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检索条件"主题词=Filtering algorithms"

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Exploring Methods of Mitigation against DDoS Attack in an IoT Network

Exploring Methods of Mitigation against DDoS Attack in an Io...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Piyush M. Prajapati Priyesh P. Gandhi Sheshang Degadwala Gujarat Tech. University Ahmedabad Gujarat India Sigma University Vadodara Gujarat India Dept. of Comp. Eng Sigma University Vadodara Gujarat India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Smart homes and industrial systems alike have benefited greatly from the increased connection and ease made possible by the explosion of Internet of Things (IoT) devices. Nevertheless, Distributed Denial of Service (DDoS) assaults have become more common due to the widespread use of these linked devices, which has put IoT networks at risk. In order to protect IoT networks against Distributed Denial of Service (DDoS) assaults, this study looks into the topic and suggests new ways to do so. The research delves into advanced tactics, such as adaptive traffic filtering, anomaly detection, machine learning algorithms, and hardware-based solutions, by examining the specific vulnerabilities of IoT ecosystems. Also covered is the possibility of using edge computing for mitigation and detection in real-time. Through simulations and experiments, the research evaluates the performance and efficacy of these methods under diverse attack scenarios, considering resource constraints and scalability. The findings not only contribute to academic discussions on IoT security but also provide practical insights for industry practitioners and policymakers. The research aims to enhance the resilience of IoT networks, ensuring the integrity, availability, and confidentiality of data in the face of evolving DDoS threats.

关键词： Industries Machine learning algorithms Scalability Smart homes filtering algorithms Real-time systems Internet of Things

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UAV Target Tracking Algorithm for Ground Moving Targets

UAV Target Tracking Algorithm for Ground Moving Targets

引用

Intelligent Design (ICID), International Conference on

作者： Xin Xu Bin Liu Yun Zhong Xinyu Bai Jia Guo Huitong Fu Xiaoyan Li Information and Navigation School Air Force Engineering University Xi'an China School of Electronic Information Engineering Xi'an Technological University Xi'an China

ISBN: (数字)9798331541491

ISBN: (纸本)9798331541507

With the goal of effectively estimating the target's location, an algorithm for adaptive target tracking that based on target's response is put forth. First, the extracted features are compressed by an encoder and a soft quantizer. Secondly, the target response regular term is added to the target training model, and the optimal target response function is updated in the process of solving, so that the tracking algorithm is able to precisely track the target after being disturbed, so as to improve the representation ability of the target. Finally, reliability coefficient is introduced to evaluate the response graph of each feature channel to express the ability of the model to express the target. According to experimental data, this method can achieve 94.5 % tracking accuracy.

关键词： Training Adaptation models Target tracking Correlation Accuracy filtering algorithms Autonomous aerial vehicles Feature extraction Reliability engineering Robustness

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Machine Learning Techniques Applied to Intrusion Detection Systems

Machine Learning Techniques Applied to Intrusion Detection S...

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IEEE Ecuador Technical Chapters Meeting (ETCM)

作者： Gabriel Lara Ricardo Flores-Moyano Maria Baldeon-Calisto Daniel Riofrío Noel Pérez-Pérez Diego Benítez Colegio de Ciencias e Ingenierías “El Politécnico” Universidad San Francisco de Quito USFQ Quito Ecuador

ISBN: (数字)9798350391589

ISBN: (纸本)9798350391596

As cyber threats continue to evolve, the necessity of Intrusion Detection Systems (IDS) to protect sensitive information has become increasingly apparent. To address the limitations of conventional signature-based IDS, machine learning-based approaches have been proposed. However, the dearth of up-to-date network datasets impedes the advancement of these innovative methods. Furthermore, robust and reproducible results are required to ensure that the performance of ML models is not merely a product of randomness but a reflection of genuine and reliable capabilities. In this context, this paper presents a comprehensive evaluation of standalone and hybrid machine learning algorithms aimed at improving the differentiation between normal and malicious traffic. The hybrid model combines rule-based filtering with machine learning-based evaluation. A set of predefined rules is first applied to filter the dataset, and then the ML algorithms focus on the most relevant data, thus improving the efficiency in detecting previously unseen threats and reducing computational complexity. This approach is designed to improve detection accuracy while maintaining a low false positive rate, addressing both precision and operational efficiency in real-world scenarios.

