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Articles de revues sur le sujet « Signal processing for network security »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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1

Alapati, Yaswanth Kumar, and Suban Ravichandran. "An Efficient Signal Processing Model for Malicious Signal Identification and Energy Consumption Reduction for Improving Data Transmission Rate." Traitement du Signal 38, no. 3 (2021): 837–43. http://dx.doi.org/10.18280/ts.380330.

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One of the fields which needs the most security is Ad hoc Network (ANET). The term ANET guarantees that there is no central authority so as to administer the signals. Security is a basic issue while using ANET for establishing communication. A ANET is an assortment of remote signals that can progressively be set up at anyplace and whenever without utilizing any prior system framework. Because of its volatile nature, it has mobility issues to improve the arrangement of the system. One of the difficulties is to recognize the malicious signals in the system. Because of malicious signals, data los

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Gao, Feng, Yun Wu, and Shang Qiong Lu. "LabVIEW-Based Virtual Laboratory for Digital Signal Processing." Advanced Materials Research 268-270 (July 2011): 2150–57. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.2150.

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Based on National Instruments LabVIEW 2009, a network-edition virtual laboratory for digital signal processing (DSP) has been developed. Which is composed of three functional modules, that is, virtual experiment table, information management, and network communication. Hereinto, virtual experiment table is composed of two sub-modules, i.e. resource & document and simulation experiment; information management module is composed of four sub-modules, i.e. database, user registration, security verification and system management; network communication module is implemented by LabVIEW Web Server

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Kumar, Dr A. Pradeep. "Radar Based Security System Alert." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2484–88. http://dx.doi.org/10.22214/ijraset.2024.63509.

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Abstract: Radar-based security systems are now a reliable option for both intruder detection and perimeter defence. This research presents a unique approach to integrate state-of-the-art signal processing methods to improve the performance of such systems. The suggested system tracks and detects moving objects inside an established region using radar technology. The system utilizes advanced processing of signals methods, such as fuzzy logic, neural networks, and wavelet analysis, to differentiate between everyday activities and possible security risks. The network of radar sensors that are pla

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Zhang, Hongwei, Haiyan Wang, Xuanming Liang, Yongsheng Yan, and Xiaohong Shen. "Remote passive acoustic signal detection using multi-scale correlation networks and network spectrum distance in marine environment." Journal of the Acoustical Society of America 154, no. 3 (2023): 1563–76. http://dx.doi.org/10.1121/10.0020907.

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Detecting acoustic signals in the ocean is crucial for port and coastal security, but existing methods often require informative priors. This paper introduces a new approach that transforms acoustic signal detection into network characterization using a MCN construction method. The method constructs a network representation of the acoustic signal by measuring pairwise correlations at different time scales. It proposes a network spectrum distance method that combines information geometry and graph signal processing theory to characterize these complex networks. By comparing the spectra of two n

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S.K., Rajesh, Mary P. Varghese, Lisa C, Rajkumar P, and Winson Rajaian. "SECURE SIGNAL PROCESSING FOR 6G LEO SATELLITE NETWORKS: A DEEP LEARNING APPROACH USING DRIVEN SECURE CHANNEL ESTIMATION MODEL." ICTACT Journal on Communication Technology 16, no. 1 (2025): 3413–18. https://doi.org/10.21917/ijct.2025.0506.

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The rapid evolution of 6G low Earth orbit (LEO) satellite networks presents new challenges in ensuring secure and efficient signal processing at the physical layer. The integration of massive connectivity, dynamic channel variations, and potential eavesdropping threats necessitates robust security mechanisms. Traditional channel estimation techniques struggle to adapt to the highly dynamic nature of LEO satellite channels, leading to degraded performance in secure communications. To address these challenges, a Secure Channel Estimation Model (SCEM) is proposed, leveraging Channel State Informa

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Elliot, Kizzy N., and Levi A.S Damingo. "Application of Blockchain Technology for Secure Signal Processing." Recent Innovations in Wireless Network Security 6, no. 2 (2024): 19–31. https://doi.org/10.5281/zenodo.10683541.

