Pub Date : 2022-01-10DOI: 10.46300/9106.2022.16.77
Xianwen Zhou, Chaoyang Gu, Yuyu Sun, Che Han, W. Gu, Wangqiang Niu
With the development of various physical industries, people pay more attention to reliability tests and test equipment. To solve the problem of making maintenance strategy of an environmental test chamber for reliability test, a periodic preventive maintenance strategy based on RCM(Reliability Centre Maintenance) is proposed. Firstly, a multi-objective optimization model of reliability and maintenance cost is established by combining reliability theory and life distribution theory, and two objectives of equipment reliability and maintenance cost are considered. Secondly, the actual environmental test chamber fault maintenance data is analyzed, and it is found the fault distribution meets the dual parameter Weibull. Finally, the particle swarm optimization algorithm is used to solve the multi-objective model optimization, and a series of Pareto optimal solutions are obtained, that is, the number of maintenance times and the corresponding time interval in the operation cycle of the environmental test chamber, and these solutions might be good references for maintenance management personnel.
随着各种物理工业的发展，人们越来越重视可靠性测试和测试设备。针对可靠性试验环境试验箱维护策略的制定问题，提出了一种基于RCM(reliability Centre maintenance)的定期预防性维护策略。首先，结合可靠性理论和寿命分布理论，建立了设备可靠性和维修费用的多目标优化模型，同时考虑了设备可靠性和维修费用两个目标;其次，对实际环境试验室故障维修数据进行分析，发现故障分布符合双参数威布尔。最后，利用粒子群优化算法求解多目标模型优化，得到了一系列Pareto最优解，即环境试验箱在运行周期内的维修次数和相应的时间间隔，这些解可供维修管理人员参考。
{"title":"Preventive Maintenance Strategy of Environmental Test Chamber Based on Particle Swarm Optimization Algorithm","authors":"Xianwen Zhou, Chaoyang Gu, Yuyu Sun, Che Han, W. Gu, Wangqiang Niu","doi":"10.46300/9106.2022.16.77","DOIUrl":"https://doi.org/10.46300/9106.2022.16.77","url":null,"abstract":"With the development of various physical industries, people pay more attention to reliability tests and test equipment. To solve the problem of making maintenance strategy of an environmental test chamber for reliability test, a periodic preventive maintenance strategy based on RCM(Reliability Centre Maintenance) is proposed. Firstly, a multi-objective optimization model of reliability and maintenance cost is established by combining reliability theory and life distribution theory, and two objectives of equipment reliability and maintenance cost are considered. Secondly, the actual environmental test chamber fault maintenance data is analyzed, and it is found the fault distribution meets the dual parameter Weibull. Finally, the particle swarm optimization algorithm is used to solve the multi-objective model optimization, and a series of Pareto optimal solutions are obtained, that is, the number of maintenance times and the corresponding time interval in the operation cycle of the environmental test chamber, and these solutions might be good references for maintenance management personnel.","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":"85505788","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: 10.46300/9106.2022.16.78
N. Shylashree, M. A. Naik, A. Mamatha, V. Sridhar
Image processing is an important task in data processing systems for applications such as medical sectors, remote sensing, and microscopy tomography. Edge recognition is a sort of image division method that is used to simplify the image records so as to reduce the amount of data to be processed. Edges are considered the most important in image processing because they are used to characterize the boundaries of an image. The performance of the Canny edge recognition algorithm remarkably surpasses the present edge recognition technology in various computer visualization methods. The main drawback of using Canny edge boundary is that it consumes lot of period due to its complex computation. In order to tackle this problem a hybrid edge recognition method is proposed in block stage to locate edges with no loss. It employs the Sobel operator estimate method to calculate the value and direction of the gradient by substituting complex processes by hardware cost savings, traditional non-maximum suppression adaptive thresholding block organization, and conventional hysteresis thresholding. Pipeline was presented to lessen latency. The planned strategy is simulated using Xilinx ISE Design Suite14.2 running on a Xilinx Spartan-6 FPGA board. The synthesized architecture uses less hardware to detect edges and operates at maximum frequency of 935 MHz.
