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Littérature scientifique sur le sujet « Signal processing for network security »

Auteur : Grafiati

Publié le 10 mars 2023

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Articles de revues sur le sujet "Signal processing for network security"

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 loss or high energy consumption will occur which reduce the overall performance of the ANET. There are a few circumstances when at least one signal in the system become malevolent and will destroy the limit of the system. The point of this work is to recognize the malignant signals quickly to avoid loss of data. The conventional strategy for firewall and encryption isn't adequate to secure the system. In this way a malicious signal identification framework must be added to the ad hoc network. A signal needs to be secured when utilizing the resources and to provide secure communication. The ad hoc networks have several issues like, congestion, overload, data loss and energy consumption. In the proposed work a framework for Rapid Malicious Signal Detection with Energy Consumption Reduction (RMSDwECR) Method is proposed. The proposed method is compared with the traditional methods in terms of load in the network, data loss ratio, signal transmission rate, energy consumption levels, malicious signal identification time and throughput levels. The proposed method exhibits better performance than the traditional methods.

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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. The DSP Virtual Laboratory is suit for experimental teaching of a range of subjects, such as Digital Signal Processing, Signals & Systems, etc. And the designed virtual laboratory can provide users with a remote virtual experimental platform without time and space constraints.

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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 vibration signals, and the vibration signals are collected through the wireless sensor network (acceleration signal), so as to obtain the integrated vibration signal (speed and displacement signal). The polynomial fitting method is applied to eliminate the trend items in the sampling integral, and improve the operation speed and accuracy by the relationship among the various coefficients. Then, they are discretized to meet the wireless sensor network requirements of "online" processing and analysis. Through the simulation of acceleration signals based on finite element modeling and the processing of actual acquisition acceleration signals based on wireless sensor network, the effectiveness of this method was verified. As a result, the precision effect by sampling frequency and the data length is summarized

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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 and computational efficiency of network security situation prediction.MATLAB simulation results show that the data processing capacity of support vector combined BP neural network is consistent with the actual security situation data requirements, the consistency can reach 98%. the consistency of the security situation results can reach 99%, the composite prediction time of the whole security situation is less than 25s, the line segment slope change can reach 2.3% ,and the slope change range can reach 1.2%,, which is better than BP neural network algorithm.

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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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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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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 transmitted in plain text. In contrast, the secrecy of message formats inhibits IVN security research by third-party researchers. It also hinders effective security tests for in-vehicle networks as performed by evaluation authorities. To mitigate this problem, a method of reverse-engineering CAN payload formats is proposed. The method utilizes classification algorithms to predict signal boundaries from CAN payloads. Several features were uniquely chosen and devised to quantify the type-specific characteristics of signals. The method is evaluated on real-world and synthetic CAN traces, and the results show that our method can predict at least 10% more signal boundaries than the existing methods.

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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 GAN is employed as part of RL environment to generate fake data with possible attacks, which is similar to the real data generated by the sensor networks. Its main aim is to confuse the adversarial network into differentiating between the real and fake data with possible attacks. The results that is from the experiments are based on environment of GAN and Network Simulator 3 (NS3) illustrate that Actor-Critic&GAN algorithm enhances security of the simulated WSN by protecting the networks data against adversaries and improves on the accuracy of the detection.

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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 suppression of 4-order cumulant slice were taken in advantage. In the post processing of fractional Fourier transform detection, the post processing operator of 4 order cumulant was introduced in the detection algorithm, the post energy was gathered in the fractional Fourier domain, the signal accumulation was likely to be increased, and the interference noise could be restrained effectively. Simulation results show that the improved algorithm has perfect noise suppression performance, and it can detect and extract the abnormal behavior feature in the network space to maximum. The detection performance is improved greatly, and the research result can be applied in the network information warfare and network security areas.

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Ahmed, Aseel K., and Abbas Akram Khorsheed. "Open network structure and smart network to sharing cybersecurity within the 5G network." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 573. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp573-582.

