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The Fusion of Feature for Person Re-identification Based on Siamese Network 7

The Fusion of Feature for Person Re-identification Based on ...

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7th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Du, Tianwen Zhang, Yifeng School of Information Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665487115

This paper proposes Fusion-based Siamese Network (FSN), a new deep learning architecture that targets at learning global and local features convergently for person re-identification (re-ID). This paper demonstrates the advantages of combining global and local features, which is embodied in indicators such as rank-1 and mAP. At the same time, it is noteworthy that FSN is based on siamese network, which leads innovation in image retrieving. Siamese network consists of two identical sub-networks. In the training process, a pair of images are input into the sub-networks at the same time. Through training, FSN updates the weights according to the triplet loss. FSN can solve the problem of retrieving pictures under different posture, size and light conditions correctly. On three benchmark datasets, Market-1501, DukeMTMC-reID, and CUHK03, the proposed FSN achieves 94.9%,89.1%,67.7% in rank-1 and 90.1%,86.3%,65.7% in mAP respectively, surpassing most of state-of-the-art methods by a large margin. © 2022 IEEE.

关键词： Training Deep learning Technological innovation Cloud computing Computer architecture Benchmark testing Big Data

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Research on Intrusion Detect System of Internet of Things based on Deep learning

Research on Intrusion Detect System of Internet of Things ba...

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2022 international conference on machine learning and Knowledge Engineering, MLKE 2022

作者： Li, Jiang Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9781665495677

In this paper, by combining the known deep learning related literature in the field of intrusion detection, and the existing in the design of intrusion detection system based on the deep learning common low detection efficiency and high rate of false positives, as well as the training data set used by the problems of unbalanced data, etc., through the depth study of feature extraction and data processing ability. To study the intrusion detection method of the Internet of Things based on deep learning. The work and achievements are as follows:First of all, will be used for feature selection embedded model (EM) and used in intrusion detection of the convolution neural network (CNN), combined with lightweight intrusion detection model (XCNN), because it is an embedded model (EM), so can reduce the impact on the equipment, at the same time compared with other algorithms, the computing time is shorter, the same or even ***, proposed to the Attention mechanism (Self - Attention) is applied to intrusion detection system, due to the nature of the Attention mechanism, the mechanism and long Attention can greatly reduce the training time, through introducing the residual network, application feature coding technique again the sparse features thickened into low density at high altitudes, and join to its relative position encoding, So it can sense global information and solve the long-term dependence problem of recurrent neural ***, the results of the intrusion detection method were analyzed on the Pthon simulation platform, and the relevant indicators were verified by using THE NSL-KDD dataset, THE CIC-IDS2017 dataset and the CSE-CIC-IDS2018 dataset. The effectiveness of the method was proved by building a laboratory Internet of Things environment for actual testing. The experimental results show that the algorithm can greatly reduce training time, improve training efficiency, and have the same or even better performance results. © 2022 IEEE.

关键词： Intrusion detection

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Enhancing Explainability of machine learning-based Intrusion Detection Systems

Enhancing Explainability of Machine Learning-based Intrusion...

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2022 RIVF international conference on computing and Communication Technologies, RIVF 2022

作者： Ho, Thi-Ngoc-Phuc Nguyen, Thuy-Linh Nguyen, Xuan-Ha Le, Kim-Hung University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9781665461665

Over the past decade, the anomaly-based Intrusion Detection System (IDS) has established itself with many studies proving its effectiveness, especially with deep learning models. However, these models have become more complex, thus making them difficult for humans to explain the system's decisions. Meanwhile, research to increase the transparency of IDSs receives insufficient attention from the research community. Therefore, this study proposes an Explainable NIDS capable of accurately detecting attacks and providing explicit explanations for its decisions. Our proposed IDS employs the Shapley Additive exPlanations (SHAP) framework to account for IDS decisions. It assists our IDS in self-explain its decisions at both the local and global levels. The local explanation explains the IDS decisions for each specific sample, while the global level provides the feature's importance and shows the attacks' characteristics. To demonstrate the effectiveness, we evaluate our proposal on two well-known IDS datasets: KDD Cup 99 and CICIDS2017. In addition, we experiment with interpreting not only the machine learning model but also the deep learning model. This research is expected to enhance the transparency of anomaly-based IDS and provide deeper insights for security experts. © 2022 IEEE.

