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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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Intrusion Detection System using Ensemble Learning Analytics

Intrusion Detection System using Ensemble Learning Analytics

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international conference for Convergence of Technology (I2CT)

作者： Anil Kumar Dasari Saroj Kr. Biswas Saptarsi Sanyal Biswajit Purkayastha Dept. of Computer Science & Engineering National Institute of Technology Silchar Silchar India

An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. the network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. the performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.

关键词： Support vector machines Analytical models Intrusion detection Termination of employment Feature extraction software Time complexity

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Research on Fusion Detection of FDI Attacks in WSNs with Privacy Preservation Under Communication Constraints 1

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Liu, Jinming Liu, Li Hu, Xin Zhang, He Leng, Kun Wang, Zengfeng School of Information and Electrical Engineering Ludong University Yantai264025 China School of Mathematics and Statistics Ludong University Yantai264025 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

this paper investigates the issue of data security and false data injection (FDI) attack detection and warning speed in wireless sensor networks under bandwidth constraints. Firstly, for non-malicious and FDI attacks, a system model with privacy protection is established to ensure data security and integrity while reducing the covertness of FDI attacks for detection. Secondly, a privacy-protected residual generator is designed to capture the actual trajectory of FDI attacks, effectively identifying and distinguishing between genuine and false data. Introduce multiple quantizers with limited levels to meet communication bandwidth requirements, a convex optimization problem is formulated to design a privacy-protected FDI distributed fusion detection criterion. Subsequently, enhancing the resilience of residual signals to interference and obtain more accurate thresholds, thereby improving detection warning speed. Finally, the effectiveness of the proposed method is validated through simulation results of unmanned surface vessel (USV) navigation at maritime. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Differential privacy

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Image Denoising using Autoencoders : Denoising noisy imgaes by removing noisy pixels/grains from natural images using Deep learning and autoencoders techniques

Image Denoising using Autoencoders : Denoising noisy imgaes ...

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international conference for Convergence of Technology (I2CT)

作者： Uday Kulkarni Sachin Patil Vikas E Rahul Patil Bodha Kulkarni Meena S. M Akshay Shanbhag School of Computer Science KLE Technological University Hubli India

Denoising images is widely used in applications from critical medical systems to software based image enhancement in our cell phones. the natural noise is simulated by adding noise to an image in random pixels. Currently the denoising problem can be solved in either with greedy algorithm or by deep learning techniques. In this paper we are discussing the usage of Autoencoders a deep learning technique to solve the problem of denoising. Autoencoders use down-sampling and up-sampling techniques to remove the unwanted noise from an *** using Encoders we compress the image gradually to remove the tiny details like noise using convolution layers and ReLU as activation function. the compressed images are fed to Decoders, the decoder up-samples the image bit-by-bit till the image reaches the original resolution, while up-sampling the tiny details like human hair or tiny speckles on face are restored without trying to bring back the noisy pixels. To train the proposed model we used FFHQ-Face dataset which consists of 70 thousand images of 128x128 resolution, each image is a face of and unique individual. the output of the neural network is a denoised image with PSNR of above 35, which maximum achieved compared to other pre-trained models.

关键词： Deep learning Image resolution Image coding Convolution Noise reduction Neural networks software

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A Modified Dual Loop Droop Control for Parallel Operation of Grid-forming Converters

A Modified Dual Loop Droop Control for Parallel Operation of...

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international conference for Convergence of Technology (I2CT)

作者： Anshul Awasthi Souvik Bera Ragavan K Electrical Engineering Indian Institute of Technology Gandhinagar India

Distributed Generators (DGs) are becoming more integrated into the existing power system due to the growing popularity of renewable energy sources. However, in contrast to synchronous generators in a conventional power system, most renewable-based DGs are connected to the grid via a low inertial power electronic interface, which can impact the system stability significantly due to their uncertain generation. these power electronic converters can operate in grid following or grid forming mode. However, the latter taxonomy is the most widely used topology since it can operate in grid-connected or standalone modes and can self-restore the nominal frequency and voltage conditions. this article presents a modified dual loop droop control technique to operate two grid-forming converters in parallel, acquire precise power sharing, and maintain voltage regulation according to the DGs’ rated capacities. the mathematical formulation and the simulation of the proposed control strategy are performed in MATLAB/Simulink, while OPAL-RT is utilized for the real-time digital simulation of the framework in software in loop (SIL) mode.