图像处理是医疗、遥感和显微断层扫描等应用的数据处理系统中的重要任务。边缘识别是一种图像分割方法，用于简化图像记录，以减少需要处理的数据量。边缘被认为是图像处理中最重要的，因为它们用来表征图像的边界。在各种计算机可视化方法中，Canny边缘识别算法的性能明显优于现有的边缘识别技术。使用Canny边缘边界的主要缺点是计算复杂，耗费大量的周期。为了解决这一问题，提出了一种分块阶段的混合边缘识别方法来无损失地定位边缘。该算法采用Sobel算子估计方法，通过节省硬件成本、传统的非最大抑制自适应阈值块组织和传统的滞后阈值来代替复杂的过程，计算梯度的值和方向。管道的出现是为了减少延迟。使用Xilinx ISE Design Suite14.2在Xilinx Spartan-6 FPGA板上对规划的策略进行了模拟。综合架构使用较少的硬件来检测边缘，并在935 MHz的最高频率下工作。
{"title":"Design and Implementation of Image Edge Detection Algorithm on FPGA","authors":"N. Shylashree, M. A. Naik, A. Mamatha, V. Sridhar","doi":"10.46300/9106.2022.16.78","DOIUrl":"https://doi.org/10.46300/9106.2022.16.78","url":null,"abstract":"Image processing is an important task in data processing systems for applications such as medical sectors, remote sensing, and microscopy tomography. Edge recognition is a sort of image division method that is used to simplify the image records so as to reduce the amount of data to be processed. Edges are considered the most important in image processing because they are used to characterize the boundaries of an image. The performance of the Canny edge recognition algorithm remarkably surpasses the present edge recognition technology in various computer visualization methods. The main drawback of using Canny edge boundary is that it consumes lot of period due to its complex computation. In order to tackle this problem a hybrid edge recognition method is proposed in block stage to locate edges with no loss. It employs the Sobel operator estimate method to calculate the value and direction of the gradient by substituting complex processes by hardware cost savings, traditional non-maximum suppression adaptive thresholding block organization, and conventional hysteresis thresholding. Pipeline was presented to lessen latency. The planned strategy is simulated using Xilinx ISE Design Suite14.2 running on a Xilinx Spartan-6 FPGA board. The synthesized architecture uses less hardware to detect edges and operates at maximum frequency of 935 MHz.","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":"89276151","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}
This paper studies the problems of load balancing and flow control in data center network, and analyzes several common flow control schemes in data center intelligent network and their existing problems. On this basis, the network traffic control problem is modeled with the goal of deep reinforcement learning strategy optimization, and an intelligent network traffic control method based on deep reinforcement learning is proposed. At the same time, for the flow control order problem in deep reinforcement learning algorithm, a flow scheduling priority algorithm is proposed innovatively. According to the decision output, the corresponding flow control and control are carried out, so as to realize the load balance of the network. Finally, experiments show, the network traffic bandwidth loss rate of the proposed intelligent network traffic control method is low. Under the condition of random 60 traffic density, the average bisection bandwidth obtained by the proposed intelligent network traffic control method is 4.0mbps and the control error rate is 2.25%. The intelligent network traffic control method based on deep reinforcement learning has high practicability in the practical application process, and fully meets the research requirements.
{"title":"Intelligent Network Traffic Control Based on Deep Reinforcement Learning","authors":"Fei Wu, Ting Li, Fucai Luo, ShuLin Wu, Chuanqi Xiao","doi":"10.46300/9106.2022.16.73","DOIUrl":"https://doi.org/10.46300/9106.2022.16.73","url":null,"abstract":"This paper studies the problems of load balancing and flow control in data center network, and analyzes several common flow control schemes in data center intelligent network and their existing problems. On this basis, the network traffic control problem is modeled with the goal of deep reinforcement learning strategy optimization, and an intelligent network traffic control method based on deep reinforcement learning is proposed. At the same time, for the flow control order problem in deep reinforcement learning algorithm, a flow scheduling priority algorithm is proposed innovatively. According to the decision output, the corresponding flow control and control are carried out, so as to realize the load balance of the network. Finally, experiments show, the network traffic bandwidth loss rate of the proposed intelligent network traffic control method is low. Under the condition of random 60 traffic density, the average bisection bandwidth obtained by the proposed intelligent network traffic control method is 4.0mbps and the control error rate is 2.25%. The intelligent network traffic control method based on deep reinforcement learning has high practicability in the practical application process, and fully meets the research requirements.","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":"77304883","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: 10.46300/9106.2022.16.74
Jiejie Cui, Xiang Li, Yang Wang
The traditional encrypted storage system is inefficient when it encrypts the data of the Internet of Things, and there are few IOT data nodes that can be encrypted in a short time. In order to solve the above problems, a new Internet of Things data effective information encryption storage system is proposed. The hardware and software of the system are mainly designed. The chip selected for the collector is TTSAD251, which can expand the collection range. The processor is set with multiple cores to reduce the system power consumption. The memory uses SPRTAN-2 chip as the structure chip. The software work consists of three parts: collecting effective information of Internet of Things big data, establishing encrypted documents and storing effective information of big data of Internet of Things. In order to detect the working effect of the system, the experimental comparison with the traditional system shows that the proposed encryption storage system can improve the storage range of big data effective information of the Internet of Things by 20.58%, and the work efficiency by 5.64%. Compared with the traditional system, the designed system also has obvious advantages in the number of big data node secrets. In different files, the average number of big data information node encryption in this system is about 166,700. The experimental data show that the designed system has ideal application performance and provides a reliable basis for related fields.