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<em>Blockchain technology is the modern-day solution for trust issues and eliminating or minimizing the role of the third party. In the context of signal processing with the attendant risk of intrusion and distortion of signal and data transmitted in the cloud computing environment, it expedient for the implementation of a more proactive, robust and secure framework to ensure integrity of the data transmitted irrespective of the domain. Using a systematic review methodology, the paper reviewed the efficiency of the application of blockchain technology for secure signal processing in a cloud-co

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Xue, Lian, and Cheng-song Hu. "A Vibration Signal Processing of Large-scale Structural Systems Based on Wireless Sensor." International Journal of Online Engineering (iJOE) 13, no. 05 (2017): 43. http://dx.doi.org/10.3991/ijoe.v13i05.7050.

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The inherent characteristics of large-scale structural system are also called modal parameters, which include natural frequency, damping ratio and vibration mode. They are the basis for analyzing dynamic characteristics of large-scale structural system. Modal Parameter Identification is a modern method, and it is used to identify the vibration signals. At present, the problem of large-scale structural system security is paid more and more attention to, so the method of modal parameter recognition is very significant. A fast integral method is put forward to eliminate the trend item of vibratio

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Asiya, E. Asiya, and U. Onuu Michael. "Design of a Security Wireless Sensor Network for Emergency Response Centers (SWiSNERC)." Journal of Network Security and Data Mining 3, no. 3 (2020): 1–14. https://doi.org/10.5281/zenodo.4280334.

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<em>The design and implementation of a security wireless sensor network capable of detecting physical intrusion was achieved following a survey of personal area networks and mesh networking. The security network engaged sensor nodes fortified with motion sensors and accelerometers. The sensor characteristics were analyzed and found useful for the design of security wireless sensor network in Cross River State Emergency Response Centre (ERC), in the Federal Republic of Nigeria, for examining and optimizing the State’s environs. Technology trends that influence the advancement of such netw

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Cao, Yunyu, Jinrui Tang, Shaohui Shi, Defu Cai, Li Zhang, and Ping Xiong. "Fault Diagnosis Techniques for Electrical Distribution Network Based on Artificial Intelligence and Signal Processing: A Review." Processes 13, no. 1 (2024): 48. https://doi.org/10.3390/pr13010048.

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This paper provides a comprehensive and systematic review of fault diagnosis methods based on artificial intelligence (AI) in smart distribution networks described in the literature. For the first time, it systematically combs through the main fault diagnosis objectives and corresponding fault diagnosis methods for a smart distribution network from the perspective of combined signal processing and artificial intelligence algorithms. The paper provides an in-depth analysis of the advantages and disadvantages of various signal processing techniques and intelligent algorithms in different fault d

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Cheng, Jie, Bingjie Lin, Jiahui Wei, and Ang Xia. "The Compound Prediction Analysis of Information Network Security Situation based on Support Vector Combined with BP Neural Network Learning Algorithm." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 489–96. http://dx.doi.org/10.46300/9106.2022.16.60.

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In order to solve the problem of low security of data in network transmission and inaccurate prediction of future security situation, an improved neural network learning algorithm is proposed in this paper. The algorithm makes up for the shortcomings of the standard neural network learning algorithm, eliminates the redundant data by vector support, and realizes the effective clustering of information data. In addition, the improved neural network learning algorithm uses the order of data to optimize the "end" data in the standard neural network learning algorithm, so as to improve the accuracy

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R., Krithika, Nidhi S., and Sandhiya V. "A Review on Wireless Sensor Network." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 2169–72. https://doi.org/10.31142/ijtsrd18244.

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If sensor networks are to attain their potential, security is one of the most important aspects to be taken care of. The need for security in military applications is obvious, but even more benign uses, such as home health monitoring, habitat monitoring and sub surface exploration require confidentiality. WSNs are perfect for detecting environmental, biological, or chemical threats over large scale areas, but maliciously induced false alarms could completely negate value of the system. , habitat monitoring, and traffic control are presented. Technical challenges in sensor network development i

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Ramachandra, H. V., Pundalik Chavan, S. Supreeth, et al. "Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture." Journal of Electrical and Computer Engineering 2023 (September 28, 2023): 1–11. http://dx.doi.org/10.1155/2023/9390660.

