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A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

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Computers, Materials & Continua 2024年第6期79卷 4845-4861页

作者： Samia Allaoua Chelloug department of information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Intrusion detection is a predominant task that monitors and protects the network ***,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and *** particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic *** is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new *** has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection *** that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD *** base model is trained on the NSL-KDD dataset to extract significant *** the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base *** combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection *** experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion *** performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual *** stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification ***,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficien

关键词： Intrusion detection multi classification deep learning stacking NSL-KDD

Maximizing Solar Potential Using the Differential Grey Wolf Algorithm for PV System Optimization

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Energy Engineering 2024年第8期121卷 2129-2142页

作者： Ezhilmathi Nagarathinam Buvana Devaraju Karthiyayini Jayamoorthy Padmavathi Radhakrishnan Santhana Lakshmi ChandraMohan Vijayakumar Perumal Karthikeyan Balakrishnan department of Electrical and electronics Engineering Pandian Saraswathiyadav Engineering CollegeSivagangaiTamil Nadu630561India department of Electrical and electronics Engineering Sri Balaji Chockalingam Engineering CollegeArniTamil Nadu632317India department of information Science and Engineering New Horizon College of EngineeringBangalore560103India department of Electrical and electronics Engineering Rajalakshmi Engineering CollegeChennaiTamil Nadu602105India department of Electrical and electronics Engineering Sona College of TechnologySalemTamil Nadu636005India School of electronics Engineering Vellore Institute of TechnologyChennaiTamil Nadu600127India department of Electrical and electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatoreTamil Nadu641008India

Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental *** work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer(DGWO).It dynamically adjusts the parameters of the MPPT controller,specifically the duty cycle of the SEPIC converter,to efficiently track the Maximum Power Point(MPP).The proposed system aims to enhance the energy harvesting capability of solar PV systems by optimizing their performance under varying solar irradiance,temperature and shading *** results demonstrate the effectiveness of the DGWO-based MPPT system in maximizing the power output of solar PV installations compared to conventional MPPT *** research contributes to the development of advanced MPPT techniques for improving the efficiency and reliability of solar energy systems.

关键词： DGWO SEPIC converter MPPT PV module

Wide Stopband Substrate Integrated Waveguide Filter Using Bisection and Trisection Coupling in Multilayer

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Chinese Journal of electronics 2024年第2期33卷 436-442页

作者： Peng CHU Jianguo FENG Peng ZHU Lei GUO Long ZHANG Leilei LIU Guoqing LUO Ke WU College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology School of information and Communication Engineering Dalian University of Technology Guangdong Provincial Key Laboratory of Optoelectronic information Processing Chips and Systems Sun Yat-sen University College of electronics and information Engineering Shenzhen University School of electronics and information Hangzhou Dianzi University Poly-Grames Research Center Polytechnique Montreal

This article presents a highly efficient method for substrate-integrated-waveguide（SIW） filters to achieve very wide stopbands. By employing the proposed trisection slots in addition to the bisection slots as the inter-coupling structures, all spurious modes below TE505 of a SIW filter working in the fundamental mode TE101（f0）can be eliminated without requiring additional structure or complex theoretical analysis, without affecting the design of the fundamental passbands, and without degrading the performance of the filters. For verification, two prototype filters are designed, fabricated, and measured with wide stopbands up to 4.15f0 and 4.83f0, respectively. The proposed technique could facilitate the development of high-performance wide-stopband SIW filters for microwave/wireless circuits and systems.

关键词： Microwave measurement Couplings Filters Prototypes Nonhomogeneous media Microwave theory and techniques Microwave circuits

An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Science China(information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN School of Electrical and information Engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical Engineering and Automation Henan Institute of Technology School of electronics and information Zhongyuan University of Technology KINDI Center for Computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2024年第6期25卷 470-482页

作者： Congyue LI Yihuai HU Jiawei JIANG Dexin CUI Merchant Marine College Shanghai Maritime UniversityShanghai 201306China School of Mechanical and Energy Engineering Shanghai Technical Institute of Electronics&InformationShanghai 201411China

