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SGDE: Secure Generative data Exchange for Cross-Silo Federated learning 5

SGDE: Secure Generative Data Exchange for Cross-Silo Federat...

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Lomurno, Eugenio Archetti, Alberto Cazzella, Lorenzo Samele, Stefano Di Perna, Leonardo Matteucci, Matteo Politecnico di Milano Italy

ISBN: (纸本)9781450396899

Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks. Several federated learning frameworks employ differential privacy to prevent private data leakage to unauthorized parties and malicious attackers. Many studies, however, highlight the vulnerabilities of standard federated learning to poisoning and inference, thus raising concerns about potential risks for sensitive data. To address this issue, we present SGDE, a generative data exchange protocol that improves user security and machine learning performance in a cross-silo federation. the core of SGDE is to share data generators with strong differential privacy guarantees trained on private data instead of communicating explicit gradient information. these generators synthesize an arbitrarily large amount of data that retain the distinctive features of private samples but differ substantially. In this work, SGDE is tested in a cross-silo federated network on images and tabular datasets, exploiting beta-variational autoencoders as data generators. From the results, the inclusion of SGDE turns out to improve task accuracy and fairness, as well as resilience to the most influential attacks on federated learning. © 2022 ACM.

关键词： Electronic data interchange

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Combating Advanced Persistent threats for Imminent Low Earth Orbit Cognitive Communications Systems 16

Combating Advanced Persistent Threats for Imminent Low Earth...

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16th Annual IEEE international Systems conference (SysCon)

作者： LaMar, Suzanna Gosselin, Jordan J. Happel, Lisa Jayasumana, Anura Northrop Grumman Corp 15120 Innovat Dr San Diego CA 92128 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO USA

ISBN: (纸本)9781665439923

With the proliferation of Low Earth Orbit (LEO) spacecraft constellations, comes the rise of space-based wireless cognitive communications systems (CCS) and the need to safeguard and protect data against potential hostiles to maintain widespread communications for enabling science, military and commercial services. For example, known adversaries are using advanced persistent threats (APT) or highly progressive intrusion mechanisms to target high priority wireless space communication systems. Specialized threats continue to evolve with the advent of machine learning and artificial intelligence, where computer systems inherently can identify system vulnerabilities expeditiously over naive human threat actors due to increased processing resources and unbiased pattern recognition. this paper presents a disruptive abuse case for an APT-attack on such a CCS and describes a trade-off analysis that was performed to evaluate a variety of machine learning techniques that could aid in the rapid detection and mitigation of an APT-attack. the trade results indicate that with the employment of neural networks, the CCS's resiliency would increase its operational functionality, and therefore, on-demand communication services reliability would increase. Further, modelling, simulation, and analysis (MS&A) was achieved using the Knowledge Discovery and data mining (KDD) Cup 1999 data set as a means to validate a subset of the trade study results against Training Time and Number of Parameters selection criteria. Training and cross-validation learning curves were computed to model the learning performance over time to yield a reasonable conclusion about the application of neural networks.

关键词： cognitive communications system wireless communications cybersecurity advanced persistent threat intrusion detection resiliency and machine learning

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Security attack situation awareness based on massive log data mining 5

Security attack situation awareness based on massive log dat...

引用

5th international conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

作者： Xu, Yin Yu, Pengfei Shen, Wen Li, Ziqian State Grid Customer Service Center Tianjin China State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Institute Co. Ltd Nanjing China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

Security situation awareness usually uses massive log information to discover abnormal attacks based on basic user attributes, user behavioral actions and user interactions through machine learning and other methods. Considering that the interaction between users in security situation awareness is exactly the graph data structure to which graph neural networks are applicable, this paper proposes a graph neural network-based security situation awareness method for massive logs, by mining log data, extracting user features for aggregation, and finally predicting user behavior to achieve security situation awareness. Compared with traditional supervised or unsupervised learning algorithms, the graph structure built in this paper not only retains the information carried by the users themselves, but also retains the relationship features between users and users, and between users and servers. By mapping the relationships between users to homogeneous graphs and between users and servers to heterogeneous graphs, and introducing an attention mechanism to dynamically adjust the weights of neighboring nodes, the accuracy of graph neural network learning can be effectively improved. © 2022 IEEE.