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The development of the fifth generation (5G) and sixth generation (6G) wireless networks has gained wide spread importance in all aspects of life through the network due to their significantly higher speeds, extraordinarily low latency, and ubiquitous availability. Owing to the importance of their users, components, and services to our everyday lives, the network must secure all of these. With such a wide range of devices and service types being present in the 5G ecosystem, security issues are now much more prevalent. Security solutions, are not implemented, must already be envisioned in order

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Poroshenko, Anton, and Andriy Kovalenko. "Audio signal transmission method in network-based audio analytics system." Innovative Technologies and Scientific Solutions for Industries, no. 4(26) (December 27, 2023): 58–67. http://dx.doi.org/10.30837/itssi.2023.26.058.

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The subject matter of the article is аudio signal transmission method in network-based audio analytics system. The creation of a network-based audio analytics system leads to the emergence of new classes of load sources that transmit packetized sound data. Therefore, without constructing adequate mathematical models, it is impossible to build a well-functioning network-based audio analytics system. A fundamental question in traffic theory is the question of load source models. The development of an method for transmitting audio signals in a network-based audio analytics system becomes necessar

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Choi, Hyun-Sik. "Electromyogram (EMG) Signal Classification Based on Light-Weight Neural Network with FPGAs for Wearable Application." Electronics 12, no. 6 (2023): 1398. http://dx.doi.org/10.3390/electronics12061398.

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Recently, the application of bio-signals in the fields of health management, human–computer interaction (HCI), and user authentication has increased. This is because of the development of artificial intelligence technology, which can analyze bio-signals in numerous fields. In the case of the analysis of bio-signals, the results tend to vary depending on the analyst, owing to a large amount of noise. However, when a neural network is used, feature extraction is possible, enabling a more accurate analysis. However, if the bio-signal time series is analyzed as is, the total neural network increas

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Arshad, Muhammad, Liu Jianwei, Muhammad Khalid, Waqar Khalid, Yue Cao, and Fakhri Alam Khan. "Access authentication via blockchain in space information network." PLOS ONE 19, no. 3 (2024): e0291236. http://dx.doi.org/10.1371/journal.pone.0291236.

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Space Information Network (SIN) has significant benefits of providing communication anywhere at any time. This feature offers an innovative way for conventional wireless customers to access enhanced internet services by using SIN. However, SIN’s characteristics, such as naked links and maximum signal latency, make it difficult to design efficient security and routing protocols, etc. Similarly, existing SIN authentication techniques can’t satisfy all of the essentials for secure communication, such as privacy leaks or rising authentication latency. The article aims to develop a novel blockchain

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Ouyang, Yuhong, Man Li, Wenqian Kang, Xiangbei Che, and Ruixian Ye. "Distribution Network Security Situation Awareness Method Based on the Distribution Network Topology Layered Model." Journal of Electrical and Computer Engineering 2023 (April 25, 2023): 1–8. http://dx.doi.org/10.1155/2023/6775337.

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Distribution network security situation awareness refers to the process of perception, understanding, and state projection of systems, elements, and environmental factors within a certain space-time volume. Security situation awareness is an important part of the security assessment of the distribution network. In response to the rapid and accurate distribution network security situation awareness requirements, this paper proposes a distribution network security situation awareness method based the distribution network topology layered model. First, a hierarchical model of the distribution net

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Wang, Kexin, Yihong Gao, Mauro Dragone, Yvan Petillot, and Xu Wang. "A Deep Echo State Network-Based Novel Signal Processing Approach for Underwater Wireless Optical Communication System with PAM and OFDM Signals." Photonics 10, no. 7 (2023): 763. http://dx.doi.org/10.3390/photonics10070763.

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Underwater wireless optical communication (UWOC) plays key role in the underwater wireless sensor networks (UWSNs), which have been widely employed for both scientific and commercial applications. UWOC offers high transmission data rates, high security, and low latency communication between nodes in UWSNs. However, significant absorption and scattering loss in underwater channels, due to ocean water conditions, can introduce highly non-linear distortion in the received signals, which can severely deteriorate communication quality. Consequently, addressing the challenge of processing UWOC signa

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Peng, Qi Hua. "An Improved Abnormal Behavior Feature Detection Algorithm of Network Information Based on Fractional Fourier Transform." Applied Mechanics and Materials 513-517 (February 2014): 2408–11. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2408.

