Pub Date : 2022-01-17DOI: 10.46300/9106.2022.16.86
K. Y. Annapurna, Deepali Koppad
IoT (Internet of Things) has been expanding into various business activities and people’s lives; however, IoT devices face security challenges. Further, the establishment of reliable security for IoT constrained devices is considered to be ongoing research due to several factors such as device cost, implementation area, power consumption, and so on. In addition to these factors, hardware security also poses major challenges like above mentioned; further Physical Unclonable Functions (PUFs) offer a promising solution for the authentication of IoT devices as they provide unique fingerprints for the underlying devices through their challenge-response pairs. However, PUFs are vulnerable to modelling attacks; in this research work, a lightweight hardware security framework is designed that provides the security for light edge devices. The proposed hardware security framework introduces the three-step optimized approach to offer a secure and reliable solution for IoT device authentication. The first step deals with the designing of SP-PUF, the second step deals with introducing obfuscation technique into the same, and the third step deals with introducing the authentication mechanism. A machine learning attack is designed to evaluate the model and the proposed model is evaluated considering the different stages. This research work is evaluated in two parts; the first part of the evaluation is carried out for the security mechanism through machine learning algorithm attack i.e., logistic regression, Neural Network, and SVM; further evaluation is carried out considering the PUF evaluation parameter as uniqueness and reliability. At last, comparative analysis suggest that proposed hardware security framework is safe against the machine learning attacks and achieves high reliability and optimal uniqueness.
IoT (Internet of Things，物联网)已经扩展到各种商业活动和人们的生活中;然而，物联网设备面临着安全挑战。此外，由于设备成本、实施面积、功耗等因素，为物联网受限设备建立可靠的安全性被认为是正在进行的研究。除了这些因素，硬件安全也带来了如上所述的主要挑战;进一步的物理不可克隆功能(puf)为物联网设备的身份验证提供了一个很有前途的解决方案，因为它们通过其挑战-响应对为底层设备提供了唯一的指纹。然而，puf很容易受到建模攻击;本研究设计了一个轻量级的硬件安全框架，为轻边缘设备提供安全保障。提出的硬件安全框架引入了三步优化方法，为物联网设备认证提供安全可靠的解决方案。第一步是SP-PUF的设计，第二步是引入混淆技术，第三步是引入认证机制。设计了一种机器学习攻击来评估模型，并考虑不同的阶段对所提出的模型进行评估。本研究工作分为两部分进行评价;第一部分通过机器学习算法攻击，即逻辑回归、神经网络和支持向量机，对安全机制进行评估;考虑PUF评价参数的唯一性和可靠性进行进一步评价。最后，对比分析表明，所提出的硬件安全框架能够抵御机器学习攻击，具有较高的可靠性和最优唯一性。
{"title":"A Lightweight Hardware Secure and Reliable Framework using Secure and Provable PUF for IoT Devices against the Machine Learning Attack","authors":"K. Y. Annapurna, Deepali Koppad","doi":"10.46300/9106.2022.16.86","DOIUrl":"https://doi.org/10.46300/9106.2022.16.86","url":null,"abstract":"IoT (Internet of Things) has been expanding into various business activities and people’s lives; however, IoT devices face security challenges. Further, the establishment of reliable security for IoT constrained devices is considered to be ongoing research due to several factors such as device cost, implementation area, power consumption, and so on. In addition to these factors, hardware security also poses major challenges like above mentioned; further Physical Unclonable Functions (PUFs) offer a promising solution for the authentication of IoT devices as they provide unique fingerprints for the underlying devices through their challenge-response pairs. However, PUFs are vulnerable to modelling attacks; in this research work, a lightweight hardware security framework is designed that provides the security for light edge devices. The proposed hardware security framework introduces the three-step optimized approach to offer a secure and reliable solution for IoT device authentication. The first step deals with the designing of SP-PUF, the second step deals with introducing obfuscation technique into the same, and the third step deals with introducing the authentication mechanism. A machine learning attack is designed to evaluate the model and the proposed model is evaluated considering the different stages. This research work is evaluated in two parts; the first part of the evaluation is carried out for the security mechanism through machine learning algorithm attack i.e., logistic regression, Neural Network, and SVM; further evaluation is carried out considering the PUF evaluation parameter as uniqueness and reliability. At last, comparative analysis suggest that proposed hardware security framework is safe against the machine learning attacks and achieves high reliability and optimal uniqueness.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90178272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-14DOI: 10.46300/9106.2021.15.197