关键词： data mining

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machine learning Algorithms for Disease Prediction Analysis

Machine Learning Algorithms for Disease Prediction Analysis

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： M Govindaraj V Asha Binju Saju M Sagar Rahul Master of Computer Application New Horizon College of Engineering Bengaluru India

People suffer from a wide variety of diseases due to their environment and lifestyle factors. As a result, early disease prediction becomes critical. On the other hand, making a diagnosis solely on symptoms is too difficult for a doctor. the most difficult challenge is predicting disease accurately. In order to solve this problem, data mining is considered as an important and effective method for disease prediction. data mining reveals hidden pattern information from massive amounts of medical data derived from disease data. To aid in early diagnosis and treatment, this research work presents a disease prediction prototype to identify potential illnesses based on their symptoms. It reduces the time required to complete the patient’s full diagnosis; additionally, the system’s recommendations can be used to determine the disease. attempts to accurately predict a disease by using machine learning techniques. the proposed system produces results that are up to 87% accurate. this method has enormous potential for more accurately predicting potential diseases. the primary goal of this study is to help nontechnical people and young doctors to make accurate opinions about the disease.

关键词： machine learning algorithms Prototypes Medical services machine learning data mining Medical diagnostic imaging Diseases

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One-Step Multi-view Clustering Based on Low-Rank Tensor Proximity learning 5

One-Step Multi-view Clustering Based on Low-Rank Tensor Prox...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Li, Lin Cao, Ying Zhou, Xiaojun Lu, Zhiqiang Li, Dongxiao Song, Li Migu Co. Ltd Beijing China The China Academy of Information and Communications Technology Beijing China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Multi-view clustering has achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of methods still suffer from the following two problems. (1) they aim to study a common representation or pairwise correlations between views, ignoring the comprehensiveness and deeper higher-order correlations among multiple views. (2) they can-not obtain optimal clustering performance since the unified graph construction is separated from the subsequent clustering step. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. In this paper, we propose a novel one-step multi-view clustering method based on low-rank tensor proximity learning to address these issues. Low-rank tensor proximity learning, consensus fusion learning and labels learning are performed in a unified framework. through the three steps of mutual negotiation, the final clustering label is directly obtained. Experimental results on four benchmark datasets demonstrate that our method outperforms other state-of-the-art methods. © 2022 IEEE.

关键词： Tensors

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Analysis of data mining-Based Approach for Intrusion Detection System 5

Analysis of Data Mining-Based Approach for Intrusion Detecti...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Soewu, Taiwo Hemant Rakhra, Manik Singh, Dalwinder Olabisi Onabanjo University Department of Computer Engineering Ago Iwoye Nigeria Lovely Professional University School of Computer Science and Enginnering Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350398267

Due to the widespread usage of computers nowadays, there is also a greater likelihood of cyberattacks on systems or networks than previously thought. Intrusion Detection System (IDS) can be a successful method to identify the attacks in order to lessen intrusion attacks. IDS combined with data mining techniques can be an effective approach for quickly and precisely identifying damaging attacks performed by intruders and logging or blocking these attempts as necessary. IDS is a data-based process, thus by applying data mining techniques, it becomes more effective at identifying anomalous actions by drawing on its prior knowledge. In this study, we use the IDS and data mining tools to look for systemic anomalies. In the paper that follows, we demonstrate how to create a secure network by using data mining classification algorithms for intrusion detection. the machine learning classifiers Random Tree, Naive Bayes, J48, and Random Forest are used for classification. Compare the strategies based on parameters including data accuracy, detecting useful patterns, and collecting useful information. © 2022 IEEE.

关键词： Intrusion detection

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Research on the Construction of Enterprise Financial Risk Early Warning Model Based on data mining 24

Research on the Construction of Enterprise Financial Risk Ea...