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The feature extraction and detection problem of network information abnormal behavior was researched in this paper. The network attack tended to ambiguity in hidden, and the abnormal behavior of network information is referred as a data signal series, and it was existed in the network information space with strong interference. Traditional detection method was hard to detect the abnormal signal. On the basis of fractional Fourier transform (FRFT), an improved abnormal behavior feature detection algorithm of network information was proposed. The properties such as energy gathering and noise sup

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Xiang, Zhongwu, Weiwei Yang, Gaofeng Pan, Yueming Cai, and Yi Song. "Physical Layer Security in Cognitive Radio Inspired NOMA Network." IEEE Journal of Selected Topics in Signal Processing 13, no. 3 (2019): 700–714. http://dx.doi.org/10.1109/jstsp.2019.2902103.

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Wang, Zhiqiang. "Application analysis of digital electronic technology in communication network." International Journal of Computing and Information Technology 5, no. 1 (2023): 8. http://dx.doi.org/10.56028/ijcit.1.5.8.2023.

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Digital electronic technology has a wide range of applications in communication networks, including digital signal processing, digital transmission technology, and satellite communication technology. However, the application of digital electronic technology also faces issues such as security, technology, and cost. To address these issues, this article analyze s the application of digital electronic technology in communication networks based on future development trends, in order to meet people's growing communication needs.

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Lee, Sanguk, Incheol Jeong, and Woo-Geun Ahn. "Study on Search and Rescue System for Military and Civil use by using COSPAS SARSAT and Terrestrial Communication Network." E3S Web of Conferences 94 (2019): 01018. http://dx.doi.org/10.1051/e3sconf/20199401018.

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We propose search and rescue system for the both military and civil uses with minimizing burden to COSPAS SARSAT by using terrestrial communication network like LTE as an example in the paper. The terrestrial network may not be limited to LTE but extended to 5G, SigFox, LoRa, etc, later on. For security, special encryption mechanism is applied to the both COSPAS SARSAT and Terrestrial links. Also, operation concept for critical and normal situation. Implementation result and enhancing positioning accuracy by using rescue signal itself from ground signal processing is presented too.

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Thang, Vu Viet, Dmitry Valerievich Pantiukhin, Bui Thi Thanh Quyen, and Vu Viet Vu. "A review of neural networks for rare intrusions detection in wireless networks." Journal of Science and Technology on Information security 3, no. 20 (2023): 23–34. http://dx.doi.org/10.54654/isj.v3i20.984.

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Abstract — Neural networks have become the most popular approach for detecting tasks. Currently, neural networks have been strongly applied in the fields of image processing, text and signal processing and have achieved certain effectiveness. However, they have not been widely applied in information security and intrusion detection. Especially, there are no much applications of neural network about rare attacks. In our review, the rare attacks are attacks with a low number of instances or unfamiliar types of security attacks with a low occurrence rate. This is due to a lack of labeled data req

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Ji, Cheongmin, Taehyoung Ko, and Manpyo Hong. "CA-CRE: Classification Algorithm-Based Controller Area Network Payload Format Reverse-Engineering Method." Electronics 10, no. 19 (2021): 2442. http://dx.doi.org/10.3390/electronics10192442.

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In vehicles, dozens of electronic control units are connected to one or more controller area network (CAN) buses to exchange information and send commands related to the physical system of the vehicles. Furthermore, modern vehicles are connected to the Internet via telematics control units (TCUs). This leads to an attack vector in which attackers can control vehicles remotely once they gain access to in-vehicle networks (IVNs) and can discover the formats of important messages. Although the format information is kept secret by car manufacturers, CAN is vulnerable, since payloads are transmitte

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Behlilovic, Narves. "One Approach to Improving Smart Environment Communication via the Security Parameter." International Journal of Embedded and Real-Time Communication Systems 13, no. 1 (2022): 1–30. http://dx.doi.org/10.4018/ijertcs.313042.