关键词： Transparency

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A DDoS Attack Detection on Cloud Framework Using Improved Features Based machine learning Approach 2

A DDoS Attack Detection on Cloud Framework Using Improved Fe...

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2nd IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Bhargav, Ravi Jain, Vishal Verma, Manish Sharda Univ Sch Engn & Technol Dept Comp Sci & Engn Greater Noida 201306 UP India

ISBN: (纸本)9781665411202

A Denial of Service (DDoS) attack consumes the network bandwidth and computing resources of a targeted system, preventing the target system from being DDoS attacked by unauthorized users. This research work proposed a modified feature selection-based neural network for efficient DDoS attack detection. The proposed method is divided into three stages: feature selection, training, and testing. For the implementation of the proposed work using MATLAB 2020 software, MATLAB is a well-known academic as well as an industrial research software package for this work. In R2020 MATLAB, the proposed method is designed and simulated. There are different types of DDoS attacks present on the cloud internet. This research work uses the Canadian Institute of Cyber Security (CICIDS2017) data set to perform the proposed methodology. This data set was selected for classification because it consists of 80 parameters. Other than the benign traffic, as per the tools used, the flow records are labeled as 'Slowloris', 'Slowhttptest', 'Hulk', and 'Begian'. The proposed method shows good results in terms of accuracy, precision, selectivity, sensitivity, and confusion matrix (C.M.). The presented method shows an accuracy of 99% and the other parameters are discussed in the simulation and result section.

关键词： Component accuracy Precision Selectivity Sensitivity Specificity Confusion Matrix (CM) Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)

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DSFL: Decentralized Satellite Federated learning for Energy-Aware LEO Constellation computing

DSFL: Decentralized Satellite Federated Learning for Energy-...

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IEEE international conference on Satellite computing (Satellite)

作者： Wu, Chenrui Zhu, Yifei Wang, Fangxin Chinese Univ Hong Kong FNii Shenzhen Peoples R China Chinese Univ Hong Kong SSE Shenzhen Peoples R China Guangdong Prov Key Lab Future Networks Intelligen Shenzhen Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China

ISBN: (纸本)9781665457293

The dense constellation of Low Earth Orbit (LEO) satellites plays a significant role in the sixth generation mobile network (6G) and Terrestrial-Aerial-Space network. With the communication network composed of the LEO satellites, ground stations and terminals, global network services can be accessed anywhere and anytime. The global communication coverage and precious data resource embraces new opportunities to integrate the computing resources and data in LEO satellite constellations for intelligent learning tasks, like carbon estimation, transportation surveillance, forest fire detection, etc. Federated learning (FL) stands out as a promising paradigm towards this goal with a balance in privacy preservation and data utilization. Traditional FL employs a centralized deployment, i.e., regarding the ground station as the server and satellites as clients, which however faces two challenges: 1) The risk of single point failure from the centralized server. 2) Slow convergences resulting from satellites' intermittent communication. To tackle the challenges, in this paper, we propose a decentralized satellite federated learning, considering the limited communication traffic in satellite communication, the privacy of data and the efficiency of machine learning. Satellites collaborate in a decentralized way to reach a consensus on model parameters, overcoming the effects of data heterogeneity between satellites. Considering the scarce power capacity, we further design an energy-aware communication strategy to prolong satellites life and avoid communication congestion. Experiments on real-world datasets demonstrate that our framework can speed training process by 5-10 times, and requires only 1/3 to 1/6 communication energy cost.

关键词： Federated learning Satellite computing Low Earth Orbit (LEO) Distributed Deep learning

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Analyzing Market Dynamics of Agricultural Commodities: A Case Study Based on Cotton 3rd

Analyzing Market Dynamics of Agricultural Commodities: A Cas...

