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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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Guided Exploration Reinforcement Learning for UAV Pursuit-Evasion Games 11th

Guided Exploration Reinforcement Learning for UAV Pursuit-E...

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11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Kim, Dohyun Lee, Seungho Lee, Jayden Dongwoo Lee, Hohyeong Bang, Hyochoong Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9783031706837

This paper proposes a novel pursuit strategy for a quadrotor-type UAV pursuit evasion game. The novel guidance law considering 3-dimensional and quadrotor UAV maneuvers is proposed as a baseline and expert knowledge for UAV pursuit-evasion games. In this paper, we propose guided exploration reinforcement learning (GE-DDPG) to enhance convergence time and exploration efficiency compared to pure DDPG with Gaussian noise and behavior cloning. The simulation results show that the proposed method outperforms the baseline guidance law and other DDPG methods in capture time and performance in UAV maneuvering scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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Fingerprint inspired advanced end-effector and its applications

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Friction 2022年第10期10卷 1739-1750页

作者： Sung Ho LEE Hyo Sung KIM Han Jun PARK Moon Kyu KWAK Department of Electrical Engineering and Computer Science University of MichiganAnn Arbor 48109USA Department of Mechanical Engineering Kyungpook National UniversityDaegu 41566Republic of Korea

The human fingertip consists of a fingerprint with many micro-grating *** main roles of the fingerprint could be divided into two purposes,namely,the enhancement of the frictional force and the effective transmittance of the *** this study,we present the fingerprint-inspired end-effector that has not only admirable frictional force but also electric *** end-effector is composed of fluorocarbon rubber,one of the famous materials to achieve high frictional force and *** various experiments,the novel performance of micro structured fluorocarbon rubber end-effector(MSFE)is characterized by comparing with a macroscale patterned sample(MPS),which has been already used in real industrial *** results are analyzed ***,as feasible applications,we suggest two applications based on the role of the *** is the conductive astronaut glove with high frictional force,and the other one is a non-slip pad for the next-generation glass transfer *** these experiments,we successfully observe the enhanced system performance and confirm the possibility of using the MSFE as feasible *** believe that the MSFE could be a useful and powerful alternative as an end-effector,not only in the aerospace industry but also in display manufacturing processes.

关键词： fingerprint end-effector fluorocarbon rubber(FKM) glass transfer system spacesuit

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Design and Technical Development of Wall-Climbing Robots:A Review

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Journal of Bionic engineering 2022年第4期19卷 877-901页

作者： Yi Fang Shuai Wang Qiushi Bi Da Cui Chuliang Yan School of Mechanical and Aerospace Engineering Jilin UniversityChangchun 130025China State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao 999078China

Once working at heights is dangerous,it is a significant *** accidents brought substantial economic losses and caused a large number of ***,it is essential to use wall-climbing robots to replace manual work at *** design of the wall-climbing robot is inspired by the climbing action of insects or *** intelligent bionic robot device can carry special equipment to operate on the wall and perform some dangerous operations instead of firefighters or inspection personnel more *** scope of application is *** paper firstly summarizes the research progress of wall-climbing robots with three different moving methods:wheel-climbing,crawler-based,and leg-footed robots;summarizes the applications and breakthroughs of four adsorption technologies:negative pressure,magnetic force,bionic and electrostatic;discusses the application of motion control algorithms in wall-climbing ***,the advantages and disadvantages of different migration modes and adsorption methods are pointed *** distribution and advantages of the combined application of different migration modes and adsorption methods are *** addition,the future development trend of wall-climbing robots and the promoting effect of bionic technology development on wall-climbing robots are *** content of this paper will provide helpful guidance for the research of wall-climbing robots.

关键词： Wall-climbing robot Walking method Adsorption technology Motion control technology Bionic

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A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence 1

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Kumari, Nancy Acharjya, D.P. Ma, Yan School of Computer Science and Engineering VIT Bhopal University Bhopal India School of Computer Science and Engineering VIT Vellore India College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

Preventing mental health problems is an essential task for national and international organizations. Such entails the establishment of active information network indicators to assess the mental health of populations. About 21% of the global people suffering from depression, anxiety, isolation, loss of income, and fear are triggering mental health problems due to the COVID-19 pandemic. Therefore, it is essential to find out various factors that affect the mental health of people in India the post COVID-19 pandemic. A hybridized rough and whale optimization technique is used to study the mental health of people in the post pandemic of India. The proposed design contains three stages of implementation. In the very beginning, the data collected from primary sources are validated using structural equation modeling and partial least square. Further, in the second phase, the proposed rough whale optimization is employed to identify the chief parameters of the decision system. The reduced decision system is analyzed further using a rough set in the final phase. It is observed that the evaluations have drawn attention to the importance of mental disorders for public health. It, in turn, helps to reduce the risk of getting a mental disorder. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 2096-2105页

作者： Zhang, Zhao Lin, Bor-Shyh Peng, Chih-Wei Lin, Bor-Shing Wuyi University College of Mechanical and Electrical Engineering Fujian Wuyishan354300 China National Taipei University College of Electrical Engineering and Computer Science Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering School of Gerontology and Long-Term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde

关键词： Sleep research

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An Industrial Micro Parts Recognition Technology Based on Improved Yolov8 7

An Industrial Micro Parts Recognition Technology Based on Im...