Marine power-generation diesel engines operate in harsh *** vibration signals are highly complex and the feature information exhibits a non-linear *** is difficult to extract effective feature information from the network model,resulting in low fault-diagnosis *** address this problem,we propose a fault-diagnosis method that combines the Gramian angular field(GAF)with a convolutional neural network(CNN).Firstly,the vibration signals are transformed into 2D images by taking advantage of the GAF,which preserves the temporal *** raw signals can be mapped to 2D image features such as texture and *** integrate the feature information,the images of the Gramian angular summation field(GASF)and Gramian angular difference field(GADF)are fused by the weighted average fusion ***,the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning *** the concept of residuals in the attention mechanism improves the feasibility of ***,the weighted average fused images are fed into the CNN for feature extraction and fault *** validity of the proposed method is verified by experiments with abnormal valve *** average diagnostic accuracy is 98.40%.When−20 dB≤signal-to-noise ratio(SNR)≤20 dB,the diagnostic accuracy of the proposed method is higher than 94.00%.The proposed method has superior diagnostic ***,it has a certain anti-noise capability and variable-load adaptive capability.

关键词： Multi-attention mechanisms(MAM) Convolutional neural network(CNN) Gramian angular field(GAF) Image fusion Marine power-generation diesel engine Fault diagnosis

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

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China Communications 2024年第2期21卷 183-196页

作者： Yueyue Su Nan Qi Zanqi Huang Rugui Yao Luliang Jia College of Electronic information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China School of electronics and information Northwestern Polytechnical UniversityXi’an 710072China School of Space information Space Engineering UniversityBeijing 101416China

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

关键词： channel selection cooperative antijamming and interference mitigation local altruistic game Stackelberg game unmanned aerial vehicle(UAV)

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Near-field wideband beam training for ELAA with uniform circular array

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Science China(information Sciences) 2024年第6期67卷 370-383页

作者： Yuhao CHEN Linglong DAI department of Electronic Engineering Tsinghua UniversityBeijing National Research Center for Information Science and Technology (BNRist)

Extremely large-scale antenna array(ELAA) at millimeter wave(mmWave) and Terahertz(THz)band has been considered a key technology for combating high attenuation in high-frequency bands in future6G communications. Uniform circular arrays(UCAs) have attracted much attention because of their ability to provide flat beamforming gain at all angles. To realize efficient beamforming, beam training is widely used to acquire channel state information. However, with a large antenna number, the beam training overhead in ELAA systems becomes overwhelming. Moreover, with a large bandwidth, the beam defocus effect severely degrades beam training accuracy. To address these issues, this paper proposes a frequency-dependent focusing(FDF)-based beam training scheme to realize effective beam training in near-field wideband ELAA systems with UCA. Specifically, we first analyze the FDF property of UCA, where signals at different subcarriers can simultaneously focus on different distances. Then, by exploiting the FDF property to search different distances using different subcarriers simultaneously, we design a hierarchical codebook and propose an FDFbased beam training scheme. To reveal the effectiveness of the proposed scheme, we compare its necessary beam training overhead with that of existing schemes. Finally, the simulation results demonstrate that the proposed scheme can achieve accurate beam training in near-field wideband UCA systems with a low beam training overhead.

关键词： extremely large-scale antenna array (ELAA) uniform circular array (UCA) near-field wideband beam training

An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1429-1457页

作者： Hela Elmannai Monia Hamdi Souham Meshoul Amel Ali Alhussan Manel Ayadi Amel Ksibi department of information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of information System College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of *** ranks as the second most prevalent cause of permanent *** glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait *** automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc *** methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted *** the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease *** approach presented in this study is based on deep classifiers for OD segmentation and glaucoma ***,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy *** OD region is then fed into the second stage for glaucoma ***,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc *** this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and *** also used the Support Vector Machine *** feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor *** final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is *** metrics including accuracy and ROC curve are compared for each classification *** novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global ***,the fusion-based decision system uses the g

关键词： Optic disc glaucoma fundus image deep learning

Multi-UAV Collaborative Edge Computing Algorithm for Joint Task Offloading and Channel Resource Allocation

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Journal of Communications and information Networks 2024年第2期9卷 137-150页

作者： Yuting Wei Sheng Wu Zhe Ji Zhigang Yu Chunxiao Jiang Linling Kuang School of information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China China Academy of electronics and information Technology Beijing 100041China Beijing National Research Center for information Science and Technology Tsinghua Space CenterTsinghua UniversityBeijing 100084China

Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.

关键词： UAV-based edge computing multi-UAV collaboration joint task offloading and wireless channel allocation simulated annealing(SA)algorithm multi-agent deep deterministic policy gradient(MADDPG)