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The 3rd international conference on Frontiers in computing and Systems (COMSYS 2022)

作者： Korivi, Nikhila Sravani, Peteti Ali, Jafar Sobin, C.C. SRM University Andhra Pradesh Amaravati India LTRC IIIT Hyderabad Telangana Hyderabad India

ISBN: (纸本)9789819926794

Market deregulation of agricultural commodities requires in-depth analysis of day-to-day transactions. The analysis helps to draw greater insights into the market dynamics and builds better policies and advisory system for the farming sector. Reformation to the agricultural commodity market impel to deregulation of market transactions and increasing digitalization of transactions for better transparency across markets. These government policies have been taken based on the need of increasing farmer’s income from farming activities. These policy decisions give farmers immense opportunities for better realization of their farm outputs. To achieve the objective, there is an inevitable need for analyzing the market dynamics to draw better insights from the transactions. Also, farmers face massive loss due to these uncertain fluctuations in the price of agricultural commodities. Price prediction models can help farmers in making necessary decisions and help them in reducing the loss caused by price fluctuations. In this paper, we have chosen cotton as a commodity in which the price and volume of cotton over seven years from the Adoni market in Kurnool district of Andhra Pradesh, which is one of the largest cotton markets in the country market in Andhra Pradesh. The data consist of daily prices and volume of transactions of 7 years starting from 2011 to 2017. We have used machine learning techniques such as the ARIMA model for predicting the prices of cotton. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Predictive Modeling for Network Anomaly Detection Using machine learning

Predictive Modeling for Network Anomaly Detection Using Mach...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： G. Sivakumar K. Amsaveni Chandralekha R V. SrirengaNachiyar S. Vaitheki P. Marichamy Department of ECE Ramco Institute of Technology Department of CSE National Engineering College Kovilpatti Department of Electronics and Communication Engineering Ramco Institute of Technology Tamilnadu India Department of ECE PSRR College of Engineering Sivakasi Department of ECE PSR Engineering College Sivakasi

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The protection of networks from unauthorized access is challenging, traditional cannot effectively prevent malicious intrusions. Now –a –days Anomaly detection relies heavily on machine learning to identify the complexity of Network Data Variability of Attack Patterns, Imbalanced Datasets. Automated anomaly detection can be accomplished without explicit programming by using supervised, unsupervised, or semi-supervised learning techniques to detect outliers in complex data sets. By harnessing the power of data-driven learning, machine learning algorithms exhibit the capacity to discern novel and previously uncharted attacks, dynamically adjust to shifting network conditions, and notably diminish instances of false positives and negatives. The incorporation of machine learning into intrusion detection proves indispensable as networks continue their evolution toward heightened complexity. This study presents four popular machine learning algorithms: Naive Bayes, K-Nearest Neighbors (KNN), Support Vector machines (SVM), and Random Forest for anomaly detection in network. The CICIDS2017 dataset, known for its contemporaneity and extensive attack diversity, serves as the experimental foundation. Employing the Random Forest Regression algorithm, a meticulous process of feature selection was conducted. The ensuing application phase involved the utilization of distinct machine learning algorithms, resulting in remarkable performance outcomes. Specifically, the success rates achieved by various machine learning algorithms.

关键词： Support vector machines machine learning algorithms Accuracy Nearest neighbor methods Prediction algorithms Bayes methods Complexity theory

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HPFL-CN: Communication-Efficient Hierarchical Personalized Federated Edge learning via Complex Network Feature Clustering 19

HPFL-CN: Communication-Efficient Hierarchical Personalized F...

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19th Annual IEEE international conference on Sensing, Communication, and Networking (IEEE SECON)

作者： Li, Zijian Chen, Zihan Wei, Xiaohui Gao, Shang Ren, Chenghao Quek, Tony Q. S. Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore Singapore

ISBN: (数字)9781665486439

ISBN: (纸本)9781665486439

Federated learning (FL), a promising privacy-preserving distributed learning paradigm, has been extensively applied in urban environmental prediction tasks of Mobile Edge computing (MEC) by training a global machine learning model without data sharing. However, it is hard for the shared global model to be well generalized among local edge servers, due to the statistical data heterogeneity, especially in real-world urban environmental data. Besides, the existing FL approaches may result in excessive communication and computation overhead due to the frequent transmission and aggregation of model parameters between massive edge servers and remote cloud servers. To address the above issues, we propose HPFL-CN, a novel communication-efficient Hierarchical Personalized Federated edge learning framework via Complex Network feature clustering, aiming to cluster edge servers with similar environmental data distributions and then high-efficiently train personalized models for each cluster via hierarchical architecture. Specifically, HPFL-CN introduces Privacy-preserving Feature Clustering (PFC) to extract privacy-preserving low-dimensional feature representations of each edge server via mapping the environmental data to different complex network domains for clustering similar edge servers accurately. According to the clustering results of PFC, HPFL-CN further introduces an edgemediator-cloud architecture for hierarchical model aggregation by Effective Hierarchical Scheduling (EHS), in which every mediator coordinates the training of edge servers within each cluster and periodically uploads model to cloud server for global model aggregation. Meanwhile, each mediator server would find a tradeoff between cloud and edge models to realize personalization within clusters. Our extensive experiments on real-world datasets demonstrate the effectiveness and generalization of HPFL-CN, which outperforms other state-of-the-art FL methods regarding personalization performance and communica

