Biblio

Security plays a major role in data transmission and reception. Providing high security is indispensable in communication systems. The RSA (Rivest-Shamir-Adleman) cryptosystem is used widely in cryptographic applications as it offers highly secured transmission. RSA cryptosystem uses Montgomery multipliers and it involves modular exponentiation process which is attained by performing repeated modular-multiplications. This leads to high latency and owing to improve the speed of multiplier, highly efficient modular multiplication methodology needs to be applied. In the conventional methodology, Carry Save Adder (CSA) is used in the multiplication and it consumes more area and it has larger delay, but in the suggested methodology, the Reverse Carry Propagate (RCP) adder is used in the place of CSA adder and the obtained output shows promising results in terms of area and latency. The simulation is done with Xilinx ISE design suite. The proposed multiplier can be used effectively in signal processing, image processing and security based applications.

Distributed acoustic sensing (DAS) systems based on fiber brag grating (FBG) have been widely used for distributed temperature and strain sensing over the past years, and function well in perimeter security monitoring and structural health monitoring. However, with relevant algorithms functioning with low accuracy, the DAS system presently has trouble in signal recognition, which puts forward a higher requirement on a high-precision identification method. In this paper, we propose an improved recognition method based on relative fundamental signal processing methods and convolutional neural network (CNN) to construct a mathematical model of disturbance FBG signal recognition. Firstly, we apply short-time energy (STE) to extract original disturbance signals. Secondly, we adopt short-time Fourier transform (STFT) to divide a longer time signal into short segments. Finally, we employ a CNN model, which has already been trained to recognize disturbance signals. Experimental results conducted in the real environments show that our proposed algorithm can obtain accuracy over 96.5%.

Recent developments in deepfake technology are increasing new security threats. To solve these issues, various detection methods have been proposed including the methods utilizing biological signals captured by R-PPG. However, existing methods have limitations in terms of detection accuracy and generalized performance. In this paper, we present our approach for R-PPG-based BPM (Beats Per Minute) analysis for effective deepfake detection. With the selected deepfake datasets, we performed (a) comparison and analysis of conditions for BPM processing, and (b) BPM extraction by dividing the face into 16 regions and comparison of BPM in each region. The results showed that our proposed BPM-related properties are effective in deepfake detection.

In the current network security situation, the types of network threats are complex and changeable. With the development of the Internet and the application of information technology, the general trend is opener. Important data and important business applications will face more serious security threats. However, with the development of cloud computing technology, the trend of large-scale deployment of important business applications in cloud centers has greatly increased. The development and use of software-defined networks in cloud data centers have greatly reduced the effect of traditional network security boundary protection. How to find an effective way to protect important applications in open multi-step large-scale cloud data centers is a problem we need to solve. Threat intelligence has become an important means to solve complex network attacks, realize real-time threat early warning and attack tracking because of its ability to analyze the threat intelligence data of various network attacks. Based on the research of threat intelligence, machine learning, cloud central network, SDN and other technologies, this paper proposes an active defense method of network security based on threat intelligence for super-large cloud data centers.

Wireless communication systems are inherently vulnerable to adversarial attacks since malevolent jammers might jam and disrupt the legitimate transmission intentionally. Of particular interest are so- called denial-of-service (DoS) attacks in which the jammer is able to completely disrupt the communication. Accordingly, it is of crucial interest for the legitimate users to detect such DoS attacks. Turing machines provide the fundamental limits of today's digital computers and therewith of the traditional signal processing. It has been shown that these are incapable of detecting DoS attacks. This stimulates the question of how powerful the signal processing must be to enable the detection of DoS attacks. This paper investigates the general computation framework of Blum-Shub-Smale machines which allows the processing and storage of arbitrary reals. It is shown that such real number signal processing then enables the detection of DoS attacks.

The latest security methods are based on biometric features. The electrocardiogram is increasingly used in such systems because it provides biometric features that are difficult to falsify. This paper aims to study the use of the electrocardiogram together with the Convolutional Neural Networks, in order to identify the subjects based on the ECG signal and to improve the security. In this study, we used the Fantasia database, available on the PhysioNet platform, which contains 40 ECG recordings. The ECG signal is pre-processed, and then spectrograms are generated for each ECG signal. Spectrograms are applied to the input of several architectures of Convolutional Neural Networks like Inception-v3, Xception, MobileNet and NasNetLarge. An analysis of performance metrics reveals that the subject identification method based on ECG signal and CNNs provides remarkable results. The best accuracy value is 99.5% and is obtained for Inception-v3.