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Improving smart environment communication remains a final unachievable destination. Continuous optimization in smart environment communication is mandatory because of an emerging number of connected devices. Carefully observing its parameters and demands leads to acknowledging existing challenges and boundaries regarding areas covered with signal and possibilities of approaching network architecture, limited battery resources in certain nodes of network architecture, privacy, and security of existing data transfer. One approach to dealing with these communication challenges and boundaries is f

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Tefera, Mulugeta Kassaw, Zengwang Jin, and Shengbing Zhang. "A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security." Sensors 22, no. 9 (2022): 3589. http://dx.doi.org/10.3390/s22093589.

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With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are b

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Tu, Jun, Willies Ogola, Dehong Xu, and Wei Xie. "Intrusion Detection Based on Generative Adversarial Network of Reinforcement Learning Strategy for Wireless Sensor Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 478–82. http://dx.doi.org/10.46300/9106.2022.16.58.

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Due to the wireless nature of wireless sensor networks (WSN), the network can be deployed in most of the unattended environment, which makes the networks more vulnerable for attackers who may listen to the traffic and inject their own nodes in the sensor network. In our work, we research on a novel machine learning algorithm on intrusion detection based on reinforcement learning (RL) strategy using generative adversarial network (GAN) for WSN which can automatically detect intrusion or malicious attacks into the network. We combine Actor-Critic Algorithm in RL with GAN in a simulated WSN. The

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Shilova, A. D., and A. A. Vorob’eva. "A Method of Network Access Control for Ensuring Network Infrastructure Security Based on Severing Superfluous Network Connectivity." Automatic Control and Computer Sciences 57, no. 8 (2023): 1116–25. http://dx.doi.org/10.3103/s0146411623080308.

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Demidov, R. A., P. D. Zegzhda, and M. O. Kalinin. "Threat Analysis of Cyber Security in Wireless Adhoc Networks Using Hybrid Neural Network Model." Automatic Control and Computer Sciences 52, no. 8 (2018): 971–76. http://dx.doi.org/10.3103/s0146411618080084.

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Kim, Kang-IL, and Eunghyuk Lee. "To Enhance Security in LED Display System A Study on the Improvement of Video Display SecurityUsing HDMI." Korea Industrial Technology Convergence Society 28, no. 4 (2023): 115–23. http://dx.doi.org/10.29279/jitr.2023.28.4.115.

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Currently, the security technology applied to LED displays is focused on network security, and a problem exists with the expression security function of the final display equipment. Therefore, the development and improvement of security technology, application of standardized security protocols, security training for operators and users, and fundamental and specific approaches are required. Unintended expression accidents and security problems may occur in the display of images. Accordingly, a fundamental security plan applying a representative HDMI signal processing technology is required. Th

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Zheng, Yunfei, Xuejun Zhang, Shenghan Wang, and Weidong Zhang. "Convolutional Neural Network and Ensemble Learning-Based Unmanned Aerial Vehicles Radio Frequency Fingerprinting Identification." Drones 8, no. 8 (2024): 391. http://dx.doi.org/10.3390/drones8080391.

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With the rapid development of the unmanned aerial vehicles (UAVs) industry, there is increasing demand for UAV surveillance technology. Automatic Dependent Surveillance-Broadcast (ADS-B) provides accurate monitoring of UAVs. However, the system cannot encrypt messages or verify identity. To address the issue of identity spoofing, radio frequency fingerprinting identification (RFFI) is applied for ADS-B transmitters to determine the true identities of UAVs through physical layer security technology. This paper develops an ensemble learning ADS-B radio signal recognition framework. Firstly, the

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Ogbondamati, Lloyd Endurance, and Audu Daniel Ekpah. "Real Time Premature Ventricular Contraction Detection Signals-Based Security and Steganography A Fuzzy Neural Network System." Recent Innovations in Wireless Network Security 7, no. 2 (2025): 1–13. https://doi.org/10.5281/zenodo.14809892.

