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A Model Training Method for DDoS Detection Using CTGAN under 5GC Traffic

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Computer systems Science & engineering 2023年第10期47卷 1125-1147页

作者： Yea-Sul Kim Ye-Eun Kim Hwankuk Kim Department of Electronics Information and System Engineering Sangmyung UniversityCheonan31066Korea department of information Security engineering Sangmyung UniversityCheonan31066Korea

With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service(DDoS)attacks across vast IoT devices.Recently,research on automated intrusion detection using machine learning(ML)for 5G environments has been actively conducted.However,5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data.If this data is used to train an ML model,it will likely suffer from generalization errors due to not training enough different features on the attack data.Therefore,this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic.We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data.To solve the imbalance problem,synthetic minority oversampling technique(SMOTE)and generative adversarial network(GAN)-based conditional tabular GAN(CTGAN)of data augmentation were used.The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem.The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline.Still,regarding the generalization error,the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times,indicating an improvement in the problem.This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem.

关键词： 5G core traffic machine learning SMOTE GAN-CTGAN IoT DDoS detection tabular form cyber security B5G mobile network security

3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data

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Computers, Materials & Continua 2022年第9期72卷 5777-5791页

作者： Siddiqui Muhammad Yasir Amin Muhammad Sadiq Hyunsik Ahn department of Robot system engineering Tongmyong UniversityBusan48520Korea department of information&Technology University of Central PunjabLahorePakistan department of electronics engineering Tongmyong UniversityBusan48520Korea

3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments.It is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent basis.The computer vision,graphics,and machine learning fields have all given it a lot of attention.Traditionally,3D segmentation was done with hand-crafted features and designed approaches that didn’t achieve acceptable performance and couldn’t be generalized to large-scale data.Deep learning approaches have lately become the preferred method for 3D segmentation challenges by their great success in 2D computer vision.However,the task of instance segmentation is currently less explored.In this paper,we propose a novel approach for efficient 3D instance segmentation using red green blue and depth(RGB-D)data based on deep learning.The 2D region based convolutional neural networks(Mask R-CNN)deep learning model with point based rending module is adapted to integrate with depth information to recognize and segment 3D instances of objects.In order to generate 3D point cloud coordinates(x,y,z),segmented 2D pixels(u,v)of recognized object regions in the RGB image are merged into(u,v)points of the depth image.Moreover,we conducted an experiment and analysis to compare our proposed method from various points of view and distances.The experimentation shows the proposed 3D object recognition and instance segmentation are sufficiently beneficial to support object handling in robotic and intelligent systems.

关键词： Instance segmentation 3D object segmentation deep learning point cloud coordinates

Cross-layer transmission and energy scheduling under full-duplex energy harvesting wireless OFDM joint transmission

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Science China(information Sciences) 2016年第10期59卷 136-151页

作者： Hongjia LI Zejue WANG Dan HU Song CI Zhen XU State Key Lab of information Security Institute of Information EngineeringChinese Academy of Sciences Cisco system Incorporated department of Computer and electronics engineering University of Nebraska-Lincoln

This paper studies the design of the optimal and online cross-layer transmission and energy schedulings for a full-duplex energy harvesting wireless orthogonal frequency division multiplexing(OFDM) joint transmissions. Supported by today’s power management integrated circuit, the full-duplex energy harvesting system becomes a reality, which can overcome the transmission time loss problem caused by the half-duplex constraint of the energy storage unit(ESU) in the serial Harvest-Store-Use system. However, its corresponding modeling is still unexplored. Therefore, the full-duplex energy harvesting system is first modeled and proved to be equivalent to a composition of energy behavior models of Harvest-Store-Use in fine-time granularity. Then, the convex optimization problem of cross-layer transmission and energy scheduling is formulated with the objective to maximize the sum of transmission throughput during successively multiple time units, which takes into account the temporal variance of energy harvesting rates and channel states, and the limited capacity of ESUs.The optimal power allocation with three dimensions of time, channel and antenna is solved by utilizing the dual decomposition method with the pre-known temporal variance, and the corresponding result of the system throughput provides the theoretical upper bound. Finally, to reduce the throughput degradation caused by channel state prediction errors, a non-convex online scheduling problem is formulated as the classical energy efficiency format. It is transformed into a convex optimization problem by exploiting the properties of fractional programming, and then, an efficiently iterative solution is designed. Numerical results show that the average throughput of the online algorithm is 24% greater than that of existing time-energy adaptive water-filling algorithm. The degradation of the average throughput is less than 19% with probability 90%, even as the channel prediction error reaches 20%. These results provide guidelines for the design and optimization

关键词： energy harvesting wireless communication power allocation joint transmission optimization green communication

Technique for Order Preference by Similarity to an Ideal Solution-based comprehensive health assessment of composite insulators for overhead transmission lines

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High Voltage 2023年第1期8卷 149-157页

作者： Zhikang Yuan Zhouhang Yang Lijun Jin Gangjie Zhou Yingyao Zhang Hanqing Wang Cong Wang Youping Tu department of Electrical engineering College of Electronics and Information EngineeringTongji UniversityShanghaiChina State Key Laboratory of Alternate Electrical Power system with Renewable Energy Sources North China Electric Power UniversityBeijingChina