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7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Hao, Yafei Jia, Feng School of Computer Science and Engineering Xi'an Technological University Xi'an China School of Mechanical and Electrical Engineering Xi’an Technological University Xi’an China

Aiming at the recognition and classification problem of micro assembly parts in industrial production, the Yolov8 object detection algorithm was used as the base model to improve the C2f class in the backbone. And apply the improved object detection algorithm to the recognition field of detecting assembly parts. By collecting images of gears, nails, and screws from various angles on Roboflow, a training set and a validation set containing three types of gears, nails, and screws are formed. At the same time, in order to improve the robustness of the Yolov8 object detection algorithm, data processing methods such as adding noise and image rotation were adopted for the samples in the data. Due to the use of Yolov8 as the base model, a TXT text file containing label prediction box position information was formed after labelling the dataset on Roboflow. Based on Python (3.10.12) and cloud platform, improved Yolov8 object detection algorithm was trained and network validation was carried out. Use TXT files as input for the model to obtain and learn features of gears, nails, and screws. After 200 epochs, the mAP for detecting gears, nails, and screws increased by about 3% compared to the base model. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Screws

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Multi-variable control to mitigate loads in CRISPRa networks 63

Multi-variable control to mitigate loads in CRISPRa networks

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Manoj, Krishna Grunberg, Theodore W. Vecchio, Domitilla Del Mit Department of Mechanical Engineering MA02139 United States Mit Department of Electrical Engineering and Computer Science MA02139 United States

ISBN: (纸本)9798350316339

The discovery of CRISPR-mediated gene activation (CRISPRa) has transformed the way in which we perform genetic screening, bioproduction and therapeutics through its ability to scale and multiplex. However, the emergence of loads on the key molecular resources constituting CRISPRa by the orthogonal short RNA that guide such resources to gene targets, couple theoretically independent CRISPRa modules. This coupling negates the ability of CRISPRa systems to concurrently regulate multiple genes independent of one another. In this paper, we propose to reduce this coupling by mitigating the loads on the molecular resources that constitute CRISPRa. In particular, we design a multi-variable controller that makes the concentration of these molecular resources robust to variations in the level of the short RNA loads. This work serves as a foundation to design and implement CRISPRa controllers for practical applications. © 2024 IEEE.

关键词： Molecular docking

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Numerical Predictions of Laminar Forced Convection Heat Transfer with and without Buoyancy Effects from an Isothermal Horizontal Flat Plate to Supercritical Nitrogen

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Frontiers in Heat and Mass Transfer 2024年第3期22卷 889-917页

作者： K.S.Rajendra Prasad Sathya Sai T.R.Seetharam Adithya Garimella Department of Mechanical and Industrial Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104India Sustainable Energy Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2600 AANetherlands Department of Mechanical Engineering PES UniversityBengaluru560085India

Numerical predictions are made for Laminar Forced convection heat transfer with and without buoyancy effects for Supercritical Nitrogen flowing over an isothermal horizontal flat plate with a heated surface facing *** are performed by varying the value ofΔT from5 to 30 K and P_(∞)/P_(cr)ratio from1.1 to *** of all the thermophysical properties of supercritical Nitrogen is *** wall temperatures are chosen in such a way that two values of Tw are less than T∗(T*is the temperature at which the fluid has a maximum value of Cp for the given pressure),one value equal to T∗and two values greater than T∗.Three different values of U∞are used to obtain Re∞range of 3.6×10_(4)to 4.74×10^(5)for forced convection without buoyancy effects and Gr_(∞)/Re^(2)_(∞)range of 0.011 to 3.107 for the case where buoyancy effects are *** different forms of correlations are proposed based on numerical predictions and are compared with actual numerical *** has been found that in all six forms of correlations,the maximum deviations are found to occur in those cases where the pseudocritical temperature TT∗lies between the wall temperature and bulk fluid temperature.

关键词： Supercritical nitrogen laminar flow numerical methods forced convection heat transfer isothermal horizontal surface

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