关键词： Personalized Federated learning Edge computing Environmental Prediction Hierarchical Scheduling Complex Network

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HMLF_CDD_SSBM: A Hybrid machine learning Framework for Cardiovascular Disease Diagnosis Prediction Using the SMOTE Stacking Method 6th

HMLF_CDD_SSBM: A Hybrid Machine Learning Framework for Cardi...

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6th international conference on Innovative computing and Communication, ICICC 2023

作者： Naganjaneyulu, Satuluri Akanksha, Gurija Shaheeda, Shaik Sadhak, Mohammed Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819930098

Nowadays, predicting the diagnosis of cardiovascular disease (CDD) is one of the critical challenges in the medical field. Every year, millions of people die from cardiovascular diseases, the majority of which are preventable if caught early (Conroy in Eur Heart J 24:987–1003, 2003;Hippisley-Cox in BMJ 336:1475–1482, 2008;Kremers et al. in Arthritis Rheum 58:2268–2274, 2008). To prevent such kind of diseases, effective early prediction techniques using artificial intelligence (AI) and machine learning (ML) techniques for CDD prediction are much demanded (Krittanawong et al. in J Am College Cardiol 69:2657–2664, 2017). Traditional AI and ML models, on the other hand, fail to predict data imbalance and lead to relatively prediction accuracy at minimal rates. Compared to individual classifier models, ensemble methods performed better. Moreover, most researchers are not concerned with the imbalance of classes in the CDD dataset;one of the good approaches is Synthetic Minority Over-sampling Technique (SMOTE) (Guo et al. in Proceedings of Chinese automation congress (CAC);Ge et al. in Proceedings of IEEE conference on energy internet and energy system integration (EI2)). With all the above assumptions, this article introduces a new framework called Hybrid machine learning Framework for Cardiovascular Disease Diagnosis Using SMOTE-Based Stacking Method (HMLF_CDD_SBSM) for efficient and accurate prediction of cardiovascular diseases. A stacking model-based ensemble framework selected the best basic learners using seven classifiers: Logistic Regression (LR), Multilayer Perceptron (MLP), Extra Tree (ET), Extreme Gradient Boost (XGB), AdaBoost (ABoost), Random Forest (RF), and Light Gradient Boosting (LGB) (Light GBM). Logistic regression (LR) is used as a meta-classifier to avoid overfitting. Using diversity among strong classifiers in this model will be a more efficient way to achieve the highest accuracy. Accuracy, precision, specificity, sensitivity, and the F1-measure are

关键词： Adaptive boosting

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DDoSNet: A Deep learning Model for detecting Network Attacks in Cloud computing 4

DDoSNet: A Deep Learning Model for detecting Network Attacks...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Srilatha, Doddi Thillaiarasu, N. School of Computing and Information Technology Reva University Bengaluru560064 India Sreenidhi Institute of Science and Technology Department of Cse Hyderabad501301 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Cloud services are the major target for malicious attacks as more organizations and individuals shift to the cloud. When DDoS assaults pose one of the most significant dangers to Internet users and cloud services, this is especially important. As a result of DDoS assaults, cloud services cannot transmit optimum network infrastructure and sensitive information. Due to the flexibility of cloud computing, DDoS mitigation techniques in the cloud are significantly different from those used in conventional networks. When it comes to DDoS assaults, this article examines their influence on cloud resources and proposes a realistic intrusion detection system (IDS) referred to as DDoSNet. To evaluate models, this research study utilizes the CICIDS 2017 dataset. The intelligence approach of particle swarm optimization (PSO) is applied for feature selection. The optimal features are given input to the deep neural network (DNN) model to classify the given data as malicious or benign. The accuracy rate, recall, precision, and F1-score metrics are considered for model evaluation. The experimental results show an accuracy of 099.81%, precision of 099.77%, recall of 099.89% & F1 score of 099.83%. The presented system outperforms the conventional machine learning models. Hence, this model imparts huge confidence in providing security to cloud services. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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