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<em>This study investigates the integration of fuzzy logic and neural network systems for analyzing heart rate variability (HRV) and electrocardiogram (ECG) features to enhance the accuracy of cardiac monitoring. The primary purpose of this research is to develop a robust computational framework capable of synthesizing the dynamic variations in HRV and ECG data for improved decision-making. The challenge addressed lies in the need for accurate, real-time cardiac signal interpretation, as traditional methods often struggle to capture complex relationships between these signals. Using simulated

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Lin, Bingjie, Jie Cheng, Jiahui Wei, and Ang Xia. "A Sensing Method of Network Security Situation Based on Markov Game Model." International Journal of Circuits, Systems and Signal Processing 16 (January 14, 2022): 531–36. http://dx.doi.org/10.46300/9106.2022.16.66.

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The sensing of network security situation (NSS) has become a hot issue. This paper first describes the basic principle of Markov model and then the necessary and sufficient conditions for the application of Markov game model. And finally, taking fuzzy comprehensive evaluation model as the theoretical basis, this paper analyzes the application fields of the sensing method of NSS with Markov game model from the aspects of network randomness, non-cooperative and dynamic evolution. Evaluation results show that the sensing method of NSS with Markov game model is best for financial field, followed b

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He, Jialuan, Zirui Xing, Tianqi Xiang, et al. "Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring." Sensors 21, no. 17 (2021): 5688. http://dx.doi.org/10.3390/s21175688.

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In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict di

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Lin, Chia-Hung, Chia-Hung Wen, Hsiang-Yueh Lai, et al. "Multilayer Convolutional Processing Network Based Cryptography Mechanism for Digital Images Infosecurity." Processes 11, no. 5 (2023): 1476. http://dx.doi.org/10.3390/pr11051476.

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Digital images can be easily shared or stored using different imaging devices, storage tools, and computer networks or wireless communication systems. However, these digital images, such as headshots or medical images, may contain private information. Hence, to protect the confidentiality, reliability, and availability of digital images on online processing applications, it is crucial to increase the infosecurity of these images. Therefore, an authorization encryption scheme should ensure a high security level of digital images. The present study aimed to establish a multilayer convolutional p

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Lazaar, Saiida. "Contribution of wavelets to cybersecurity: Intrusion detection systems using neural networks." General Letters in Mathematics 10, no. 2 (2021): 24–30. http://dx.doi.org/10.31559/glm2021.10.2.2.

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The gigantic growth of the exchanged digital data has raised important security challenges. In this ecosystem, connected objects, systems and networks are exposed to various cyber threats endangering sensitive data and compromising confidentiality, integrity and authentication. Modelling intrusion detection systems (IDS) constitute an important research field with a major goal to protect targeted systems and networks against malicious activities. Many network IDS have been recently designed with artificial intelligence techniques. Signal processing techniques have been applied in network detec

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Mrs. V. Deepapriya, C. Sathana, J. Rishwana Begam, V. Rohini, V. Muthu Subhashini, and D. Evangelin. "Innovative Approaches to Secure Image Processing in Decentralized Environment." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 768–76. https://doi.org/10.47392/irjaeh.2025.0107.

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Ensuring robust image security in cloud environments is a critical challenge due to risks such as unauthorized access, data tampering, and privacy breaches. This study introduces a Blockchain-based Secure Image Encryption (BC-SIE) method using Chebyshev Polynomial Fostered Hierarchical Auto-Associative Polynomial Convolutional Neural Network (CPHAPCNN) to enhance security, integrity, and high-fidelity image reconstruction. During encryption, the input image is divided into two unpredictable cryptographic shares, represented by black dot patterns, rendering them meaningless individually and pre

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Alhusayni, Ahmed, Vijey Thayananthan, Aiiad Albeshri, and Saleh Alghamdi. "Decentralized Multi-Layered Architecture to Strengthen the Security in the Internet of Things Environment Using Blockchain Technology." Electronics 12, no. 20 (2023): 4314. http://dx.doi.org/10.3390/electronics12204314.

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Smart devices are connected to IoT networks and the security risks are substantial. Using blockchain technology, which is decentralized and distributed, 5G-enabled IoT networks might be able to tackle security issues. In order to simplify the implementation and security of IoT networks, we propose a multi-level blockchain security model. As part of the multi-level architecture, the communication between levels is facilitated by clustering. IoT networks define unknown clusters with applications that utilize the evolutionary computation method coupled with anatomy simulation and genetic methodol

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Ku, Giwon, Choeljun Choi, Chulseung Yang, et al. "Electrocardiogram-Based Driver Authentication Using Autocorrelation and Convolutional Neural Network Techniques." Electronics 13, no. 24 (2024): 4974. https://doi.org/10.3390/electronics13244974.