A conventional visible light communication system consists of a transmitter, a jammer that includes a few light emitting diodes, a legal listener and an eavesdropper. In this work, a similar system is designed with a collimating lens in order to create an extra layer of practical physical security measure. The use of a collimating lens makes it available to spatially limiting data transmission to an area under the lensed transmitter. Also focused data transmission through the optical lens, increases the secrecy rate. To investigate the applicability of the proposed design we designed a sample experimental setup using USRP and implemented in a laboratory environment. In the proposed set up, the receiver is in a fixed position. However, it is possible to implement an easy, practical and cheap hardware solution with respect to a beamforming type VLC that uses directional beam forming method to establish transmission to a dynamic target. In addition, it is achievable to control the size of the area where a receiver can access data by manipulating the distance between the optical lens and transmitter.

Aimming at the severe security threat faced by information transmission in wireless communication, the artificial interference in physical layer security technology was considered, and the influence of artificial interference signal style on system information transmission security was analyzed by simulation, which provided technical accumulation for the design of wireless security transmission system based on artificial interference.

A method for providing energy and structural secrecy of a signal is presented, which is based on the method of pseudo-random restructuring of the spreading sequence. This method complicates the implementation of the accumulation mode, and therefore the detection of the signal-code structure of the signal in a third-party receiver, due to the use of nested pseudo-random sequences (PRS) and their restructuring. And since the receiver-detector is similar to the receiver of the communication system, it is necessary to ensure optimal signal processing to implement an acceptable level of structural secrecy.

An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbations on one model and apply it the input to fool the output of a different model. Our work focuses on studying the transferability of adversarial attacks in sound event classification. We are able to demonstrate differences in transferability properties from those observed in computer vision. We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks.

In this paper, our aim is to detect illegal spectrum access behaviors. Firstly, we detect whether the channel is busy, and then if it is busy, recognizing whether there are illegal users. To get closer to the actual situation, we consider a more general scenario where multiple users are authorized to work on the same channel under certain interference control strategies, and build it as a ternary hypothesis test model using the generalized multi-hypothesis Neyman-Pearson criterion. Considering the various potential combination of multiple authorized users, the spectrum detection process utilizes a two-step detector. We adopt the Generalized Likelihood Ratio Test (GLRT) and the Rao test to detect illegal spectrum access behaviors. What is more, the Wald test is proposed which has a compromise between computational complexity and performance. The relevant formulas of the three detection schemes are derived. Finally, comprehensive and in-depth simulations are provided to verify the effectiveness of the proposed detection scheme that it has the best detection performance under different authorized sample numbers and different performance constraints. Besides, we illustrate the probability of detection of illegal behaviors under different parameters of illegal behaviors and different sets of AUs' states under the Wald test.

This article investigates the beamforming method selection in artificial-noise-aid (AN-aid) multiuser multiple-input-single-output (MU-MISO) broadcast wiretap systems in slow fading channel environment. We adopt beamforming pre-coding matrix with artificial noise to achieve secure multiuser communication and optimize system performance, and compare the secure transmission performance of two beamforming methods. To overcome the complexity of this model, a novel optimization scheme expressed using semi-closed-form expressions and Monte Carlo method is employed to derive the relationship between transmission parameters and secure transmission performance. This scheme would help us to analyses performance of different beamforming methods.

The following topics are dealt with: diseases; medical signal processing; learning (artificial intelligence); security of data; blood; patient treatment; patient monitoring; bioelectric phenomena; biomedical electrodes; biological tissues.

This paper studies the security issue of a gossip-based distributed projected gradient (DPG) algorithm, when it is applied for solving a decentralized multi-agent optimization. It is known that the gossip-based DPG algorithm is vulnerable to insider attacks because each agent locally estimates its (sub)gradient without any supervision. This work leverages the convolutional neural network (CNN) to perform the detection and localization of the insider attackers. Compared to the previous work, CNN can learn appropriate decision functions from the original state information without preprocessing through artificially designed rules, thereby alleviating the dependence on complex pre-designed models. Simulation results demonstrate that the proposed CNN-based approach can effectively improve the performance of detecting and localizing malicious agents, as compared with the conventional pre-designed score-based model.