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The next-generation communication system incorporates information technology (IT) and operations technology (OT) for generating, delivering, and collecting, and obtaining communication power. We plan to include a brief outline of internet of thing (IoT) communication and its context, along with security concerns that arise for IoT data on the network and some methods for detecting and avoiding cyber security threats. With the rise of the 5G networks, we introduce the smart network's emergent technology and its opportunities and more cybersecurity issues. Whereas, finding or responding to a power outage is an essential part of system security That is why we will discuss the innumerable advantages of 5G networks and we must also cover the inevitable problems that we will encounter in power delivery. The use of smart IoT communication technologies is becoming more common in the energy sector, particularly with the network (5G. The smart network and energy flow integration Real-time data on generation, electricity distribution, and energy consumption is measured using computers and cutting-edge technologies. This information aids utility companies in managing electricity supply and demand, as well as price. While enhanced communication and information technologies are unquestionably crucial to the smart network.

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Thèses sur le sujet "Signal processing for network security"

Lu, Xiaotao. "Cost-effective signal processing algorithms for physical-layer security in wireless networks." Thesis, University of York, 2016. http://etheses.whiterose.ac.uk/16043/.

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Data privacy in traditional wireless communications is accomplished by cryptography techniques at the upper layers of the protocol stack. This thesis aims at contributing to the critical security issue residing in the physical-layer of wireless networks, namely, secrecy rate in various transmission environments. Physical-layer security opens the gate to the exploitation of channel characteristics to achieve data secure transmission. Precoding techniques, as a critical aspect in pre-processing signals prior to transmission has become an effective approach and recently drawn significant attention in the literature. In our research, novel non-linear precoders are designed focusing on the improvement of the physical-layer secrecy rate with consideration of computational complexity as well as the Bit Error Ratio (BER) performance. In the process of designing the precoder, strategies such as Lattice Reduction (LR) and Artificial Noise (AN) are employed to achieve certain design requirements. The deployment and allocation of resources such as relays to assist the transmission also have gained significant interest. In multiple-antenna relay networks, we examine various relay selection criteria with arbitrary knowledge of the channels to the users and the eavesdroppers. Furthermore, we provide novel effective relay selection criteria that can achieve a high secrecy rate performance. More importantly they do not require knowledge of the channels of the eavesdroppers and the interference. Combining the jamming technique with resource allocation of relay networks, we investigate an opportunistic relaying and jamming scheme for Multiple-Input Multiple-Output (MIMO) buffer-aided downlink relay networks. More specifically, a novel Relaying and Jamming Function Selection (RJFS) algorithm as well as a buffer-aided RJFS algorithm are developed along with their ability to achieve a higher secrecy rate. Relying on the proposed relay network, we detail the characteristics of the system, under various relay selection criteria, develop exhaustive search and greedy search-based algorithms, with or without inter-relay Interference Cancellation (IC).

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Di, Mauro Mario. "Statistical models for the characterization, identification and mitigation of distributed attacks in data networks." Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3088.

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2016 - 2017 The thesis focuses on statistical approaches to model, mitigate, and prevent distributed network attacks. When dealing with distributed network attacks (and, more in general, with cyber-security problems), three fundamental phases/issues emerge distinctly. The first issue concerns the threat propagation across the network, which entails an "avalanche" effect, with the number of infected nodes increasing exponentially as time elapses. The second issue regards the design of proper mitigation strategies (e.g., threat detection, attacker's identification) aimed at containing the propagation phenomenon. Finally (and this is the third issue), it is also desirable to act on the system infrastructure to grant a conservative design by adding some controlled degree of redundancy, in order to face those cases where the attacker has not been yet defeated. The contributions of the present thesis address the aforementioned relevant issues, namely, propagation, mitigation and prevention of distributed network attacks. A brief summary of the main contributions is reported below. The first contribution concerns the adoption of Kendall’s birth-and-death process as an analytical model for threat propagation. Such a model exhibits two main properties: i) it is a stochastic model (a desirable requirement to embody the complexity of real-world networks) whereas many models are purely deterministic; ii) it is able to capture the essential features of threat propagation through a few parameters with a clear physical meaning. By exploiting the remarkable properties of Kendall’s model, the exact solution for the optimal resource allocation problem (namely, the optimal mitigation policy) has been provided for both conditions of perfectly known parameters, and unknown parameters (with the latter case being solved through a Maximum-Likelihood estimator). The second contribution pertains to the formalization of a novel kind of randomized Distributed Denial of Service (DDoS) attack. In particular, a botnet (a network of malicious entities) is able to emulate some normal traffic, by picking messages from a dictionary of admissible requests. Such a model allows to quantify the botnet “learning ability”, and to ascertain the real nature of users (normal or bot) via an indicator referred to as MIR (Message Innovation Rate). Exploiting the considered model, an algorithm that allows to identify a botnet (possibly) hidden in the network has been devised. The results are then extended to the case of a multi-cluster environment, where different botnets are concurrently present in the network, and an algorithm to identify the different clusters is conceived. The third contribution concerns the formalization of the network resilience problem and the consequent design of a prevention strategy. Two statistical frameworks are proposed to model the high availability requirements of network infrastructures, namely, the Stochastic Reward Network (SRN), and the Universal Generating Function (UGF) frameworks. In particular, since in the network environment dealing with multidimensional quantities is crucial, an extension of the classic UGF framework, called Multi-dimensional UGF (MUGF), is devised. [edited by author] XVI n.s.