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This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally,

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Kolesnyk, Serhii, and Serhii Kovalov. "Analyzing the effect of reflection on real-time drone sound detection using parallel model of neural networks." Problems of Modeling and Design Automatization 2, no. 20 (2024): 84–92. https://doi.org/10.31474/2074-7888-2024-2-20-84-92.

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This study evaluates the influence of reflections on real-time audio recognition utilizing neural networks, with a specific emphasis on locations where these effects are widespread. The research evaluates the impact of reflections on detection accuracy by creating and evaluating a convolutional neural network model using datasets that include direct and reflected audio inputs. The study explores the use of acoustic lens integration and spatial filtering approaches to improve the resilience of neural networks against reflecting interference. This is particularly important for applications in se

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Korzhov, Serhii O., and Valentyn S. Yesilevskyi. "Using complex-valued neural networks for aircraft identification." Applied Aspects of Information Technology 8, no. 1 (2025): 38–47. https://doi.org/10.15276/aait.08.2025.3.

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This paper presents an approach to aircraft recognition using complex-valued neural networks. The objective of the article is to study the effectiveness of complex-valued neural networks for aircraft identification tasks based on radar data, the efficiencyevaluated based on criteria such as classification accuracy, robustness to noise interference, the ability to maintain high accuracy with limited training data, and an optimal trade-off between accuracy and computational complexity.The study focuses on aircraft identification using phase and amplitude characteristics of radar signals, which a

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Song, Wenzhan, Fangyu Li, Maria Valero, and Liang Zhao. "Toward Creating a Subsurface Camera." Sensors 19, no. 2 (2019): 301. http://dx.doi.org/10.3390/s19020301.

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In this article, the framework and architecture of a Subsurface Camera (SAMERA) are envisioned and described for the first time. A SAMERA is a geophysical sensor network that senses and processes geophysical sensor signals and computes a 3D subsurface image in situ in real time. The basic mechanism is geophysical waves propagating/reflected/refracted through subsurface enter a network of geophysical sensors, where a 2D or 3D image is computed and recorded; control software may be connected to this network to allow view of the 2D/3D image and adjustment of settings such as resolution, filter, r

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Wichary, Tomasz, Jordi Mongay Batalla, Constandinos X. Mavromoustakis, Jerzy Żurek, and George Mastorakis. "Network Slicing Security Controls and Assurance for Verticals." Electronics 11, no. 2 (2022): 222. http://dx.doi.org/10.3390/electronics11020222.

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This paper focuses on the security challenges of network slice implementation in 5G networks. We propose that network slice controllers support security by enabling security controls at different network layers. The slice controller orchestrates multilevel domains with resources at a very high level but needs to understand how to define the resources at lower levels. In this context, the main outstanding security challenge is the compromise of several resources in the presence of an attack due to weak resource isolation at different levels. We analysed the current standards and trends directed

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Lafia, Diafale, Mistura Laide Sanni, Rasheed Ayodeji Adetona, Bodunde Odunola Akinyemi, and Ganiyu Adesola Aderounmu. "Signal Processing-based Model for Primary User Emulation Attacks Detection in Cognitive Radio Networks." Journal of Computing and Information Technology 29, no. 2 (2022): 77–88. http://dx.doi.org/10.20532/cit.2021.1005297.

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Cognitive Radio Networks (CRNs) have been conceived to improve the efficiency of accessing the spectrum. However, these networks are prone to various kinds of attacks and failures that can compromise the security and performance of their users. One of the notable malicious attacks in cognitive radio networks is the Primary User Emulation (PUE) attack, which results in underutilization and unavailability of the spectrum and low operational efficiency of the network. This study developed an improved technique for detecting PUE attacks in cognitive radio networks and further addressed the charact

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Ahmed, Amjed Abbas, Rana Ali Salim, and Mohammad Kamrul Hasan. "Deep Learning Method for Power Side-Channel Analysis on Chip Leakages." Elektronika ir Elektrotechnika 29, no. 6 (2023): 50–57. http://dx.doi.org/10.5755/j02.eie.34650.

