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Efficient Distributed Preprocessing Model for Machine Learning-Based Anomaly Detection over Large-Scale Cybersecurity Datasets

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Investigating the problem of IDS false alarms: An experimental study using Snort
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ISDI: A New Window-Based Framework for Integrating IoT Streaming Data from Multiple Sources
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The Future of Cybersecurity: Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Cyberspace
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Bias in information-based measures in decision tree induction
inferredcites →https://doi.org/10.1016/j.cose.2011.12.012openalex-snapshot
Toward developing a systematic approach to generate benchmark datasets for intrusion detection
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UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSs
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A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection
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Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues
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Real-time frequency-based noise-robust Automatic Speech Recognition using Multi-Nets Artificial Neural Networks: A multi-views multi-learners approach
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Efficient tree classifiers for large scale datasets
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Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison
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A Framework for Fast and Efficient Cyber Security Network Intrusion Detection Using Apache Spark
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Performance evaluation of intrusion detection based on machine learning using Apache Spark
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Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
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Random Forests
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Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies
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A detailed analysis of the KDD CUP 99 data set
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A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
inferredcites →https://doi.org/10.1109/fskd.2015.7381996openalex-snapshot
False positive elimination in intrusion detection based on clustering
inferredcites →https://doi.org/10.1109/gcct.2015.7342657openalex-snapshot
Blacklist-based malicious IP traffic detection
inferredcites →https://doi.org/10.1109/icacci.2017.8126009openalex-snapshot
Applying convolutional neural network for network intrusion detection
inferredcites →https://doi.org/10.1109/icmla.2016.0040openalex-snapshot
Toward an Online Anomaly Intrusion Detection System Based on Deep Learning
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Performance and Scalability of GPU-Based Convolutional Neural Networks
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Nearest neighbor pattern classification
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ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1155/2018/4680867openalex-snapshot
Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment
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A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets
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Classification And Regression Trees
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Searching for Activation Functions

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Effective Pre-processing of Datasets by Removing Anomalies with OptiContamFinder on Isolation Forest for Stock Market forecasting and Nowcasting
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Machine Learning Model for Anomaly Detection in Big Data for Health Care Applications
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An Ensemble Intrusion Detection System based on Acute Feature Selection
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Prepare for trouble and make it double! Supervised – Unsupervised stacking for anomaly-based intrusion detection
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Using Generative Adversarial Networks for Anomaly Detection in Network Traffic: Advancements in AI Cybersecurity
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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques
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Machine Learning-Based Anomaly Detection in Cloud Virtual Machine Resource Usage
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Artificial Intelligence for Anomaly Detection in Cyber Defense: A Critical Review of Methodological Trends, Datasets, and Explainability
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Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges
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Design and Evaluation of Unsupervised Machine Learning Models for Anomaly Detection in Streaming Cybersecurity Logs
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An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets
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