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DDoS Detection in SDN Environments: Leveraging Machine Learning Algorithms with Local Interpretable Model-Agnostic Explanations (LIME) for Enhanced Security

DDoS Detection in SDN Environments: Leveraging Machine Lear...

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International Conference on Business Intelligence and Data Analytics, BIDA 2024

作者： Karna, Siwani Jahanve, Poluru Reddy Yadukul, S. Bhaskaran, Sreebha Vekkot, Susmitha Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9789819777167

SDN, short for Software Defined Networking, represents a network architecture controlled by software applications. Distributed Denial-of-Service (DDoS) attacks create a challenge to these SDN environments and lead to many issues like loss of data and network instability, and takes a lot of time to address requests made by human users. It is very crucial to detect such attacks, for users to seamlessly use the network and take advantage of what is available online. This study demonstrates the significance of Machine Learning algorithms in identifying malicious traffic. Employing algorithms Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) proves effective in detecting complex and dynamic attacks. The study utilizes Local Interpretable Model-agnostic Explanation (LIME) to analyze the strength of each feature that contributes towards the prediction of classification. The results highlight the superior performance of the Gradient Boosting Classifier, achieving an accuracy of 99.75% and an F1-score of 99.68% in detecting network anomalies and threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application programs

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A Survey on Evolutionary Computation Based Drug Discovery

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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Next-Gen Parking Facilities: Deep Learning for Face and License Plate based Access 18

Next-Gen Parking Facilities: Deep Learning for Face and Lice...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Balasubramanian, K. Kannan, N. Sivakami, S. Senthil, S. Immaculate Rexi Jenifer, P. Murali, N. E. G. S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Trichy India School of Computing SASTRA Department of Computer Science and Engineering India

ISBN: (纸本)9789380544519

Parking congestion has become a major problem in today's metropolitan environments, resulting in lost time, higher emissions, and irritated drivers. There is a severe shortage of parking places in metropolitan areas because of the ever-growing number of the vehicles and the ever-growing urban population. This shortage has exacerbated traffic congestion and environmental pollution in addition to making it difficult and time-consuming to find a parking space. Conventional parking management systems are finding it harder and harder to handle these issues;they frequently depend on physical barriers or manual ticketing. Innovative approaches that incorporate automation and technology are becoming more prevalent to address this problem. Conventional parking management systems frequently depend on human intervention and physical infrastructure, which leads to inefficiencies and constrained scalability. In contrast, new opportunities for a more intelligent, effective, and user-friendly experience are made possible by the incorporation of deep learning methods and cutting-edge computer vision technology into parking management. This system makes use of two essential elements: automatically recognizing license plate numbers for vehicle identification and facial recognition technology for person identification. Through the smooth integration of these technologies, the system not only makes parking easier, but it also improves security and maximizes parking space usage. We will examine the essential elements, advantages, and possible effects of the Face and Number Plate-Based Smart Parking System in this survey. According to experimental findings, employing a face- and number-plate verification method can increase the efficiency of smart parking systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Face recognition system Features based matching Slot allocation Smart parking system

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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mmFingerprint: A New Application Fingerprinting Technique via mmWave Sensing and Its Use in Rowhammer Detection 1st

mmFingerprint: A New Application Fingerprinting Technique vi...

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1st EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles, SmartSP 2023

作者： Liang, Sisheng Li, Zhengxiong Jiang, Chenxu Guo, Linke Zhang, Zhenkai School of Computing Clemson University Clemson United Kingdom Department of Computer Science and Engineering University of Colorado Denver Denver United States Holcombe Department of Electrical and Computer Engineering Clemson University Clemson United Kingdom

ISBN: (纸本)9783031516290

Application fingerprinting is a technique broadly utilized in diverse fields such as cybersecurity, network management, and software development. We discover that the mechanical vibrations of cooling fans for both the CPU and power supply unit (PSU) in a system strongly correlate with the computational activities of running applications. In this study, we measure such vibrations with the help of mmWave sensing and design a new application fingerprinting approach named mmFingerprint. We create a prototype of mmFingerprint and demonstrate its effectiveness in distinguishing between various applications. To showcase the use of mmFingerprint in cybersecurity for defensive purposes, we deploy it in a real computer system to detect the execution of reputable Rowhammer attack tools like TRRespass and Blacksmith. We find that the detection can reach a very high accuracy in practical scenarios. Specifically, the accuracy is 89% when exploiting CPU fan vibrations and nearly 100% when leveraging PSU fan vibrations. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Software design

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2024年第10期21卷 1-15页

作者： Li Zhongjie Yuan Weijie Guo Qinghua Wu Nan Zhang Ji Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： channel estimation hidden Markov model(HMM) orthogonal time frequency space(OTFS) unitary approximate message passing(UAMP)

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Intelligent Channel Impulse Response Feature Prediction in Underwater Acoustic Networks 15

Intelligent Channel Impulse Response Feature Prediction in U...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Yun, Sinwoong Lee, Jemin Department of Electrical Engineering and Computer Science Daegu Korea Republic of School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798350364637

In this paper, we develop an intelligent channel im-pulse response (CIR) feature prediction algorithm in underwater networks. To this end, we first extract the major features, i.e., CIR values and tap distances, from raw CIR. Then, we implement a feature prediction module by adopting the time-series forecasting learning algorithm. Through the simulation results, we verify the prediction accuracy of the proposed algorithm with the normalized mean square error (NMSE) loss curve. © 2024 IEEE.