{"title":"Design of E-commerce Data Scalable Storage System Based on Mobile Internet Communication Technology","authors":"Jiejie Cui, Xiang Li, Yang Wang","doi":"10.46300/9106.2022.16.74","DOIUrl":"https://doi.org/10.46300/9106.2022.16.74","url":null,"abstract":"The traditional encrypted storage system is inefficient when it encrypts the data of the Internet of Things, and there are few IOT data nodes that can be encrypted in a short time. In order to solve the above problems, a new Internet of Things data effective information encryption storage system is proposed. The hardware and software of the system are mainly designed. The chip selected for the collector is TTSAD251, which can expand the collection range. The processor is set with multiple cores to reduce the system power consumption. The memory uses SPRTAN-2 chip as the structure chip. The software work consists of three parts: collecting effective information of Internet of Things big data, establishing encrypted documents and storing effective information of big data of Internet of Things. In order to detect the working effect of the system, the experimental comparison with the traditional system shows that the proposed encryption storage system can improve the storage range of big data effective information of the Internet of Things by 20.58%, and the work efficiency by 5.64%. Compared with the traditional system, the designed system also has obvious advantages in the number of big data node secrets. In different files, the average number of big data information node encryption in this system is about 166,700. The experimental data show that the designed system has ideal application performance and provides a reliable basis for related fields.","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":"82976215","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: 10.46300/9106.2022.16.63
Wei Li, Wei Hu, Kun Hu, Qiang Qin
The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.
{"title":"Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research","authors":"Wei Li, Wei Hu, Kun Hu, Qiang Qin","doi":"10.46300/9106.2022.16.63","DOIUrl":"https://doi.org/10.46300/9106.2022.16.63","url":null,"abstract":"The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.","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":"79701508","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: 10.46300/9106.2022.16.69
K. Vanitha, Viswanath Talasila
In this study tremor data of 25 subjects (Senile tremor = 5, Alcohol induced tremor = 9, Healthy individuals = 11) were collected using a wearable device consisting of five Inertial Measuring Units (IMUs) and an embedded optical sensor. The subjects were made to draw the Archimedes spiral under the influence of external stressors. Features were extracted from measured acceleration data and also from an optical sensor. Using the selected features few supervised machined learning algorithms were explored for automatic classification of tremor. Performance matrix used to evaluate the classifier was accuracy, recall, and precision. It is observed that the algorithms are able to accurately classify healthy, senile tremor and alcohol induced tremor.
{"title":"Machine Learning Techniques for Automated Tremor Detection in the Presence of External Stressors","authors":"K. Vanitha, Viswanath Talasila","doi":"10.46300/9106.2022.16.69","DOIUrl":"https://doi.org/10.46300/9106.2022.16.69","url":null,"abstract":"In this study tremor data of 25 subjects (Senile tremor = 5, Alcohol induced tremor = 9, Healthy individuals = 11) were collected using a wearable device consisting of five Inertial Measuring Units (IMUs) and an embedded optical sensor. The subjects were made to draw the Archimedes spiral under the influence of external stressors. Features were extracted from measured acceleration data and also from an optical sensor. Using the selected features few supervised machined learning algorithms were explored for automatic classification of tremor. Performance matrix used to evaluate the classifier was accuracy, recall, and precision. It is observed that the algorithms are able to accurately classify healthy, senile tremor and alcohol induced tremor.","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":"87032012","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: 10.46300/9106.2022.16.68
Haiting Ji, Jianfeng Liu
This paper studies the application of data fusion technology in power system to solve some difficult problems in this complex energy system. A transmission tower identification and bird nest detection method based on corner, line, color and shape features is proposed. Through LSD (Line Segment Detection) and Harris corner detection method, the straight line segment and corner point in the image are extracted respectively. Combined with triangle method, the actual tilt angle of tower is measured; According to the nesting rule of birds in transmission towers, the basic unit segmentation algorithm of transmission towers is proposed, and the basic unit segmentation of transmission towers is realized by using the local maximum of the target pixel row statistical histogram. The algorithm proposed in this paper can effectively solve the problems of on-line measurement of tilt angle of transmission tower and on-line detection of bird's nest, which will lay a theoretical foundation for on-line monitoring of transmission tower status.