T. Tsuzuki, S. Ogata, R. Kobayashi, Masayuki Uranagase, Seiya Shimoi, Saki Tsujimoto
BaTiO3 is one of the well-known ferroelectric and piezoelectric materials, which has been widely used in various devices. However, the microscopic mechanism of the ferroelectric domain growth is not understood well. We investigated the effects of point defects, mono- and di-vacancies of Ba, Ti, and O, on the domain growth of BaTiO3 using molecular dynamics simulation with the core-shell inter-atomic potential. We found the following: ｓ(1) One kind of monovacancy, VO1, located on the TiO plane perpendicular to the applied electric field direction, acts to hinder the polarization inversion induced by the applied electric field. The monopole electric field produced by VO1 either hinders or assists the local polarization inversion in accordance with the local intensity of the total electric field. (2) The 1st-neighbor divacancies VBa-VO and VTi-VO as compared to the 2nd-neighbor divacancies asymmetrically affect the domain growth with respect to the applied electric field, making the hysteresis behavior of applied electric field vs. polarization relation. The domain grows even at a small electric field when the directions of the applied electric field and the divacancy dipole are mutually the same. (3) The domain growth speed towards the applied electric field direction is about 2 orders of magnitude higher than that towards the perpendicular direction.
{"title":"Simulation Analysis of Effect of Vacancies on Ferroic Domain Growth of BaTiO^3","authors":"T. Tsuzuki, S. Ogata, R. Kobayashi, Masayuki Uranagase, Seiya Shimoi, Saki Tsujimoto","doi":"10.46300/9106.2021.15.197","DOIUrl":"https://doi.org/10.46300/9106.2021.15.197","url":null,"abstract":"BaTiO3 is one of the well-known ferroelectric and piezoelectric materials, which has been widely used in various devices. However, the microscopic mechanism of the ferroelectric domain growth is not understood well. We investigated the effects of point defects, mono- and di-vacancies of Ba, Ti, and O, on the domain growth of BaTiO3 using molecular dynamics simulation with the core-shell inter-atomic potential. We found the following: ｓ(1) One kind of monovacancy, VO1, located on the TiO plane perpendicular to the applied electric field direction, acts to hinder the polarization inversion induced by the applied electric field. The monopole electric field produced by VO1 either hinders or assists the local polarization inversion in accordance with the local intensity of the total electric field. (2) The 1st-neighbor divacancies VBa-VO and VTi-VO as compared to the 2nd-neighbor divacancies asymmetrically affect the domain growth with respect to the applied electric field, making the hysteresis behavior of applied electric field vs. polarization relation. The domain grows even at a small electric field when the directions of the applied electric field and the divacancy dipole are mutually the same. (3) The domain growth speed towards the applied electric field direction is about 2 orders of magnitude higher than that towards the perpendicular direction.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79124113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present paper addresses a precise and an accurate mathematical model for three-phase squirrel cage induction motors, based on winding function theory. Through an analytical development, a comparative way is presented to separate the signature between the existence of the outer race bearing fault and the static eccentricity concerning the asymmetry of the air gap between the stator and the rotor. This analytical model proposes an effective signature of outer race defect separately from other signatures of static eccentricity. Simulation and experimental results are presented to validate the proposed analytical model.