In this paper, an E-band MIMO radar with 1 transmit and 4 receive channels is designed. The signal bandwidth is 2GHz at 77GHz, the max power of transmitted signal which is Frequency-modulated continuous-wave (FMCW) is 13dBm. This radar consists of two cascade parts: RF frond-end and digital signal process block. The RF front-end part includes antenna array, millimeter wave transceiver chips, and the digital signal process part includes FPGA, DSP and power supply circuits. It could be used in foreign object detection (FOD), landing assistance of helicopter and security checking.

The use of wireless connectivity has increased exponentially in recent years. Fifth generation (5G) communications will soon be deployed worldwide. Six-generation (6G) communications vision and planning have begun, and the use of 6G communications is expected to begin in the 2030s. The 6G system has higher capacity, higher data rates, lower latency, higher security and better quality of service (QoS) compared to the 5G system. This paper presents a brief overview on the vision and requirements of 6G wireless communications and networks. Finally, some of the challenges in launching the 6G are also explained.

Transmit beamforming and artificial noise (AN) methods have been widely employed to achieve wireless physical layer (PHY) secure transmissions. While most works focus on transmit beamforming optimization, little attention is paid to the design of artificial noise projection matrix (ANPM). In this paper, compared with traditional ANPM obtained by zero-forcing method, which only makes AN power uniform distribution in free space outside legitimate users (LU) locations, we design ANPM to maximize the interference on eavesdroppers without interference on LUs for multicast directional modulation (MCDM) scenario based on frequency diverse array (FDA). Furthermore, we extend our approach to the case of with imperfect locations of Eves. Finally, simulation results show that Eves can be seriously affected by the AN with perfect/imperfect locations, respectively.

Intentional interference such as spoofing is an emerging threat to GPS receivers used in both civilian and defense applications. With the majority of smartphones relying on GPS for positioning and navigation, the vulnerability of these phones to spoofing attacks is an issue of security concern. In this paper, it is demonstrated that is easy to successfully spoof a smartphone using a simplistic spoofing technique. A spoofing signal is generated using open-source signal simulator and transmitted using a low-cost SDR. In view of the tremendously increasing usage of GPS enabled smartphones, it is necessary to develop suitable countermeasures for spoofing. This work carries significance as it would help in understanding the effects of spoofing at various levels of signal processing in the receiver and develop advanced spoofing detection and mitigation techniques.

The orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering K-sparse signals x ∈ ℝn from linear model y = Ax, where A ∈ ℝm×n is a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover x for random matrix A. Although in many practical applications, in addition to the sparsity, x usually also has some additional property (for example, the nonzero entries of x independently and identically follow the Gaussian distribution), none of existing analysis uses these properties to answer the above question. In this paper, we first show that the prior distribution information of x can be used to provide an upper bound on \textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar21/\textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar22, and then explore the bound to develop a better lower bound on the probability of exact recovery with OMP in K iterations. Simulation tests are presented to illustrate the superiority of the new bound.

Although beamforming optimization problems in full-duplex communication systems can be optimally solved with the semidefinite relaxation (SDR) approach, its computational complexity increases rapidly when the problem size increases. In order to circumvent this issue, in this paper, we propose an alternating direction of multiplier method (ADMM) which minimizes the augmented Lagrangian of the dual of the SDR and handles the inequality constraints with the use of slack variables. The proposed ADMM is then applied for optimizing the relay beamformer to maximize the secrecy rate. Simulation results show that the proposed ADMM performs as good as the SDR approach.