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Mynampati, Vittal Reddy, Dilip Kandula, Raghuram Garimilla, and Kalyan Srinivas. "Performance and Security of Wireless Mesh Networks." Thesis, Blekinge Tekniska Högskola, Avdelningen för telekommunikationssystem, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2901.

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The thesis aims to find issues that may affect the performance of meshed wireless networks. There is no denying the fact that out of the wireless technologies being used in today’s environment, the wireless meshed technology is one of the most advanced and can be viewed as the technology of the future. This thesis deals closely with aspects like throughput, security and performance as these metrics have a direct influence on the performance of the wireless mesh.The thesis is subdivided into various categories explaining the primary structure of wireless mesh networks. Performance of the network has always been a key issue and reliability is the core metric of evaluating the quality of a network. Routing protocols for these networks and which help in improving the performance are examined and the best routing protocol is suggested. This helps to improve the throughput which is the main aspect for maintaining a good performance. The main problem with wireless networks is making them security. This area is also considered as it improves the performance of the whole network. Also the network should be scalable to properly utilize the frequency and get optimal performance. This is required for the successful delivery of data packets. Thus, this area is also investigated together with some other factors that influence the behaviour of these networks. Last, but not least, we provide a discussion about possible future work as well as specifying a system that will help to increase the performance.

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Xu, Jingxin. "Unusual event detection in crowded scenes." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/76365/1/Jingxin_Xu_Thesis.pdf.

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Novel computer vision techniques have been developed to automatically detect unusual events in crowded scenes from video feeds of surveillance cameras. The research is useful in the design of the next generation intelligent video surveillance systems. Two major contributions are the construction of a novel machine learning model for multiple instance learning through compressive sensing, and the design of novel feature descriptors in the compressed video domain.

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Moore, Patrick. "Architectural investigation into network security processing." Thesis, Queen's University Belfast, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492519.

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In this thesis, innovative techniques for accelerating and scaling cryptographic architectures for the provision of network security are presented. The first of these is a novel ASIP based interface, which allows for generic hardware block cipher cores to be used within a 32-bit ASIP platform. An example system consisting of the Altera Nios-II and an AES block cipher is described and how the interface can be extended to support additional encryption modes of operation is discussed. A figure of merit that can be utilised to determine the efficiency of interfaces is proposed. Following on from this a thorough investigation into IKEv2 is presented, With specific focus on hardware/software partitioning of the system. This resulted in the proposal of two systems -- one suitable for use in an embedded context, the other suitable for server deployment -- each capable of processing 250 key exchanges per second. The scalability of the proposed IKEv2 systems is also investigated with respect to the number of processors and the rate at which key exchanges can be sustained. As a result, two scalable architectures are proposed -- pipelined and iterative -- both of which could utilise the embedded or server based implementation. A simulator was developed to determine the optimal configurations within these systems. The iterative architecture was found to be more efficient in terms of throughput and latency. Further to this, the effect of unreliable networks on the developed architectures is investigated with falloffs in performance being observed as the network degrades. The implications of being able to employ the high-speed IKEv2 architecture within a scalable overall IPsec system were investigated. In the course of this exploration, three novel architectures were developed to aid with the scalability: a multi-threaded block cipher' architecture, a system to allow for parallelisation of HMAC structures and finally a system to allow for efficient key distribution within the IPsec architecture.