关键词： Power system stability Synchronous generators Real-time systems Power electronics Mathematical models Topology Voltage control

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Forest Fire Detection using Convolutional Neural Network Model

Forest Fire Detection using Convolutional Neural Network Mod...

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international conference for Convergence of Technology (I2CT)

作者： Shubham Sah Suneet Prakash Shweta Meena Department of Software Engineering Delhi Technological University Delhi India

Everyone recalls the destruction brought on by the Australian forest fires in 2019. Even though there isn’t much we can do to battle forest fires on our own, we can always rely on technology. By this we are trying to predict the accuracy of these models on forest fire data set. We are trying to detect forest fire in dense forest; our data set is very diverse and consist of images having forest fires, smokes, non-smoke and fire images. We have found out that Sensor detection and real-time geological data analysis are two methods for detecting forest fires. However, using image classification, for which Deep learning is the most efficient solution, is one of the best methods for detecting fire. In addition, these algorithms can be integrated with drones using deep learning techniques so that images can be taken frequently from the sky with ease, smoke can be detected in dense forests, and the authorities can be notified to take immediate action. the convolutional neural network algorithm for fire detection was the sole focus of our paper. the value of various epochs is used to evaluate these results.

关键词： Deep learning Biological system modeling Forestry Predictive models Cameras Real-time systems Recording

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Formalized Designing Methodology of ISMS for Critical Infrastructure 8

Formalized Designing Methodology of ISMS for Critical Infras...

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8th ieee international conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Ulianovska, Yuliia Florov, Serhii Hrebeniuk, Andrii Katkova, Tatiana Prokopovich-Tkachenko, Dmytro Gvozdov, Roman Dnipro University of Customs And Finance Department of Computer Science And Software Engineering Dnipro Ukraine Dnipro University of Technology Department of Information Security And Telecommunications Dnipro Ukraine Dnipro State University of Internal Affairs Department of Economics And Information Security Dnipro Ukraine Dnipro University of Customs And Finance Department of Cyber Security Dnipro Ukraine Dnipro University of Customs And Finance Department of Cyber Security And Informational Technologies Dnipro Ukraine Kharkiv National University of Radioelectronics Department of Security of Informational Technologies Kharkiv Ukraine

ISBN: (纸本)9781665406826

Today, the functioning of the state is impossible without a developed information infrastructure. Such systems are already considered as critical infrastructure. the architecture of information systems is growing along with the number of services they provide. Designing flaws of such systems can critically affect the safety of their operation. As information and telecommunication systems become more complex, security is becoming an important issue, where a formal approach to the design of security features will help to avoid mistakes in integrating an information security management system. this paper proposes new requirements for the ISMS project for critical infrastructure, in particular a formal design approach. © 2021 ieee.

关键词： Information management

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PyTPU: Migration of Python Code for Heterogenous Acceleration with Automated Test Generation

PyTPU: Migration of Python Code for Heterogenous Acceleratio...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Arghya Kundu Uyen Trang Nguyen Electrical and Computer Engineering York University Toronto Canada

software applications are increasingly built to take advantage of heterogeneous architectures as specialised hardware accelerators take centre stage in today’s computing environment. Tensor processing units (TPUs), the latest hardware addition, have demonstrated orders of magnitude improvement in computing efficiency over CPUs and GPUs for heterogeneous deep learning applications. However, despite the trend of incorporating heterogeneity and specialization in hardware, the creation of heterogeneous applications is confined to a handful of engineers. We propose a framework called PyTPU that takes Python code written in the PyTorch framework as input and automatically migrates it to its TPU compatible counterpart with test behaviour preservation and increased performance. First, PyTPU generates unit test cases to ensure test behavior compatibility. Second, using the abstract syntax tree and a manually curated exhaustive knowledge base, PyTPU migrates the original code to its TPU compatible version. Finally, PyTPU ensures the migrated code is readable and maintainable by adding necessary comments and conforming to PEP8 standard if needed. We evaluated PyTPU on four real-world Python applications with TPU v2 kernels. On average the migrated heterogeneous code is 19.103% faster than the original code while safeguarding test behavior preservation.