{"title":"Operation Status Monitoring of Transmission Tower in Power System based on Data Fusion","authors":"Haiting Ji, Jianfeng Liu","doi":"10.46300/9106.2022.16.68","DOIUrl":"https://doi.org/10.46300/9106.2022.16.68","url":null,"abstract":"This paper studies the application of data fusion technology in power system to solve some difficult problems in this complex energy system. A transmission tower identification and bird nest detection method based on corner, line, color and shape features is proposed. Through LSD (Line Segment Detection) and Harris corner detection method, the straight line segment and corner point in the image are extracted respectively. Combined with triangle method, the actual tilt angle of tower is measured; According to the nesting rule of birds in transmission towers, the basic unit segmentation algorithm of transmission towers is proposed, and the basic unit segmentation of transmission towers is realized by using the local maximum of the target pixel row statistical histogram. The algorithm proposed in this paper can effectively solve the problems of on-line measurement of tilt angle of transmission tower and on-line detection of bird's nest, which will lay a theoretical foundation for on-line monitoring of transmission tower status.","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":"82172722","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: 10.46300/9106.2022.16.66
Bingjie Lin, Jie Cheng, Jiahui Wei, Ang Xia
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 by educational field. In addition, the model can also be used in the applicability evaluation of the sensing methods of different industries’ network security situation. Certainly, in different categories, and under the premise of different sensing methods of network security situation, the proportions of various influencing factors are different, and once the proportion is unreasonable, it will cause false calculation process and thus affect the results.
{"title":"A Sensing Method of Network Security Situation Based on Markov Game Model","authors":"Bingjie Lin, Jie Cheng, Jiahui Wei, Ang Xia","doi":"10.46300/9106.2022.16.66","DOIUrl":"https://doi.org/10.46300/9106.2022.16.66","url":null,"abstract":"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 by educational field. In addition, the model can also be used in the applicability evaluation of the sensing methods of different industries’ network security situation. Certainly, in different categories, and under the premise of different sensing methods of network security situation, the proportions of various influencing factors are different, and once the proportion is unreasonable, it will cause false calculation process and thus affect the results.","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":"82405034","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: 10.46300/9106.2022.16.81
Vanya Ivanova, T. Tashev, I. Draganov
In this paper an optimized feedforward neural network model is proposed for detection of IoT based DDoS attacks by network traffic analysis aimed towards a specific target which could be constantly monitored by a tap. The proposed model is applicable for DoS and DDoS attacks which consist of TCP, UDP and HTTP flood and also against keylogging, data exfiltration, OS fingerprint and service scan activities. It simply differentiates such kind of network traffic from normal network flows. The neural network uses Adam optimization as a solver and the hyperbolic tangent activation function in all neurons from a single hidden layer. The number of hidden neurons could be varied, depending on targeted accuracy and processing speed. Testing over the Bot IoT dataset reveals that developed models are applicable using 8 or 10 features and achieved discrimination error of 4.91.10-3%.
{"title":"Detection of IoT based DDoS Attacks by Network Traffic Analysis using Feedforward Neural Networks","authors":"Vanya Ivanova, T. Tashev, I. Draganov","doi":"10.46300/9106.2022.16.81","DOIUrl":"https://doi.org/10.46300/9106.2022.16.81","url":null,"abstract":"In this paper an optimized feedforward neural network model is proposed for detection of IoT based DDoS attacks by network traffic analysis aimed towards a specific target which could be constantly monitored by a tap. The proposed model is applicable for DoS and DDoS attacks which consist of TCP, UDP and HTTP flood and also against keylogging, data exfiltration, OS fingerprint and service scan activities. It simply differentiates such kind of network traffic from normal network flows. The neural network uses Adam optimization as a solver and the hyperbolic tangent activation function in all neurons from a single hidden layer. The number of hidden neurons could be varied, depending on targeted accuracy and processing speed. Testing over the Bot IoT dataset reveals that developed models are applicable using 8 or 10 features and achieved discrimination error of 4.91.10-3%.","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":"84020322","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: 10.46300/9106.2022.16.67
Suchana Mishra, R. K. Mishra, S. Patnaik
This paper deals with a rectangular microstrip antenna on a trapezoidal substrate. It finds radiation pattern of the antenna using the concept of fractional cross product. Results show that as the fraction goes from 1 to 0.1, the direction of null in the H-plane moves from end fire towards broad side. Also, a back-lobe starts to appear in the H-plane.
{"title":"Radiation Pattern of a Microstrip Antenna on a Trapezoidal Substrate","authors":"Suchana Mishra, R. K. Mishra, S. Patnaik","doi":"10.46300/9106.2022.16.67","DOIUrl":"https://doi.org/10.46300/9106.2022.16.67","url":null,"abstract":"This paper deals with a rectangular microstrip antenna on a trapezoidal substrate. It finds radiation pattern of the antenna using the concept of fractional cross product. Results show that as the fraction goes from 1 to 0.1, the direction of null in the H-plane moves from end fire towards broad side. Also, a back-lobe starts to appear in the H-plane.","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":"85408425","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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