{"title":"Analytical Model for Separated Frequency Signature of Outer Race Bearing Fault From Static Eccentricity","authors":"Touil Abderrahim, Babaa Fatima, Bennis Ouafae, Kratz Frédéric","doi":"10.46300/9106.2021.15.196","DOIUrl":"https://doi.org/10.46300/9106.2021.15.196","url":null,"abstract":"The present paper addresses a precise and an accurate mathematical model for three-phase squirrel cage induction motors, based on winding function theory. Through an analytical development, a comparative way is presented to separate the signature between the existence of the outer race bearing fault and the static eccentricity concerning the asymmetry of the air gap between the stator and the rotor. This analytical model proposes an effective signature of outer race defect separately from other signatures of static eccentricity. Simulation and experimental results are presented to validate the proposed analytical model.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83107113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.46300/9106.2022.16.65
Shihui Li
The distribution optimization of WSN nodes is one of the key issues in WSN research, and also is a research hotspot in the field of communication. Aiming at the distribution optimization of WSN nodes, the distribution optimization scheme of nodes based on improved invasive weed optimization algorithm(IIWO) is proposed. IIWO improves the update strategy of the initial position of weeds by using cubic mapping chaotic operator, and uses the Gauss mutation operator to increase the diversity of the population. The simulation results show that the algorithm proposed in this paper has a higher solution quality and faster convergence speed than IWO and CPSO. In distribution optimization example of WSN nodes, the optimal network coverage rate obtained by IIWO is respectively improved by 1.82% and 0.93% than the IWO and CPSO. Under the condition of obtaining the same network coverage rate, the number of nodes required by IIWO is fewer.
{"title":"An Improved Intrusion Weed Optimization Algorithm for Node Location in Wireless Sensor Networks","authors":"Shihui Li","doi":"10.46300/9106.2022.16.65","DOIUrl":"https://doi.org/10.46300/9106.2022.16.65","url":null,"abstract":"The distribution optimization of WSN nodes is one of the key issues in WSN research, and also is a research hotspot in the field of communication. Aiming at the distribution optimization of WSN nodes, the distribution optimization scheme of nodes based on improved invasive weed optimization algorithm(IIWO) is proposed. IIWO improves the update strategy of the initial position of weeds by using cubic mapping chaotic operator, and uses the Gauss mutation operator to increase the diversity of the population. The simulation results show that the algorithm proposed in this paper has a higher solution quality and faster convergence speed than IWO and CPSO. In distribution optimization example of WSN nodes, the optimal network coverage rate obtained by IIWO is respectively improved by 1.82% and 0.93% than the IWO and CPSO. Under the condition of obtaining the same network coverage rate, the number of nodes required by IIWO is fewer.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73888201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.46300/9106.2022.16.83
N. Assimakis, M. Adam, Christos Massouros
In this paper a distributed implementation for the periodic steady state Kalman filter is proposed. The distributed algorithm has parallel structure and can be implemented using processors in parallel without idle time. The number of processors is equal to the model period. The resulting speedup is also derived. The Finite Impulse Response (FIR) form of the periodic steady state Kalman filter is derived.
{"title":"Distributed Periodic Steady State Kalman Filter","authors":"N. Assimakis, M. Adam, Christos Massouros","doi":"10.46300/9106.2022.16.83","DOIUrl":"https://doi.org/10.46300/9106.2022.16.83","url":null,"abstract":"In this paper a distributed implementation for the periodic steady state Kalman filter is proposed. The distributed algorithm has parallel structure and can be implemented using processors in parallel without idle time. The number of processors is equal to the model period. The resulting speedup is also derived. The Finite Impulse Response (FIR) form of the periodic steady state Kalman filter is derived.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85442128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Transcranial magnetic-acoustic electrical stimulation (TMAES) is a new technology with ultrasonic waves and a static magnetic field to generate an electric current in nerve tissues to modulate neuronal firing activities. The existing neuron models only simulate a single neuron, and there are few studies on coupled neurons models about TMAES. Most of the neurons in the cerebral cortex are not isolated but are coupled to each other. It is necessary to study the information transmission of coupled neurons. The types of neuron coupled synapses include electrical synapse and chemical synapse. A neuron model without considering chemical synapses is not comprehensive. Here, we modified the Hindmarsh-Rose (HR) model to simulate the smallest nervous system—two neurons coupled electrical synapses and chemical synapses under TMAES. And the environmental variables describing the synaptic coupling between two neurons and the nonlinearity of the nervous system are also taken into account. The firing behavior of the nervous system can be modulated by changing the intensity or the modulation frequency. The results show that within a certain range of parameters, the discharge frequency of coupled neurons could be increased by altering the modulation frequency, and intensity of stimulation, modulating the excitability of neurons, reducing the response time of chemical postsynaptic neurons, and accelerating the information transferring. Moreover, the discharge frequency of neurons was selective to stimulus parameters. These results demonstrate the possible theoretical regulatory mechanism of the neurons' firing frequency characteristics by TMAES. The study establishes the foundation for large-scale neural network modeling and can be taken as the theoretical basis for TMAES experimental and clinical application.