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Ka-band Reflectarray Unit-cell with 1-bit Digital Phase Resolution 8

Ka-band Reflectarray Unit-cell with 1-bit Digital Phase Reso...

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8th NAFOSTED conference on Information and computer Science, NICS 2021

作者： Nguyen, Minh thien Tran, Van Su Nguyen, Binh Duong International University Vietnam National University School of Electrical Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9781665410014

A simple and low-profile reconfigurable unit-cell design for Ka band reconfigurable reflectarray antennas is presented in this paper. the unit-cell is based on a single substrate and a ground plane that allows a simple fabrication process. One p-i-n diode is used to control the reflection phase shift with a step of 180°. the optimization of the unit-cell structure is carried out with full wave simulation software. Radiation characteristics of a 10x10-element reflectarray is also validated in Ka-frequency band. Simulation results show that the unit cell exhibits a good 1-bit phase control within a wide bandwidth and the array achieves an excellent beam-steering capability with low loss and wide scan angle. © 2021 ieee.

关键词： Cytology

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Channel Robust RF Fingerprint Identification Method with Narrow Band Differential Constellation Trace Figure

Channel Robust RF Fingerprint Identification Method with Nar...

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ieee international conference on Signal and Image Processing (ICSIP)

作者： Xiangqi Meng Linning Peng Ruowei Song Hua Fu Aiqun Hu School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Institute of Advanced Technology University of Science and Technology of China Hefei China School of Information Science and Engineering Southeast University Nanjing China

RF fingerprint (RFF) identification in wideband wireless communication systems is vulnerable to interference from wireless channel variations. this paper proposes a channel robust RFF identification method with narrow band differential constellation trace figure (NB-DCTF). A particular narrowband filter bank is designed to extract narrow-band RFF features from 802.11 preamble signal. the RFF features of 12 subcarriers in the short training symbol are respectively extracted and drawn into the NB-DCTF. A convolutional neural network (CNN) is then designed to identify different devices using NB-DCTF features. In the experiment, we construct a testbed including 11 mobile phones and one universal software radio peripheral (USRP) platform as the receiver. the classification accuracy in static state can reach 98.9%, and that in mobile state can reach 92.6%.

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FixGPT: A Novel three-Tier Deep Learning Model for Automated Program Repair

FixGPT: A Novel Three-Tier Deep Learning Model for Automated...

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ieee international conference on Data Science in Cyberspace (DSC)

作者： Wenhao Ye Jun Xia Shuo Feng Xiangyu Zhong Shuai Yuan Zhitao Guan STATE GRID HUNAN ELECTRIC POWER COMPANY LIMITED Changsha China School of Control and Computer Engineering North China Electric Power University Beijing China HUNAN SGIT TECHNOLOGY CO. LTD. Changsha China Department of Finance Operations and Information Systems Brock University Ontario Canada

Automatic program repair plays a crucial role in the software development and implementation. While deep learning-based approaches have made significant progress, one inherent challenge is the inefficiency in code representation, which hampers accurate patch generation. Furthermore, the training data used by these data-driven approaches may be limited, and they may not be able to capture the subtle differences between vulnerabilities and patches. To address these issues, FixGPT, we propose a three-tier deep learning model in the study. Specifically, a generative pre-trained transformer model is designed in the first tier to capture code characteristics and programming patterns. the second tier integrates a generation model based on the structure of neural machine translation, for the purpose of generating potential patches. Finally, a contrast model is introduced in the last tier to differentiate between the vulnerability and the patch. We also incorporate the Byte Pair Encoding approach to reduce the search space by converting identifiers into subwords. Detailed experimental studies have been carried out to evaluate the performance of FixGPT on two well-known benchmarking datasets: QuixBugs and Defects4J. the results demonstrated significant improvements in the effectiveness and accuracy in comparison with existing solutions. We complement these findings through the analysis of two case studies.

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