Biblio

In covert communication systems, covert messages can be transmitted without being noticed by the monitors or adversaries. Therefore, the covert communication technology has emerged as a novel method for network authentication, copyright protection, and the evidence of cybercrimes. However, how to design the covert communication in the physical layer of wireless networks and how to improve the channel capacity for the covert communication systems are very challenging. In this paper, we propose a wireless covert communication system, where data streams from the antennas of the transmitter are coded according to a code book to transmit covert and public messages. We adopt a modulation scheme, named covert quadrature amplitude modulation (QAM), to modulate the messages, where the constellation of covert information bits deviates from its normal coordinates. Moreover, the covert receiver can detect the covert information bits according to the constellation departure. Simulation results show that proposed covert communication system can significantly improve the covert data rate and reduce the covert bit error rate, in comparison with the traditional covert communication systems.

This paper proposes a novel network intrusion detection algorithm based on the combination of Subspace Clustering (SSC) and BP neural network. Firstly, we perform a subspace clustering algorithm on the network data set to obtain different subspaces. Secondly, BP neural network intrusion detection is carried out on the data in different subspaces, and calculate the prediction error value. By comparing with the pre-set accuracy, the threshold is constantly updated to improve the ability to identify network attacks. By comparing with K-means, DBSCAN, SSC-EA and k-KNN intrusion detection model, the SSC-BP neural network model can detect the most attacked networks with the lowest false detection rate.

In electronic warfare, communication may not counter reconnaissance and jamming without the help of network-station selection of frequency hopping. The competition in the field of electromagnetic spectrum is becoming more and more fierce with the increasingly complex electromagnetic environment of modern battlefield. The research on detection, identification, parameter estimation and network station selection of frequency hopping communication network has aroused the interest of scholars both at home and abroad, which has been summarized in this paper. Firstly, the working mode and characteristics of two kinds of FH communication networking modes synchronous orthogonal network and asynchronous non orthogonal network are introduced. Then, through the analysis of FH signals time hopping, frequency hopping, bandwidth, frequency, direction of arrival, bad time-frequency analysis, clustering analysis and machine learning method, the feature-based method is adopted Parameter selection technology is used to sort FH network stations. Finally, the key and difficult points of current research on FH communication network separation technology and the research status of blind source separation technology are introduced in details in this paper.

The Internet of Things (IoT) provides significant benefits for industry due to connect the devices together through the internet. Attribute-Based Encryption (ABE) is a technique can enforce an access control over data to guarantee the data security. In this paper, we propose an ABE scheme for data in industrial IoT. The scheme achieves both security and high performance. When there is a shared subpolicy among the access policies of a sensor, the scheme optimizes the encryption of the messages. Through analysis and simulation, we show that our solution is security and efficient.

Mobile embedded terminals widely applied in individual lives, but its security threats become more and more serious. Malicious attacker can steal sensitive information such as user’s phonebook, credit card information by instrumenting malicious programs, or compromising vulnerable software. Against these problems, this paper proposes a scheme for trusted protection system on the embedded platform. The system uses SM algorithms and hardware security chip as the root of trust to establish security mechanisms, including trusted boot of system image, trusted monitoring of the system running environment, disk partition encryption and verification, etc. These security mechanisms provide comprehensive protection to embedded system boot, runtime and long-term storage devices. This paper introduces the architecture and principles of the system software, design system security functions and implement prototype system for protection of embedded OS. The experiments results indicates the promotion of embedded system security and the performance test shows that encryption performance can meet the practical application.

The code length and rate of length-compatible polar codes can be adaptively adjusted and changed because of the special coding structure. In this paper, we propose a method to construct length-compatible polar codes by employing physical layer encryption technology. The deletion way of frozen bits and generator matrix are random, which makes polar codes more flexible and safe. Simulation analysis shows that the proposed algorithm can not only effectively improve the performance of length-compatible polar codes but also realize the physical layer security encryption of the system.

In this paper, we establish a cryptographic primitive for wireless communications. An asymmetric physical layer encryption (PLE) scheme based on elliptic curve cryptography is proposed. Compared with the conventional symmetric PLE, asymmetric PLE avoids the need of key distribution on a private channel, and it has more tools available for processing complex-domain signals to confuse possible eavesdroppers when compared with upper-layer public key encryption. We use quantized information entropy to measure the constellation confusion degree. The numerical results show that the proposed scheme provides greater confusion to eavesdroppers and yet does not affect the bit error rate (BER) of the intended receiver (the information entropy of the constellation increases to 17.5 for 9-bit quantization length). The scheme also has low latency and complexity [O(N2.37), where N is a fixed block size], which is particularly attractive for implementation.

Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.

In VANET, Sybil nodes generated by attackers cause serious damages to network protocols, resource allocation mechanisms, and reputation models. Other types of attacks can also be launched on the basis of Sybil attack, which bring more threats to VANET. To solve this problem, this paper proposes a Sybil nodes detection method based on RSSI sequence and vehicle driving matrix - RSDM. RSDM evaluates the difference between the RSSI sequence and the driving matrix by dynamic distance matching to detect Sybil nodes. Moreover, RSDM does not rely on VANET infrastructure, neighbor nodes or specific hardware. The experimental results show that RSDM performs well with a higher detection rate and a lower error rate.

Thanks to the development and popularity of context-aware applications, the quality of users' life has been improved through a wide variety of customized services. Meanwhile, users are suffering severe risk of privacy leakage and their privacy concerns are growing over time. To tackle the contradiction between the serious privacy issues and the growing privacy concerns in context-aware applications, in this paper, we propose a privacy-preserving data collection scheme by incorporating the complicated interactions among user, attacker, and service provider into a three-antithetic-party game. Under such a novel game model, we identify and rigorously prove the best strategies of the three parties and the equilibriums of the games. Furthermore, we evaluate the performance of our proposed data collection game by performing extensive numerical experiments, confirming that the user's data privacy can be effective preserved.

With the development of wireless communication, the reliability and the security of data is very significant for the wireless communication. In this paper, a delayed feedback chaotic encryption algorithm based on polar codes is proposed. In order to protect encoding information, we make uses of wireless channels to extract binary keys. The extracted binary keys will be used as the initial value of chaotic system to produce chaotic sequences. Besides, we use the chain effects of delayed feedback, which increase the difficulty of cryptanalysis. The results of the theoretical analyses and simulations show that the algorithm could guarantee the security of data transmission without affecting reliability.

Self-assembled semiconductor quantum dots possess an intrinsic geometric symmetry due to the crystal periodic structure. In order to systematically analyze the symmetric properties of quantum dots' bound states resulting only from geometric confinement, we apply group representation theory. We label each bound state for two kinds of popular quantum dot shapes: pyramid and half ellipsoid with the irreducible representation of the corresponding symmetric groups, i.e., C4v and C2v, respectively. Our study completes all the possible irreducible representation cases of groups C4v and C2v. Using the character theory of point groups, we predict the selection rule for electric dipole induced transitions. We also investigate the impact of quantum dot aspect ratio on the symmetric properties of the state wavefunction. This research provides a solid foundation to continue exploring quantum dot symmetry reduction or broken phenomena because of strain, band-mixing and shape irregularity. The results will benefit the researchers who are interested in quantum dot symmetry related effects such as absorption or emission spectra, or those who are studying quantum dots using analytical or numerical simulation approaches.

In this paper, a dual-field elliptic curve cryptographic processor is proposed to support arbitrary curves within 576-bit in dual field. Besides, two heterogeneous function units are coupled with the processor for the parallel operations in finite field based on the analysis of the characteristics of elliptic curve cryptographic algorithms. To simplify the hardware complexity, the clustering technology is adopted in the processor. At last, a fast Montgomery modular division algorithm and its implementation is proposed based on the Kaliski's Montgomery modular inversion. Using UMC 90-nm CMOS 1P9M technology, the proposed processor occupied 0.86-mm2 can perform the scalar multiplication in 0.34ms in GF(p160) and 0.22ms in GF(2160), respectively. Compared to other elliptic curve cryptographic processors, our design is advantageous in hardware efficiency and speed moderation.

Many applications of mobile computing require the computation of dot-product of two vectors. For examples, the dot-product of an individual's genome data and the gene biomarkers of a health center can help detect diseases in m-Health, and that of the interests of two persons can facilitate friend discovery in mobile social networks. Nevertheless, exposing the inputs of dot-product computation discloses sensitive information about the two participants, leading to severe privacy violations. In this paper, we tackle the problem of privacy-preserving dot-product computation targeting mobile computing applications in which secure channels are hardly established, and the computational efficiency is highly desirable. We first propose two basic schemes and then present the corresponding advanced versions to improve efficiency and enhance privacy-protection strength. Furthermore, we theoretically prove that our proposed schemes can simultaneously achieve privacy-preservation, non-repudiation, and accountability. Our numerical results verify the performance of the proposed schemes in terms of communication and computational overheads.