{"title":"Theoretical Analysis of Coupled Modified Hindmarsh-rose Model Under Transcranial Magnetic-acoustic Electrical Stimulation","authors":"Liang Guo, Shuai Zhang, Jian-kang Wu, Xinyu Gao, Mingkang Zhao, Guizhi Xu","doi":"10.46300/9106.2022.16.76","DOIUrl":"https://doi.org/10.46300/9106.2022.16.76","url":null,"abstract":"Transcranial magnetic-acoustic electrical stimulation (TMAES) is a new technology with ultrasonic waves and a static magnetic field to generate an electric current in nerve tissues to modulate neuronal firing activities. The existing neuron models only simulate a single neuron, and there are few studies on coupled neurons models about TMAES. Most of the neurons in the cerebral cortex are not isolated but are coupled to each other. It is necessary to study the information transmission of coupled neurons. The types of neuron coupled synapses include electrical synapse and chemical synapse. A neuron model without considering chemical synapses is not comprehensive. Here, we modified the Hindmarsh-Rose (HR) model to simulate the smallest nervous system—two neurons coupled electrical synapses and chemical synapses under TMAES. And the environmental variables describing the synaptic coupling between two neurons and the nonlinearity of the nervous system are also taken into account. The firing behavior of the nervous system can be modulated by changing the intensity or the modulation frequency. The results show that within a certain range of parameters, the discharge frequency of coupled neurons could be increased by altering the modulation frequency, and intensity of stimulation, modulating the excitability of neurons, reducing the response time of chemical postsynaptic neurons, and accelerating the information transferring. Moreover, the discharge frequency of neurons was selective to stimulus parameters. These results demonstrate the possible theoretical regulatory mechanism of the neurons' firing frequency characteristics by TMAES. The study establishes the foundation for large-scale neural network modeling and can be taken as the theoretical basis for TMAES experimental and clinical application.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73705087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.46300/9106.2022.16.62
Yahao Zhang, Jin Pang, H. Yin
Mail transmission was not only the main function of information system, but also the main way of network virus and Trojan horse transmission, which has a key impact on the running state of information. In order to deal with the threats of network viruses and Trojans and improve the level of e-mail management, this paper studies the filtering of information system, and proposes a phishing e-mail filtering method based on Improved Bayesian model. MATLAB simulation results show that the consistency p between the amount of data sent by e-mail and the amount received is good, the consistency rate reached 92.3%. the data security level is 95%, encryption proportion / data proportion ratio under Bayesian optimization are higher than those of unfiltered method，which up to 97.2%. Therefore, the Bayesian optimization model constructed in this paper can meet the needs of phishing email filtering in information communication at this stage.