关键词： Machine learning algorithms Accuracy filtering Computational modeling Intrusion detection Machine learning filtering algorithms Reflection Reliability Computational complexity

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Feature-Aided UKF-PHD Filter for Nonlinear Multi-target Tracking in Holographic Staring Radar

Feature-Aided UKF-PHD Filter for Nonlinear Multi-target Trac...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Junyao Yang Zebin Chen Zhanming Zhong Yue Zhang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Traditional radar multi-target tracking relies on data association methods, which result in exponential computational complexity when clutter and target densities are high. The Gaussian mixture probability hypothesis density (GM-PHD) filter, built upon the random finite set framework, presents a more efficient approach for multi-target tracking without having to conduct explicit data association. However, this method is only applicable to Gaussian linear models, limiting its utility in practical scenarios. Moreover, radar environments often feature high clutter densities and low detection probabilities, making real-time and accurate target tracking challenging. To address these issues, this paper proposes extending the GM-PHD filter to nonlinear Gaussian models using the unscented transform and introduces a feature-aided unscented Kalman filter PHD (FAUKF-PHD) multi-target tracking algorithm. By leveraging the high-precision Doppler information contained in holographic staring radar echoes, the proposed algorithm adjusts the weights of Gaussian components, significantly reducing clutter weights to distinguish targets from clutter and pruning redundant components. The simulation outcomes evidently show that the FAUKF-PHD filter put forward in this study boosts the estimation accuracy and real-time performance under scenarios characterized by high clutter density and low detection probability.

关键词： Maximum likelihood detection Target tracking Radar clutter Nonlinear filters filtering algorithms Radar tracking Information filters Real-time systems Doppler radar Clutter

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Temperature and Gas Concentration Imaging of LAS Tomography from Gaussian Filtered SART with Artifact Removal

Temperature and Gas Concentration Imaging of LAS Tomography ...

引用

IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Jinting Wen Zhang Cao Lijun Xu School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

Laser absorption spectroscopy (LAS) is widely applied for tomographic imaging of temperature and gas concentration in combustion fields. To improve the imaging accuracy and reduce artifacts, the Gaussian filter is added in the iteration of simultaneous algebraic reconstruction technique (SART) as a smoothing operator, and an artifact removal method is developed based on the correlation between flame temperature and concentration. Symmetrical and asymmetrical distributions of temperature and $\mathrm{H}_{2}\mathrm{O}$ mole fraction obtained from the computational fluid dynamics (CFD) simulation were reconstructed in the numerical verification. The proposed Gaussian filtered SART with artifact removal performed well with the fewest artifacts and lowest reconstruction errors compared to typical SART-based methods. The exit of a high-temperature wind tunnel was measured for experiment verification, and temperatures at the center of the wind tunnel exit in 11 different cases were measured by the thermocouple as a reference. Temperature and $\mathbf{H}_{2}\mathbf{O}$ mole fraction distributions reconstructed by the proposed method were smooth, and its variation trend was consistent with the real condition. The maximum relative error of temperature of the proposed method was only 5.29%, much smaller than that of the typical SART with a median filter. Both numerical and experiment verifications demonstrated the ability of the proposed method in high-accuracy tomographic imaging.

关键词： Temperature measurement Temperature distribution Spectroscopy Correlation Wind tunnels Computational fluid dynamics Thermocouples Tomography filtering algorithms Image reconstruction

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A Rao-Blackwellized Particle Filter for Superelliptical Extended Target Tracking

A Rao-Blackwellized Particle Filter for Superelliptical Exte...

引用

International Conference on Information Fusion

作者： Oğul Can Yurdakul Mehmet Çetinkaya Enescan Çelebi Emre Özkan Department of Electrical & Electronics Engineering Middle East Technical University Ankara Turkey

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

In this work, we propose a new method to track extended targets of different shapes such as ellipses, rectangles and rhombi. We provide an analytical framework to express these shapes as superelliptical contours and propose a Bayesian filtering scheme that can handle measurements from the contour of the object. The method utilizes the Rao-Blackwellized particle filtering algorithm with novel sensor-object geometry constraints. The success of the algorithm is demonstrated using both simulations and real-data experiments, and the algorithm has been demonstrated to be of high performance in various challenging scenarios.

关键词： Geometry Analytical models Target tracking Shape Atmospheric measurements Shape measurement filtering algorithms Particle measurements Mathematical models Particle filters

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A Finite- Time Complementary Filter for Attitude Estimation Using Inertial Sensors

A Finite- Time Complementary Filter for Attitude Estimation ...

引用

IEEE International Conference on Unmanned Systems (ICUS)

作者： Linan Wang Guanghui Wen Xiaojian Yi Xinhe Wang Geng Liu Department of Systems Science Southeast University Nanjing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Cyber Science and Engineering Southeast University Nanjing China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

This paper presents a novel finite-time complementary filter specifically designed for attitude and gyroscope bias estimation. The key contributions of this filter include ensuring rapid convergence to the actual attitude within a finite time, significantly improving performance over traditional filters that only guarantee asymptotic stability. The filter achieves high accuracy by effectively integrating data from multiple sensors, such as gyroscopes, accelerometers, and magnetometers, through advanced sensor fusion algorithms and noise suppression techniques. Additionally, simulation tests confirm the filter's robust resistance to noise, highlighting its potential to enhance the performance of attitude estimation systems across various applications by providing faster, more accurate, and reliable estimates.