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Zhao, Wentao. "Genomic applications of statistical signal processing." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2952.

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Liu, Jinshan. "Secure and reliable deep learning in signal processing." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103740.

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In conventional signal processing approaches, researchers need to manually extract features from raw data that can better describe the underlying problem. Such a process requires strong domain knowledge about the given problems. On the contrary, deep learning-based signal processing algorithms can discover features and patterns that would not be apparent to humans by feeding a sufficient amount of training data. In the past decade, deep learning has proved to be efficient and effective at delivering high-quality results. Deep learning has demonstrated its great advantages in image processing and text mining. One of the most promising applications of deep learning-based signal processing techniques is autonomous driving. Today, many companies are developing and testing autonomous vehicles. High-level autonomous vehicles are expected to be commercialized in the near future. Besides, deep learning has demonstrated great potential in wireless communications applications. Researchers have addressed some of the most challenging problems such as transmitter classification and modulation recognition using deep learning. Despite these advantages, there exist a wide range of security and reliability issues when applying deep learning models to real-world applications. First, deep learning models could not generate reliable results for testing data if the training data size is insufficient. Since generating training data is time consuming and resource intensive, it is important to understand the relationship between model reliability and the size of training data. Second, deep learning models could generate highly unreliable results if the testing data are significantly different from the training data, which we refer to as ``out-of-distribution (OOD)'' data. Failing to detect OOD testing data may expose serious security risks. Third, deep learning algorithms can be easily fooled when the input data are falsified. Such vulnerabilities may cause severe risks in safety-critical applications such as autonomous driving. In this dissertation, we focus on the security and reliability issues in deep learning models in the following three aspects. (1) We systematically study how the model performance changes as more training data are provided in wireless communications applications. (2) We discuss how OOD data can impact the performance of deep learning-based classification models in wireless communications applications. We propose FOOD (Feature representation for OOD detection), a unified model that can detect OOD testing data effectively and perform classifications for regular testing data simultaneously. (3) We focus on the security issues of applying deep learning algorithms to autonomous driving. We discuss the impact of Perception Error Attacks (PEAs) on LIDAR and camera and propose a countermeasure called LIFE (LIDAR and Image data Fusion for detecting perception Errors). Doctor of Philosophy Deep learning has provided computers and mobile devices extraordinary powers to solve challenging signal processing problems. For example, current deep learning technologies are able to improve the quality of machine translation significantly, recognize speech as accurately as human beings, and even outperform human beings in face recognition. Although deep learning has demonstrated great advantages in signal processing, it can be insecure and unreliable if the model is not trained properly or is tested under adversarial scenarios. In this dissertation, we study the following three security and reliability issues in deep learning-based signal processing methods. First, we provide insights on how the deep learning model reliability is changed as the size of training data increases. Since generating training data requires a tremendous amount of labor and financial resources, our research work could help researchers and product developers to gain insights on balancing the tradeoff between model performance and training data size. Second, we propose a novel model to detect the abnormal testing data that are significantly different from the training data. In deep learning, there is no performance guarantee when the testing data are significantly different from the training data. Failing to detect such data may cause severe security risks. Finally, we design a system to detect sensor attacks targeting autonomous vehicles. Deep learning can be easily fooled when the input sensor data are falsified. Security and safety can be enhanced significantly if the autonomous driving systems are able to figure out the falsified sensor data before making driving decisions.

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Farhat, Md Tanzin. "An Artificial Neural Network based Security Approach of Signal Verification in Cognitive Radio Network." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo153511563131623.

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CARDOSO, LUIZ ALBERTO LISBOA DA SILVA. "ANALYSIS OF PLASTIC NEURAL NETWORK MODELLING APPROACH TO SIGNAL PROCESSING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1992. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9512@1.