{"title":"The Optimization Analysis of Phishing Email Filtering in Network Fraud based on Improved Bayesian Algorithm","authors":"Yahao Zhang, Jin Pang, H. Yin","doi":"10.46300/9106.2022.16.62","DOIUrl":"https://doi.org/10.46300/9106.2022.16.62","url":null,"abstract":"Mail transmission was not only the main function of information system, but also the main way of network virus and Trojan horse transmission, which has a key impact on the running state of information. In order to deal with the threats of network viruses and Trojans and improve the level of e-mail management, this paper studies the filtering of information system, and proposes a phishing e-mail filtering method based on Improved Bayesian model. MATLAB simulation results show that the consistency p between the amount of data sent by e-mail and the amount received is good, the consistency rate reached 92.3%. the data security level is 95%, encryption proportion / data proportion ratio under Bayesian optimization are higher than those of unfiltered method，which up to 97.2%. Therefore, the Bayesian optimization model constructed in this paper can meet the needs of phishing email filtering in information communication at this stage.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82922566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.46300/9106.2022.16.82
Binesh Thankappan
Riemann zeta is defined as a function of a complex variable that analytically continues the sum of the Dirichlet series, when the real part is greater than unity. In this paper, the Riemann zeta associated with the finite energy possessed by a 2mm radius, free falling water droplet, crashing into a plane is considered. A modified zeta function is proposed which is incorporated to the spherical coordinates and real analysis has been performed. Through real analytic continuation, the single point of contact of the drop at the instant of touching the plane is analyzed. The zeta function is extracted at the point of destruction of the drop, where it defines a unique real function. A special property is assumed for some continuous functions, where the function’s first derivative and first integral combine together to a nullity at all points. Approximate reverse synthesis of such a function resulted in a special waveform named the dying-surge. Extending the proposed concept to general continuous real functions resulted in the synthesis of the corresponding function’s Dying-surge model. The Riemann zeta function associated with the water droplet can also be modeled as a dying–surge. The Dying- surge model corresponds to an electrical squeezing or compression of a waveform, which was originally defined over infinite arguments, squeezed to a finite number of values for arguments placed very close together with defined final and penultimate values. Synthesized results using simulation software are also presented, along with the analysis. The presence of surges in electrical circuits will correspond to electrical compression of some unknown continuous, real current or voltage function and the method can be used to estimate the original unknown function.
{"title":"Riemann Zeta Based Surge Modelling of Continuous Real Functions in Electrical Circuits","authors":"Binesh Thankappan","doi":"10.46300/9106.2022.16.82","DOIUrl":"https://doi.org/10.46300/9106.2022.16.82","url":null,"abstract":"Riemann zeta is defined as a function of a complex variable that analytically continues the sum of the Dirichlet series, when the real part is greater than unity. In this paper, the Riemann zeta associated with the finite energy possessed by a 2mm radius, free falling water droplet, crashing into a plane is considered. A modified zeta function is proposed which is incorporated to the spherical coordinates and real analysis has been performed. Through real analytic continuation, the single point of contact of the drop at the instant of touching the plane is analyzed. The zeta function is extracted at the point of destruction of the drop, where it defines a unique real function. A special property is assumed for some continuous functions, where the function’s first derivative and first integral combine together to a nullity at all points. Approximate reverse synthesis of such a function resulted in a special waveform named the dying-surge. Extending the proposed concept to general continuous real functions resulted in the synthesis of the corresponding function’s Dying-surge model. The Riemann zeta function associated with the water droplet can also be modeled as a dying–surge. The Dying- surge model corresponds to an electrical squeezing or compression of a waveform, which was originally defined over infinite arguments, squeezed to a finite number of values for arguments placed very close together with defined final and penultimate values. Synthesized results using simulation software are also presented, along with the analysis. The presence of surges in electrical circuits will correspond to electrical compression of some unknown continuous, real current or voltage function and the method can be used to estimate the original unknown function.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83899482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.46300/9106.2022.16.84
Vanya Ivanova
In this paper a new neural model for detection of multiple network IoT-based attacks, such as DDoS TCP, UDP, and HHTP flood, is presented. It consists of feedforward multilayer network with back propagation. A general algorithm for its optimization during training is proposed, leading to proper number of neurons in the hidden layers. The Scaled Gradient Descent algorithm and the Adam optimization are studied with better classification results, obtained by the developed classifiers, using the latter. Tangent hyperbolic function appears to be proper selection for the hidden neurons. Two sets of features, gathered from aggregated records of the network traffic, are tested, containing 8 and 10 components. While more accurate results are obtained for the 10-feature set, the 8-feature set offers twice lower training time and seems applicable for real-world applications. The detection rate for 7 of 10 different network attacks, primarily various types of floods, is higher than 90% and for 3 of them – mainly reconnaissance and keylogging activities with low intensity of the generated traffic, deviates between 57% and 68%. The classifier is considered applicable for industrial implementation.