关键词： Prediction models

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Recovery Strategy of Wind Turbine for Frequency Regulation Considering Wind Condition 3

Recovery Strategy of Wind Turbine for Frequency Regulation C...

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3rd International Conference on Energy, Power and electrical Technology, ICEPET 2024

作者： Xin, Zhipeng Zhang, Feng Sun, Kexu Cornélusse, Bertrand Shandong University School of Electrical Engineering Jinan China University of Liege Department of Electrical Engineering and Computer Science Liège Belgium

ISBN: (纸本)9798350352658

The traditional recovery strategy of wind turbines after frequency regulation generally sets the wind speed unchanged during the frequency recovery period, during which while the wind speed has random volatility and has an impact on the overall wind power. For this reason, a staged frequency recovery strategy for wind turbines adapted to the random change of wind speed is proposed. Specifically, a high level of power support is provided by the wind power at the early stage to inhibit the rapid fall of frequency, and the wind speed is regarded as constant due to the relatively short control time. At the middle stage, wind power carries out continuous frequency support, continuously feeding back random wind speed information and iteratively updating control parameters, and then cooperates with synchronous machine frequency response for smooth action. The last stage is the wind turbine speed recovery phase, divided into several time intervals, and the speed recovery parameters are adjusted to adapt to the random wind speed changes, and weaken the frequency secondary drop caused by the wind power withdrawal. Finally, the effectiveness of the proposed strategy is verified in simulation part. © 2024 IEEE.

关键词： Windmill

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Automated Classification and Segmentation of Brain Tumor Images using Backboned ResUNet Framework 6

Automated Classification and Segmentation of Brain Tumor Ima...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prasanalakshmi, B. Anitha, Cuddapah Balamanigandan, R. Mahaveerakannan, R. Gade, N.V. Ratnakishor Adjunct Faculty Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

One of the most challenging issues in computer imaging is the automated segmentation of brain tumors using Magnetic Resonance Images (MRI). Several approaches are explored using Deep Neural Networks in image segmentation since they perform well in automated tumor segmentation using tumor scans. Due to the complexity, training deeper neural networks usually takes a long time and many processing resources. An automated approach for classifying and segmenting images of Brain tumor MRI is proposed. As a hybrid transfer learning approach, modified ResNet50 identifies the class of MRI images as Benign or malignant. After identifying the Benign images, the tumor’s location is segmented using a hybrid network involving ResUNet with the Xception network as Backbone. This combination approach is chosen after its comparative analysis with other backbones. Various combination approaches are carried out, and performance metrics like Jaccard, Dice, and Tversky coefficient are done to prove the ResUNet backboned with Xception plays a better role concerning accuracy and speed. The proposed approach was tested on the dataset Cancer Imaging Archive to validate its efficiency. The proposed model has an average dice similarity coefficient score of 0.94, outperforming the conventional transfer learning training method with all the layers. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Simultaneous improving luminescence intensity and stability of CsPbBr_(3):SCN−@Eu/Zr-Uio-66-NH_(2) with tunable emissions from blue to green and applications in indoor photovoltaics

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Nano Research 2024年第8期17卷 6879-6887页

作者： Minze Li Yao Sun Honglai Lu Peifen Zhu Ruihong Wang Guofeng Wang Key Laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA

The construction of stable and efficient materials that emit blue and green light remains a *** the blue light materials reported,metal-organic framework(MOF)materials are rarely reported as blue phosphors due to their weak luminescence *** on the construction of CsPbBr_(3)@MOF(CPB@MOF),an innovative idea was proposed to simultaneously enhance the green luminescence of CPB and the blue luminescence of MOF through the interaction between CPB and MOF for the first *** expected,the blue luminescence from CPB:7%SCN−@0.5%MOF:Eu as well as the green luminescence from 5%CPB:7%SCN−@MOF:Eu was sufficient to construct high-performance light-emitting diode(LED)devices and further excite solar cells to generate stable photoelectric *** white LED(WLED)device with excellent color quality(color rendering index(CRI)=96.2)and correlated color temperature(CCT=9688 K)can be constructed by using the obtained blue-emitting CPB:7%SCN-@0.5%MOF:Eu,green-emitting 5%CPB:7%SCN−@MOF:Eu,and red-emitting PPB:30%Mn^(2+).The density functional theory(DFT)theoretical calculation results indicate that the p orbital of Pb plays the major role in the conduction band,and the p orbital of Br plays the major role in the valance band of CPB and CPB:SCN−.While the p orbital of O plays the major role in both the conduction band and valance band of *** heat capacity of CPB and CPB:SCN−separately reaches the Dulong–Petit limit at 200 and 400 K,indicating that the thermal stability of CsPbBr_(3)increases after SCN−doping.

关键词： CsPbBr_(3):SCN−@Uio-66-NH_(2) composites luminescence stability white light-emitting diodes(WLEDs) indoor photovoltaics density functional theory(DFT)calculation

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