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MARINHA DO BRASIL Os modelos plásticos de redes neurais são estudados e avaliados como uma interessante abordagem da neurocomputação ao processamento de sinais. Dentre estes, o modelo SONN, recentemente proposto por Tenório e Lee, é revisado e adotado como base para a implementação de um ambiente interativo de prototipagem e análise de redes, dada sua reduzida carga heurística. Como ilustração de seu emprego, um problema de detecção e classificação de sinais pulsados é solucionado, com resultados que preliminarmente indicam a adequação do modelo como ferramenta na filtragem não-linear de sinais e no reconhecimento de padrões. Plastic neural network models are evaluated as an attractive neurocomputing approach to signal processing. Among these, the SONN model, as recently introduced by Tenorio and Lee, is reviewed and adopted as the basis for the implementation of an interactive network prototyping and analysis system, due to its reduced heuristics. Its use is exemplified in the task of detection and classification of pulsed signals, showing up results that preliminarily qualify the model as a tool for non-linear filtering and pattern recognition applications.

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Harper, Scott Jeffery. "A Secure Adaptive Network Processor." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/28023.

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Network processors are becoming a predominant feature in the field of network hardware. As new network protocols emerge and data speeds increase, contemporary general-purpose network processors are entering their second generation and academic research is being actively conducted into new techniques for the design and implementation of these systems. At the same time, systems ranging from secured military communications equipment to consumer devices are being updated to provide network connectivity. Many of these devices require, or would benefit from, the inclusion of device security in addition to data security. Whether it is a top-secret encryption scheme that must be concealed or a personal device that needs protection against unauthorized use, security of the device itself is becoming an important factor in system design. Unfortunately, current network processor solutions were not developed with device security in mind. A secure adaptive network processor can provide the means to fill this gap while continuing to provide full support for emerging communication protocols. This dissertation describes the concept and structure of one such device. Analysis of the hardware security provided by the proposed device is provided to highlight strengths and weaknesses, while a prototype system is developed to allow it to be embedded into practical applications for investigation. Two such applications are developed, using the device to provide support for both a secure network edge device and a user-adaptable network gateway. Results of these experiments indicate that the proposed device is useful both as a hardware security measure and as a basis for user adaptation of information-handling systems. Ph. D.

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Livres sur le sujet "Signal processing for network security"

International, Conference on Intelligent Information Hiding and Multimedia Signal Processing (4th 2008 Harbin Shi China). IIH-MSP 2008 : 2008 Fourth International Conference on Intelligent Information Hiding and Multimedia Signal Processing : 15-17 August 2008, Harbin, China. IEEE Computer Society, 2008.

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2nd 2006 Pasadena, Calif.). 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing: (IIH-MSP 2006) : proceedings ; 18-20 December, 2006, Pasadena, California, USA. IEEE Computer Society, 2006.

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Joaquim, Filipe, Coelhas Helder, Saramago Monica, and International Conference on E-business and Telecommunication Networks (2nd : 2005 : Reading, England), eds. E-business and telecommunication networks: Second International Conference, ICETE 2005, Reading, UK, October 3-7, 2005 : selected papers. Springer, 2007.

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Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2013.

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1944-, Deprettere Ed F., Leupers Rainer, Takala Jarmo, and SpringerLink (Online service), eds. Handbook of Signal Processing Systems. Springer Science+Business Media, LLC, 2010.

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Dey, Nilanjan, and V. Santhi, eds. Intelligent Techniques in Signal Processing for Multimedia Security. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44790-2.

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Corporate computer and network security. 2nd ed. Prentice Hall, 2010.

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Security+ guide to network security fundamentals. 3rd ed. Course Technology, Cengage Learning, 2009.

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Takanami, Tetsuo, and Genshiro Kitagawa. Methods and Applications of Signal Processing in Seismic Network Operations. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/bfb0117693.

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missing], [name. Methods and applications of signal processing in seismic network operations. Springer, 2003.

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Chapitres de livres sur le sujet "Signal processing for network security"

Wang, Wenting, Xin Liu, Xiaohong Zhao, Yang Zhao, Rui Wang, and Jianpo Li. "Design of Intelligent Substation Communication Network Security Audit System." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_48.

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Wang, Wenting, Guilin Huang, Xin Liu, Hao Zhang, Rui Wang, and Jianpo Li. "Research on Security Auditing Scheme of Intelligent Substation Communication Network." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_49.