There are a lot of quantitative performance characterisation methods for composite insulators.However,how to apply the characterisation methods with different dimensions to comprehensively assess the health state of composite insulators remains a big problem for operators and researchers.A comprehensive health assessment model for composite insulators based on the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)method is proposed in this study.Fourteen composite insulators from four regions in Yunnan Province,China,and five quantitative characterisation indicators of the insulators,namely hardness,hydrophobicity,loss of hydrophobicity,recovery of hydrophobicity,and trap charge,are selected to verify the model.The results show that the Pearson correlation coefficient of the TOPSIS-based comprehensive health score with operating time is 0.72,which is better than the other single indicator.By introducing the operating time into the TOPSIS-based model,we can also obtain the ageing resistance performance of the composite insulator.This work provides a new idea to assess the health state and ageing performance of composite insulators.

关键词： composite comprehensive Ideal

Adaptive network fuzzy inference system based navigation controller for mobile robotAdaptive network fuzzy inference system based navigation controller for mobile robot

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Frontiers of information Technology & Electronic engineering 2019年第2期20卷 141-151页

作者： Panati SUBBASH Kil To CHONG department of electronics and information engineering Chonbuk National University Advanced electronics and information Research Center Chonbuk National University

Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system(ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.

关键词： Adaptive network fuzzy inference system Additive white Gaussian noise Autonomous navigation Mobile robot

Adaptive walking control of quadruped robots based on central pattern generator (CPG) and reflex

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控制理论与应用（英文版） 2013年第3期11卷 386-392页

作者： Chengju LIU Qijun CHEN Guoxing WANG department of electronics and information engineering and Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University

This paper presents a central pattern generator （CPG） and vestibular reflex combined control strategy for a quadruped robot. An oscillator network and a knee-to-hip mapping function are presented to realize the rhythmic motion for the quadruped robot. A two-phase parameter tuning method is designed to adjust the parameters of oscillator network. First, based on the numerical simulation, the influences of the parameters on the output signals are analyzed, then the genetic algorithm （GA） is used to evolve the phase relationships of the oscillators to realize the basic animal-like walking pattern. Moreover, the animal＇s vestibular reflex mechanism is mimicked to realize the adaptive walking of the quadruped robot on a slope terrain. Coupled with the sensory feedback information, the robot can walk up and down the slope smoothly. The presented bio-inspired control method is validated through simulations and experiments with AIBO. Under the control of the presented CPG and vestibular reflex combined control method, AIBO can cope with slipping, falling down and walk on a slope successfully, which demonstrates the effectiveness of the proposed walking control method.

关键词： CPG Walking control Quadruped robot Vestibular reflex

I-Quiz:An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

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Computer systems Science & engineering 2022年第3期40卷 1007-1021页

作者： B.T.Shobana G.A.Sathish Kumar department of information Technology Sri Venkateswara College of EngineeringSriperumbudur602117India department of electronics and Communication engineering Sri Venkateswara College of EngineeringSriperumbudur602117India

Electronic learning(e-learning)has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities.Advancements in information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web.Post covid-19 pandemic,online learning has become the most essential and inevitable medium of learning in primary,secondary and higher education.In recent times,Massive Open Online Courses(MOOCs)have transformed the current education strategy by offering a technology-rich and flexible form of online learning.A key component to assess the learner’s progress and effectiveness of online teaching is the Multiple Choice Question(MCQ)assessment in most of the MOOC courses.Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score.This is due to the possibility of random and smart guesses,learners can attempt,as MCQ assessments are more vulnerable than essay type assessments.This paper presents the architecture,development,evaluation of the I-Quiz system,an intelligent assessment tool,which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner’s real knowledge acquisition level.The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques.The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners.A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming.The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period.The resultant datase

关键词： E-Learning adaptive and intelligent e-learning systems intelligent tutoring systems emotion recognition non-verbal behaviour knowledge acquisition level

Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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Computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa department of Electrical and electronics engineering Christian College of Engineering and TechnologyOddanchatram624619India department of Electrical and electronics engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India department of Computer Science and engineering Christian College of Engineering and TechnologyOddanchatram624619India department of Computer system and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia department of information systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia department of information systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer.It also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer.In recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction stability.In order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in SGs.The proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter optimization.In addition,MOGOA-based FS technique is utilized in the selection of optimum subset of features.Besides,DELM-based predictive model is also applied in forecasting the load requirements.The proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive outcome.To inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter dataset.In the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

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Computers, Materials & Continua 2022年第3期70卷 4393-4410页

作者： Abeer D.Algarni Walid El-Shafai Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman department of information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia department of electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt department of Industrial electronics and Control engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt department of electronics and Communications engineering Faculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt

COVID-19 remains to proliferate precipitously in the world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients.With this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical images.This paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)images.The proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT images.The classification stage is firstly applied to detect and classify the input medical CT images.Then,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images.The classification stage is implemented based on a simple and efficient CNN deep learning model.This model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)layers.TheConv layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection process.For the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid approach.The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,accurately.To summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis system.Once the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the images.The region of interest can be assesses both automatically and through experts.This strategy helps somuch in saving the time and ef

关键词： COVID-19 segmentation classification CNN SPCNN CT images