problem is first transformed to a convex problem using successive convex approximation. An iterative algorithm with low complexity is proposed to solve this optimization problem. Simulation results show that the proposed algorithm can reduce the sum MSE of legitimate users by up to 40% compared to a conventional zero forcing scheme.

The security of the power grid is the first element of its operation. This paper aims at finding the vulnerability nodes in the power grid to prevent it from being destroyed. A novel comprehensive vulnerability index is proposed to the singleness of evaluation indicators for existing literature by integrating the power grid's topology information and operating state. Taking IEEE-118 as an example, the simulation analysis proves that the proposed vulnerability index has certain discriminative advantages and the best weighting factor is obtained through correlation analysis.

The ongoing expansion of underwater Internet of Things techniques promote diverse categories of maritime intelligent systems, e.g., Underwater Acoustic Sensor Networks (UASNs), Underwater Wireless Networks (UWNs), especially multiple Autonomous Underwater Vehicle (AUV) based UWNs have produced many civil and military applications. To enhance the network management and scalability, in this paper, the technique of Software-Defined Networking (SDN) technique is introduced, leading to the paradigm of Software-Defined multi-AUV-based UWNs (SD-UWNs). With SD-UWNs, the network architecture is divided into three functional layers: data layer, control layer, and application layer, and the network administration is re-defined by a framework of software-defined beacon. To manage the network, a control model based on artificial potential field and network topology theory is constructed. On account of the efficient data sharing ability of SD-UWNs, a proactive alarming-enabled path planning scheme is proposed, wherein all potential categories of obstacle avoidance scenes are taken into account. Evaluation results indicate that the proposed SD-UWN is more efficient in scheduling the cooperative network function than the traditional approaches and can secure exact path planning.

Smart grids have to protect meter measurements against false data injection attacks. By modifying the meter measurements, the attacker misleads the control decisions of the control center, which results in physical damages of power systems. In this paper, we propose a reinforcement learning based vulnerability analysis scheme for data injection attack without relying on the power system topology. This scheme enables the attacker to choose the data injection attack vector based on the meter measurements, the power system status, the previous injected errors and the number of meters to compromise. By combining deep reinforcement learning with prioritized experience replay, the proposed scheme more frequently replays the successful vulnerability detection experiences while bypassing the bad data detection, which is able to accelerate the learning speed. Simulation results based on the IEEE 14 bus system show that this scheme increases the probability of successful vulnerability detection and reduce the number of meters to compromise compared with the benchmark scheme.

Cognitive radio (CR) as a key technology of solving the problem of low spectrum utilization has attracted wide attention in recent years. However, due to the open nature of the radio, the communication links can be eavesdropped by illegal user, resulting to severe security threat. Unmanned aerial vehicle (UAV) equipped with signal sensing and data transmission module, can access to the unoccupied channel to improve network security performance by transmitting artificial noise (AN) in CR networks. In this paper, we propose a resource allocation scheme for UAV-assisted overlay CR network. Based on the result of spectrum sensing, the UAV decides to play the role of jammer or secondary transmitter. The power splitting ratio for transmitting secondary signal and AN is introduced to allocate the UAV's transmission power. Particularly, we jointly optimize the spectrum sensing time, the power splitting ratio and the hovering position of the UAV to maximize the total secrecy rate of primary and secondary users. The optimization problem is highly intractable, and we adopt an adaptive inertia coefficient particle swarm optimization (A-PSO) algorithm to solve this problem. Simulation results show that the proposed scheme can significantly improve the total secrecy rate in CR network.

Device-to-Device (D2D) communications play a key role in the mobile communication networks. In spite of its benefits, new system architecture expose the D2D communications to unique security threats. Due to D2D users share the same licensed spectrum resources with the cellular users, both the cellular user and D2D receiver can eavesdrop each other's critical information. Thus, to maximize the secrecy rate from the perspective of physical layer security, the letter proposed a optimal power allocation scheme and subsequently to optimization problem of resource allocation is systematically investigated. The efficacy of the proposed scheme is assessed numerically.