The main information and communication systems (ICS) effectiveness parameters are: reliability, resiliency, network bandwidth, service quality, profitability and cost, malware protection, information security, etc. Most modern ICS refers to multiuser systems, which implement the most promising method of distributing subscribers (users), namely, the code distribution, at which, subscribers are provided with appropriate forms of discrete sequences (signatures). Since in multiuser systems, channels code division is based on signal difference, then the ICS construction and systems performance indicators are determined by the chosen signals properties. Distributed spectrum technology is the promising direction of information security for telecommunication systems. Currently used data generation and processing methods, as well as the broadband signal classes used as a physical data carrier, are not enough for the necessary level of information security (information secrecy, imitation resistance) as well as noise immunity (impedance reception, structural secrecy) of the necessary (for some ICS applications). In this case, discrete sequences (DS) that are based on nonlinear construction rules and have improved correlation, ensemble and structural properties should be used as DS that extend the spectrum (manipulate carrier frequency). In particular, with the use of such signals as the physical carrier of information or synchronization signals, the time expenditures on the disclosure of the signal structure used are increasing and the setting of "optima", in terms of the counteracting station, obstacles becomes problematic. Complex signals obtained on such sequences basis have structural properties, similar to random (pseudorandom) sequences, as well as necessary correlation and ensemble properties. For designing signals for applications applied for measuring delay time, signal detecting, synchronizing stations and etc, side-lobe levels of autocorrelation function (ACF) minimization is essential. In this paper, the problem of optimizing the synthesis of nonlinear discrete sequences, which have improved ensemble, structural and autocorrelation properties, is formulated and solved. The use of nonlinear discrete signals, which are formed on the basis of such sequences, will provide necessary values for impedance protection, structural and information secrecy of ICS operation. Increased requirements for ICS information security, formation and performance data in terms of internal and external threats (influences), determine objectively existing technical and scientific controversy to be solved is goal of this work.The paper presents the results of solving the actual problem of performance indicators improvements for information and communication systems, in particular secrecy, information security and noise immunity with interfering influences, based on the nonlinear discrete cryptographic signals (CS) new classes synthesis with the necessary properties.

A distributed energy resource prototype is used to show cybersecurity best practices. These best practices include straightforward security techniques, such as encrypted serial communication. The best practices include more sophisticated security techniques, such as a method to evaluate and respond to firmware integrity at run-time. The prototype uses embedded Linux, a hardware-assisted monitor, one or more digital signal processors, and grid-connected power electronics. Security features to protect communication, firmware, power flow, and hardware are developed. The firmware run-time integrity security is presently evaluated, and shown to maintain power electronics uptime during firmware updating. The firmware run-time security feature can be extended to allow software rejuvenation, multi-mission controls, and greater flexibility and security in controls.

Smart Grid (SG) is regarded as complex electrical power system due to massive penetration of Renewable Energy Resources and Distribution Generations. The implementation of adjustable speed drives, advance power electronic devices, and electric arc furnaces are incorporated in SG (the transition from conventional power system). Moreover, SG is an advance, automated, controlled, efficient, digital, and intelligent system that ensures pertinent benefits, such as: (a) consumer empowerment, (b) advanced communication infrastructure, (c) user-friendly system, and (d) supports bi-directional power flow. Digital Signal Processing (DSP) is key tool for SG deployment and provides key solutions to a vast array of complex SG challenges. This research provides a comprehensive study on DSP interactions within SG. The prominent challenges posed by conventional grid, such as: (a) monitoring and control, (b) Electric Vehicles infrastructure, (c) cyber data injection attack, (d) Demand Response management and (e) cyber data injection attack are thoroughly investigated in this research.

In this paper, we consider one of the approaches to the study of the characteristics of an information system that is under the influence of various factors, and their management using neural networks and wavelet transforms based on determining the relationship between the modified state of the information system and the possibility of dynamic analysis of effects. At the same time, the process of influencing the information system includes the following components: impact on the components providing the functions of the information system; determination of the result of exposure; analysis of the result of exposure; response to the result of exposure. As an input signal, the characteristics of the means that affect are taken. The system includes an adaptive response unit, the input of which receives signals about the prerequisites for changes, and at the output, this unit generates signals for the inclusion of appropriate means to eliminate or compensate for these prerequisites or directly the changes in the information system.

This paper studies secure communication from a multi-antenna transmitter to a single-antenna receiver in the presence of multiple multi-antenna eavesdroppers, considering constraints of security quality of service (QoS), i.e., minimum allowable signal-to-interference-and-noise ratio (SINR) at receiver and maximum tolerable SINR at eavesdroppers. The robust joint optimal beamforming (RJOBF) of secret signal and artificial noise (AN) is designed to minimize transmit power while estimation errors of channel state information (CSI) for wiretap channels are taken into consideration. The formulated design problem is shown to be nonconvex and we transfer it into linear matrix inequalities (LMIs) along with semidefinite relaxation (SDR) technique. The simulation results illustrate that our proposed RJOBF is efficient for power saving in security communication.