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Sawlikar, Alka P., Z. J. Khan, and S. G. Akojwar. "Parametric Evaluation of Different Cryptographic Techniques for Enhancement of Energy Efficiency in Wireless Communication Network." In Intelligent Techniques in Signal Processing for Multimedia Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44790-2_9.

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Gao, Lifang, Zhihui Wang, Huifeng Yang, et al. "Network Security Situation Assessment of Power Information System Based on Improved Artificial Bee Colony Algorithm." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_42.

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Yang, Yong, Jilin Wang, Rong Li, and Jinxiong Zhao. "Review of Constructing the Early Warning and Diagnosis Information Database of Power Plant Network Security Events." In 3D Imaging Technologies—Multidimensional Signal Processing and Deep Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3180-1_37.

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Wu, Ji, Qilian Liang, Baoju Zhang, and Xiaorong Wu. "Security Analysis of Distributed Compressive Sensing-Based Wireless Sensor Networks." In The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00536-2_5.

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Benesty, Jacob, Tomas Gänsler, Dennis R. Morgan, M. Mohan Sondhi, and Steven L. Gay. "Dynamic Resource Allocation for Network Echo Cancellation." In Digital Signal Processing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04437-7_4.

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Marks, Friedrich, Ursula Klingmüller, and Karin Müller-Decker. "Supplying the Network with Energy: Basic Biochemistry of Signal Transduction." In Cellular Signal Processing. Garland Science, 2017. http://dx.doi.org/10.4324/9781315165479-2.

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Khattab, Ahmed, Zahra Jeddi, Esmaeil Amini, and Magdy Bayoumi. "RBS Security Analysis." In Analog Circuits and Signal Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47545-5_5.

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Campion, Sébastien, Julien Devigne, Céline Duguey, and Pierre-Alain Fouque. "Multi-Device for Signal." In Applied Cryptography and Network Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57878-7_9.

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Actes de conférences sur le sujet "Signal processing for network security"

Fok, Mable P., Konstantin Kravtsov, Yanhua Deng, Zhenxing Wang, and Paul R. Prucnal. "Providing Network Security with Optical Signal Processing." In 2008 IEEE PhotonicsGlobal@Singapore (IPGC). IEEE, 2008. http://dx.doi.org/10.1109/ipgc.2008.4781422.

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Cao, Zhanghua, Yuansheng Tang, and Xinmei Huang. "Against wiretappers without key-security is an intrinsic property of network coding." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397469.

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Jia, Fenggen, Weiming Wang, Ming Gao, and Chaoqi Lv. "A real-time rule-matching algorithm for the network security audit system." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397574.

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Khan, Jihas. "Vehicle network security testing." In 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS). IEEE, 2017. http://dx.doi.org/10.1109/ssps.2017.8071577.

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Ko, Hoon, Chang Choi, Pankoo Kim, and Junho Choi. "NETWORK SECURITY ARCHITECTURE AND APPLICATIONS BASED ON CONTEXT-AWARE SECURITY." In 7th International Conference on Signal Image Processing and Multimedia. AIRCC Publication, 2019. http://dx.doi.org/10.5121/csit.2019.90308.

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Gemilang Gultom, Rudy Agus, Tatan Kustana, and Romie Oktovianus Bura. "ENHANCING COMPUTER NETWORK SECURITY ENVIRONMENT BY IMPLEMENTING THE SIX-WARE NETWORK SECURITY FRAMEWORK (SWNSF)." In 7th International Conference on Signal, Image Processing and Pattern Recognition. AIRCC Publication Corporation, 2018. http://dx.doi.org/10.5121/csit.2018.81714.

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Du, Haoyuan, Meng Fan, and Liquan Dong. "Super-resolution network for x-ray security inspection." In OIT21: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, edited by Xun Cao, Chao Zuo, Wolfgang Osten, Guohai Situ, and Xiaopeng Shao. SPIE, 2022. http://dx.doi.org/10.1117/12.2616535.

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Do, Emily H., and Vijay N. Gadepally. "Classifying Anomalies for Network Security." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053419.