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Power side channel analysis signal analysis is automated using deep learning. Signal processing and cryptanalytic techniques are necessary components of power side channel analysis. Chip leakages can be found using a classification approach called deep learning. In addition to this, we do this so that the deep learning network can automatically tackle signal processing difficulties such as re-alignment and noise reduction. We were able to break minimally protected Advanced Encryption Standard (AES), as well as masking-countermeasure AES and protected elliptic-curve cryptography (ECC). These re

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Wang, Huan, Jian Gu, Jianping Zhao, et al. "Prediction Method of Network Security Situation Based on GA-LSSVM Time Series Analysis." Advances in Modelling and Analysis B 60, no. 2 (2017): 372–90. http://dx.doi.org/10.18280/ama_b.600208.

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Heo, Jeonghwan, and Jechang Jeong. "Deceptive Techniques to Hide a Compressed Video Stream for Information Security." Sensors 21, no. 21 (2021): 7200. http://dx.doi.org/10.3390/s21217200.

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With the recent development of video compression methods, video transmission on traditional devices and video distribution using networks has increased in various devices such as drones, IP cameras, and small IoT devices. As a result, the demand for encryption techniques such as MPEG-DASH for transmitting streams over networks is increasing. These video stream security methods guarantee stream confidentiality. However, they do not hide the fact that the encrypted stream is being transmitted over the network. Considering that sniffing attacks can analyze the entropy of the stream and scan huge

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Zhang, Yongkang. "6G Fronthaul Network Architecture Based on Quantum Technology: Integration of QKD with Passive WDM." Applied and Computational Engineering 119, no. 1 (2025): 9–17. https://doi.org/10.54254/2755-2721/2025.21529.

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The rapid evolution of mobile communication technology is propelling us from the era of 5G to the cusp of 6G, which promises ultra-high speeds, ultra-low latency, and seamless global connectivity. However, these ambitious goals require not only significant advancements in traditional communication technologies but also the integration of novel approaches to address emerging security and efficiency challenges. Quantum communication, particularly Quantum Key Distribution (QKD), offers a groundbreaking solution due to its inherent security based on the principles of quantum mechanics. This paper

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Cococcioni, Marco, Federico Rossi, Emanuele Ruffaldi, Sergio Saponara, and Dupont de Dinechin Benoit. "Novel Arithmetics in Deep Neural Networks Signal Processing for Autonomous Driving: Challenges and Opportunities." IEEE Signal Processing Magazine 38, no. 1 (2020): 97–110. https://doi.org/10.1109/MSP.2020.2988436.

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This article focuses on the trends, opportunities, and challenges of novel arithmetic for deep neural network (DNN) signal processing, with particular reference to assisted- and autonomous driving applications. Due to strict constraints in terms of the latency, dependability, and security of autonomous driving, machine perception (i.e., detection and decision tasks) based on DNNs cannot be implemented by relying on remote cloud access. These tasks must be performed in real time in embedded systems on board the vehicle, particularly for the inference phase (considering the use of DNNs pretraine

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Wu, Bin, Xinyu Wu, Peng Li, Youbing Gao, Jiangbo Si, and Naofal Al-Dhahir. "Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition." Sensors 24, no. 3 (2024): 889. http://dx.doi.org/10.3390/s24030889.

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In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To

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Khonde, Shraddha R., and Venugopal Ulagamuthalvi. "Hybrid Architecture for Distributed Intrusion Detection System Using Semi-supervised Classifiers in Ensemble Approach." Advances in Modelling and Analysis B 63, no. 1-4 (2020): 10–19. http://dx.doi.org/10.18280/ama_b.631-403.

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Security of data is becoming a big treat today because of modern attacks. All the data passing through network is at risk as intruders can easily access and modify data. Security to the network is provided using Intrusion Detection System (IDS) which helps to monitor and analyze each packet entering or passing through the network. In this paper hybrid architecture for IDS is proposed which can work as an intelligent system in distributed environment. Proposed system makes use of semi-supervised machine learning classifiers into an ensemble approach. Classifiers used are Support vector machine,

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