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Proceedings of the 2024 5th international conference on Big data Economy and Information Management

作者： Shihui Du Weifang Engineering Vocational College Weifang Shandong China

ISBN: (纸本)9798400711862

In order to effectively identify and respond to enterprise financial risks, this study adopts data mining technology to construct an enterprise financial risk early warning model. through data preprocessing and feature selection, representative financial indicators are extracted, and the risk warning model is built by combining machine learning algorithms. Based on historical financial data and industry cases, the study selects indicators such as accuracy, recall and F1 value to evaluate the model performance, and proposes optimization strategies to enhance the model's early warning effect. the results show that the optimized model can effectively improve the accuracy and stability of financial risk early warning, and provide decision support for enterprise managers.

关键词： data mining

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epiDAMIK 5.0: the 5th international Workshop on Epidemiology meets data mining and Knowledge Discovery 22

epiDAMIK 5.0: The 5th International Workshop on Epidemiology...

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28th ACM SIGKDD conference on Knowledge Discovery and data mining (KKD)

作者： Adhikari, Bijaya Yadav, Amulya Pei, Sen Srivastava, Ajitesh Kefayati, Sarah Rodriguez, Alexander Charpignon, Marie-Laure Vullikanti, Anil Prakash, B. Aditya Univ Iowa Iowa City IA 52240 USA Penn State Univ University Pk PA 16802 USA Columbia Univ New York NY USA Univ Southern Calif Los Angeles CA 90007 USA IBM Corp Boston MA USA Georgia Inst Technol Atlanta GA 30332 USA MIT Boston MA USA Univ Virginia Charlottesville VA USA

ISBN: (纸本)9781450393850

Similar to previous iterations, the epiDAMIK @ KDD workshop is a forum to promote data driven approaches in epidemiology and public health research. Even after the devastating impact of COVID-19 pandemic, data driven approaches are not as widely studied in epidemiology, as they are in other spaces. We aim to promote and raise the profile of the emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. the current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health. Homepage: https://***/.

关键词： epidemiology public health forecasting AI for good

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Classification of Hepatitis Disease Using learning Vector Quantization 3(LVQ3) 5

Classification of Hepatitis Disease Using Learning Vector Qu...

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5th international conference on Information and Communications Technology, ICOIACT 2022

作者： Bagi, Yufika Sari Mihuandayani Stmik Multicom Bolaang Mongondow Department of Information System Kotamobagu Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Artificial Neural Network (ANN) is one of the fields of science in Artificial Intelligence (AI) that has been widely applied in various problems such as pattern recognition, forecasting, and classification. learning Vector Quantization 3 (LVQ3) is one method in ANN that has been widely applied to assist in the medical field. In this study, LVQ3 was used to classify the types of hepatitis. the data used in this study were 105 medical records with 3 types of hepatitis, where 80 data were used as a training set and 25 data were used for data test. there are 47 attributes consists 41 symptom factor and 6 risk factors. Based on experimental results, the LVQ3 algorithm can classify the types of hepatitis with an accuracy of 88%. © 2022 IEEE.

关键词： Neural networks

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A Credit Card Fraud Detection Model Using machine learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced datasets for High Scores 1

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5th international conference on Applied machine learning and data Analytics, AMLDA 2022

作者： Sinayobye, Omar Musabe, Richard Uwitonze, Alfred Ngenzi, Alexander School of ICT University of Rwanda Kigali Rwanda

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Due to recent advancements in communication technology and e-commerce systems, credit cards are currently the most popular form of payment for online as well as offline purchases. As a consequence, there is substantially more fraud involved in the transaction processes. data mining has an issue for prediction, and data classification, therefore finding occurrences is essential. Unusual events are challenging to be identified due to their irregularity and casualness, but misclassifying unusual events can lead to large financial losses. In this study, an approach for detecting credit card fraud using machine learning methods, such as K-Nearest Neighbors, random forest, decision trees, logistic regression, and support vector machines, is proposed. the research attempts to look at the effectiveness of the classification models while applying both the Oversampling and Undersampling techniques to find instances of fraud in the dataset for fraudulent activities. the experimental study used two days of credit card transactions made by European cardholders in September 2013. To evaluate the models’ performances, confusion matrix, precision, recall, f1_score, cross-validation score, and ROC_AUC score metrics were used. From different experiments of the tested model, it can be easily observed that the performance of all models was better compared with previous literature thus the KNN was the best in almost all metrics used. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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