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Prasad, Jai Prakash, and S. C. Mohan. "Energy Efficient Dual Function Security Protocol for Wireless Sensor Network." In Second International Conference on Signal Processing, Image Processing and VLSI. Research Publishing Services, 2015. http://dx.doi.org/10.3850/978-981-09-6200-5_d-28.

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Oujezsky, Vaclav, David Chapcak, Tomas Horvath, and Petr Munster. "Security Testing Of Active Optical Network Devices." In 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2019. http://dx.doi.org/10.1109/tsp.2019.8768811.

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Rapports d'organisations sur le sujet "Signal processing for network security"

Tong, Lang. Network-Centric Distributed Signal Processing. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada519513.

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Friedman, Haya, Julia Vrebalov, and James Giovannoni. Elucidating the ripening signaling pathway in banana for improved fruit quality, shelf-life and food security. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7594401.bard.

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Background : Banana being a monocot and having distinct peel and pulp tissues is unique among the fleshy fruits and hence can provide a more comprehensive understanding of fruit ripening. Our previous research which translated ripening discoveries from tomato, led to the identification of six banana fruit-associated MADS-box genes, and we confirmed the positive role of MaMADS1/2 in banana ripening. The overall goal was to further elucidate the banana ripening signaling pathway as mediated by MADS-boxtranscriptional regulators. Specific objectives were: 1) characterize transcriptional profiles and quality of MaMADS1/2 repressed fruit; 2) reveal the role of additional MaMADSgenes in ripening; 3) develop a model of fruit MaMADS-box mode of action; and 4) isolate new components of the banana ripening signaling pathway. Major conclusion: The functions of the banana MaMADS1-5 have been examined by complimenting the rinor the TAGL1-suppressed lines of tomato. Only MaMADS5 exhibited partial complementation of TAGL1-suppressed and rinlines, suggesting that while similar genes play corresponding roles in ripening, evolutionary divergence makes heterologous complementation studies challenging. Nevertheless, the partial complementation of tomato TAGL1-surpessed and rinlines with MaMADS5 suggests this gene is likely an important ripening regulator in banana, worthy of further study. RNA-seqtranscriptome analysis during ripening was performed on WT and MaMADS2-suppressed lines revealing additional candidate genes contributing to ripening control mechanisms. In summary, we discovered 39 MaMADS-box genes in addition to homologues of CNR, NOR and HB-1 expressed in banana fruits, and which were shown in tomato to play necessary roles in ripening. For most of these genes the expression in peel and pulp was similar. However, a number of key genes were differentially expressed between these tissues indicating that the regulatory components which are active in peel and pulp include both common and tissue-specific regulatory systems, a distinction as compared to the more uniform tomato fruit pericarp. Because plant hormones are well documented to affect fruit ripening, the expressions of genes within the auxin, gibberellin, abscisic acid, jasmonic acid, salicylic and ethylene signal transduction and synthesis pathways were targeted in our transcriptome analysis. Genes’ expression associated with these pathways generally declined during normal ripening in both peel and pulp, excluding cytokinin and ethylene, and this decline was delayed in MaMADS2-suppressed banana lines. Hence, we suggest that normal MaMADS2 activity promotes the observed downward expression within these non-ethylene pathways (especially in the pulp), thus enabling ripening progression. In contrast, the expressions of ACSand ACOof the ethylene biosynthesis pathway increase in peel and pulp during ripening and are delayed/inhibited in the transgenic bananas, explaining the reduced ethylene production of MaMADS2-suppressed lines. Inferred by the different genes’ expression in peel and pulp of the gibberellins, salicylic acid and cytokinins pathways, it is suggested that hormonal regulation in these tissues is diverse. These results provide important insights into possible avenues of ripening control in the diverse fruit tissues of banana which was not previously revealed in other ripening systems. As such, our transcriptome analysis of WT and ripening delayed banana mutants provides a starting point for further characterization of ripening. In this study we also developed novel evidence that the cytoskeleton may have a positive role in ripening as components of this pathway were down-regulated by MaMADS2 suppression. The mode of cytoskeleton involvement in fruit ripening remains unclear but presents a novel new frontier in ripening investigations. In summary, this project yielded functional understanding of the role and mode of action of MaMADS2 during ripening, pointing to both induction of ethylene and suppression of non-ethylene hormonal singling pathways. Furthermore, our data suggest important roles for cytoskeleton components and MaMADS5 in the overall banana ripening control network. Implications: The project revealed new molecular components/genes involved in banana ripening and refines our understanding of ripening responses in the peel and pulp tissues of this important species. This information is novel as compared to that derived from the more uniform carpel tissues of other highly studied ripening systems including tomato and grape. The work provides specific target genes for potential modification through genetic engineering or for exploration of useful genetic diversity in traditional breeding. The results from the project might point toward improved methods or new treatments to improve banana fruit storage and quality.