{"title":"Multiple IoT based Network Attacks Discrimination by Multilayer Feedforward Neural Networks","authors":"Vanya Ivanova","doi":"10.46300/9106.2022.16.84","DOIUrl":"https://doi.org/10.46300/9106.2022.16.84","url":null,"abstract":"In this paper a new neural model for detection of multiple network IoT-based attacks, such as DDoS TCP, UDP, and HHTP flood, is presented. It consists of feedforward multilayer network with back propagation. A general algorithm for its optimization during training is proposed, leading to proper number of neurons in the hidden layers. The Scaled Gradient Descent algorithm and the Adam optimization are studied with better classification results, obtained by the developed classifiers, using the latter. Tangent hyperbolic function appears to be proper selection for the hidden neurons. Two sets of features, gathered from aggregated records of the network traffic, are tested, containing 8 and 10 components. While more accurate results are obtained for the 10-feature set, the 8-feature set offers twice lower training time and seems applicable for real-world applications. The detection rate for 7 of 10 different network attacks, primarily various types of floods, is higher than 90% and for 3 of them – mainly reconnaissance and keylogging activities with low intensity of the generated traffic, deviates between 57% and 68%. The classifier is considered applicable for industrial implementation.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91191643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.46300/9106.2022.16.61
H. Xiang, Anrong Wang, Guoqun Fu, Xue Luo, Xudong Pan
PMH (psychiatry/mental health) is affected by many factors, among which there are numerous connections, so the prediction of PMH is a nonlinear problem. In this paper, BPNN (Back Propagation Neural Network) is applied to fuzzy clustering analysis and prediction of PMH data, and the rules and characteristics of PMH and behavioral characteristics of people with mental disorders are analyzed, and various internal relations among psychological test data are mined, thus providing scientific basis for establishing and perfecting early prevention and intervention of mental disorders in colleges and universities. Artificial neural network is a mathematical model of information processing, which is composed of synapses similar to the structure of brain neurons. The fuzzy clustering analysis and data prediction ability of optimized PMH data are obviously improved. Applying BPNN to the fuzzy clustering analysis and prediction of PMH data, analyzing the rules and characteristics of PMH and the behavioral characteristics of patients with mental disorders, can explore various internal relations among psychological test data, and provide scientific basis for establishing early prevention and intervention of mental disorders.
{"title":"Fuzzy Cluster Analysis and Prediction of Psychiatric Health Data Based on BPNN","authors":"H. Xiang, Anrong Wang, Guoqun Fu, Xue Luo, Xudong Pan","doi":"10.46300/9106.2022.16.61","DOIUrl":"https://doi.org/10.46300/9106.2022.16.61","url":null,"abstract":"PMH (psychiatry/mental health) is affected by many factors, among which there are numerous connections, so the prediction of PMH is a nonlinear problem. In this paper, BPNN (Back Propagation Neural Network) is applied to fuzzy clustering analysis and prediction of PMH data, and the rules and characteristics of PMH and behavioral characteristics of people with mental disorders are analyzed, and various internal relations among psychological test data are mined, thus providing scientific basis for establishing and perfecting early prevention and intervention of mental disorders in colleges and universities. Artificial neural network is a mathematical model of information processing, which is composed of synapses similar to the structure of brain neurons. The fuzzy clustering analysis and data prediction ability of optimized PMH data are obviously improved. Applying BPNN to the fuzzy clustering analysis and prediction of PMH data, analyzing the rules and characteristics of PMH and the behavioral characteristics of patients with mental disorders, can explore various internal relations among psychological test data, and provide scientific basis for establishing early prevention and intervention of mental disorders.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90585026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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