关键词： Accuracy Magnetometers Magnetic separation Magnetic sensors Noise reduction Noise filtering algorithms Sensor fusion Gyroscopes Convergence

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Research on Ubiquitous Power Internet of Things in Distribution Networks Using Adaptive Gradient Algorithm with Convolution Neural Network

Research on Ubiquitous Power Internet of Things in Distribut...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Songmei Liu Wanjiang University of Technology Ma'anshan China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In China, several distribution automation devices are established in distribution network to increase reliability of power supply. The major challenge of ubiquitous electric Internet of Things (IoT) is a resource aspect which enables distributed computational sources. Therefore, the Adaptive Gradient Algorithm with Convolution Neural Network (Adagrad-CNN) is proposed for ubiquitous electric IoT in distribution networks. The Adagrad is helpful when dealing with sparse features or data that has extensive-range of values. The CNN has multiple-layer architecture for classifying image pixels patterns through processing and it filtered noise captures significant predictive properties. The Adagrad-CNN leads to better generalization and enhance the CNN performance through mitigating issues based on exploding gradients that is essential when dealing with difficult and large datasets in ubiquitous electric IoT. The dataset is collected from various devices and sensors in the power grid and it is preprocessed by Z-score normalization. This preprocessing enhances convergence rates and reduces the large scales. It is essential when features are in different ranges and it prevents particular feature dominance. The Adagrad-CNN attains 96.54%, 94.21 %, 95.73% and 94.59% of accuracy, recall, precision and f1-score.

关键词： Performance evaluation Adaptive systems Neural networks Noise Distribution networks filtering algorithms Prediction algorithms Power grids Sensors Internet of Things

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Adam Pine cone optimization with Deep learning for cervical cancer detection using pap smear image

Adam Pine cone optimization with Deep learning for cervical ...

引用

Intelligent Technologies (ACOIT), Asian Conference on

作者： V T Ram Pavan Kumar P.L. Ramesh M Arulselvi Department of MCA K.B.N College Vijayawada Andhra Pradesh Department of Computers K.B.N College Vijayawada Andhra Pradesh Department of CSE Annamalai University Chidambaram Tamil Nadu

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Cervical cancer is the main reason of mortality in females worldwide. The Human Papillomavirus (HPV) is the major reasonfor cervical cancer in female patients. The virus first infects cells in the cervix of female patients and spreads throughout the cervix. This cancer is curable if diagnosed early and treated on time. Here, this paper presents an innovative method based on Deep Learning (DL) for detecting cervical cancer. Initially, the input pap smear image is considered as inputand is forwarded to the preprocessing step in which the noise contained in the pap smear image is eradicated utilizing an adaptive bilateral filter. Next, Utilizing a Mask Regional Convolutional Neural Network (Mask-RCNN) the affected region is segmented and shape features are mined from the segmented image. Then, cervical cancer detection is effectuated by exploiting the Deep Maxout Network (DMN) model, which is structurally optimizedbythe designed Adam Pine Cone Optimization Algorithm (APCOA). Furthermore, the performance of APCOA_DMN is assessed using several metrics and it obtained anaccuracyof $\mathrm{9 1. 2 8 4 \%}$, a True Negative Rate (TNR) of $91.315 \%$, and a True Positive Rate (TPR) of $\mathrm{9 1. 9 7 9 \%}$.

关键词： Measurement Deep learning Image segmentation Accuracy Noise Adaptive filters Mortality filtering algorithms Cervical cancer Optimization

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Improved Adaptive Fading Memory Filter Based on State Switching Under Anomalous Measurement Data

Improved Adaptive Fading Memory Filter Based on State Switch...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Aihua Shan Yajie Hou Hui He Bo Sun College of Electronic Information Engineering Shandong University of Science and Technology Qingdao China Hunan Huahuit Automation Technology Co. Changsha China College of Intelligent Equipment Shandong University of Science and Technology Taian China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

With the swift evolution of integrated navigation technology in our country, it is essential to realize more accurate positioning of integrated navigation systems. In this paper, the INS/GNSS integrated navigation method is studied. Firstly, the system model is established according to the INS/GNSS integrated navigation principle. Then, based on the system model, the data fusion algorithms of Kalman filter memory filter (KF), FKF, and adaptive filtering are studied, to solve the problem of excessive use of fading factor in fading memory filtering, there is a state-switching-based fading memory filtering method (MFKF), however, this algorithm only considers the imprecise modeling of the Integrated Navigation System or the mismatch of the noise characteristics and does not consider the situation of the abnormal measurement data. To solve this problem, an improved adaptive fading memory filter based on state switching (IMFKF) is proposed by combining the innovation-based adaptive filtering method with the state switching fading memory filter and building the algorithm model that is more suitable for precise positioning. Simulation experiments verify the validity of the algorithm.

关键词： Fading channels Technological innovation Adaptive systems filtering Current measurement Noise Sea measurements Switches filtering algorithms Noise measurement

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