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.

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Irudayaraj, Joseph, Ze'ev Schmilovitch, Amos Mizrach, Giora Kritzman, and Chitrita DebRoy. Rapid detection of food borne pathogens and non-pathogens in fresh produce using FT-IRS and raman spectroscopy. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7587221.bard.

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Rapid detection of pathogens and hazardous elements in fresh fruits and vegetables after harvest requires the use of advanced sensor technology at each step in the farm-to-consumer or farm-to-processing sequence. Fourier-transform infrared (FTIR) spectroscopy and the complementary Raman spectroscopy, an advanced optical technique based on light scattering will be investigated for rapid and on-site assessment of produce safety. Paving the way toward the development of this innovative methodology, specific original objectives were to (1) identify and distinguish different serotypes of Escherichia coli, Listeria monocytogenes, Salmonella typhimurium, and Bacillus cereus by FTIR and Raman spectroscopy, (2) develop spectroscopic fingerprint patterns and detection methodology for fungi such as Aspergillus, Rhizopus, Fusarium, and Penicillium (3) to validate a universal spectroscopic procedure to detect foodborne pathogens and non-pathogens in food systems. The original objectives proposed were very ambitious hence modifications were necessary to fit with the funding. Elaborate experiments were conducted for sensitivity, additionally, testing a wide range of pathogens (more than selected list proposed) was also necessary to demonstrate the robustness of the instruments, most crucially, algorithms for differentiating a specific organism of interest in mixed cultures was conceptualized and validated, and finally neural network and chemometric models were tested on a variety of applications. Food systems tested were apple juice and buffer systems. Pathogens tested include Enterococcus faecium, Salmonella enteritidis, Salmonella typhimurium, Bacillus cereus, Yersinia enterocolitis, Shigella boydii, Staphylococus aureus, Serratiamarcescens, Pseudomonas vulgaris, Vibrio cholerae, Hafniaalvei, Enterobacter cloacae, Enterobacter aerogenes, E. coli (O103, O55, O121, O30 and O26), Aspergillus niger (NRRL 326) and Fusarium verticilliodes (NRRL 13586), Saccharomyces cerevisiae (ATCC 24859), Lactobacillus casei (ATCC 11443), Erwinia carotovora pv. carotovora and Clavibacter michiganense. Sensitivity of the FTIR detection was 103CFU/ml and a clear differentiation was obtained between the different organisms both at the species as well as at the strain level for the tested pathogens. A very crucial step in the direction of analyzing mixed cultures was taken. The vector based algorithm was able to identify a target pathogen of interest in a mixture of up to three organisms. Efforts will be made to extend this to 10-12 key pathogens. The experience gained was very helpful in laying the foundations for extracting the true fingerprint of a specific pathogen irrespective of the background substrate. This is very crucial especially when experimenting with solid samples as well as complex food matrices. Spectroscopic techniques, especially FTIR and Raman methods are being pursued by agencies such as DARPA and Department of Defense to combat homeland security. Through the BARD US-3296-02 feasibility grant, the foundations for detection, sample handling, and the needed algorithms and models were developed. Successive efforts will be made in transferring the methodology to fruit surfaces and to other complex food matrices which can be accomplished with creative sampling methods and experimentation. Even a marginal success in this direction will result in a very significant breakthrough because FTIR and Raman methods, in spite of their limitations are still one of most rapid and nondestructive methods available. Continued interest and efforts in improving the components as well as the refinement of the procedures is bound to result in a significant breakthrough in sensor technology for food safety and biosecurity.

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Federal Information Processing Standards Publication: guideline for the analysis of local area network security. National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.fips.191.

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