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Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-387-84858-7openalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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An uncertainty-managing batch relevance-based approach to network anomaly detection
inferredcites →https://doi.org/10.1016/j.bushor.2015.03.008openalex-snapshot
The Internet of Things (IoT): Applications, investments, and challenges for enterprises
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Anomaly-based network intrusion detection: Techniques, systems and challenges
inferredcites →https://doi.org/10.1016/j.future.2019.03.043openalex-snapshot
A deep learning approach for proactive multi-cloud cooperative intrusion detection system
inferredcites →https://doi.org/10.1016/j.iot.2019.100059openalex-snapshot
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
inferredcites →https://doi.org/10.1016/j.procs.2016.07.238openalex-snapshot
A Framework for Fast and Efficient Cyber Security Network Intrusion Detection Using Apache Spark
inferredcites →https://doi.org/10.1016/j.procs.2018.01.091openalex-snapshot
Performance evaluation of intrusion detection based on machine learning using Apache Spark
inferredcites →https://doi.org/10.1016/s0167-9473(01)00065-2openalex-snapshot
Stochastic gradient boosting
inferredcites →https://doi.org/10.1049/cp.2018.0003openalex-snapshot
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Machine Learning With Big Data: Challenges and Approaches
inferredcites →https://doi.org/10.1109/bigdatacongress.2017.25openalex-snapshot
Zero-Day Attack Identification in Streaming Data Using Semantics and Spark
inferredcites →https://doi.org/10.1109/comst.2015.2494502openalex-snapshot
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
inferredcites →https://doi.org/10.1109/iemcon.2016.7746264openalex-snapshot
Real-time Support Vector Machine based Network Intrusion Detection system using Apache Storm
inferredcites →https://doi.org/10.1109/msp.2018.2825478openalex-snapshot
IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?
inferredcites →https://doi.org/10.1155/2016/5974586openalex-snapshot
5G: Vision and Requirements for Mobile Communication System towards Year 2020
inferredcites →https://doi.org/10.1155/2018/9562801openalex-snapshot
An Efficient High-Throughput and Low-Latency SYN Flood Defender for High-Speed Networks
inferredcites →https://doi.org/10.1186/s40537-018-0145-4openalex-snapshot
Intrusion detection model using machine learning algorithm on Big Data environment
inferredcites →https://doi.org/10.14722/ndss.2018.23191openalex-snapshot
Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations
inferredcites →https://doi.org/10.14722/ndss.2018.23204openalex-snapshot
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
inferredcites →https://doi.org/10.17632/zvsk3k9cf2openalex-snapshot
Small versions of the extracted features datasets for 9 attacks on IP camera and IoT networks generated by Mirskey et al (2018).
inferredcites →https://doi.org/10.18494/sam.2018.1789openalex-snapshot
Detection and Analysis of Distributed Denial-of-service in the Internet of Things—Employing an Artificial Neural Network and Apache Spark Platform
inferredcites →https://doi.org/10.3390/bdcc3010001openalex-snapshot
Comparative Study between Big Data Analysis Techniques in Intrusion Detection
inferredcites →https://doi.org/10.4018/978-1-5225-0463-4.ch010openalex-snapshot
Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things
inferredcites →https://doi.org/10.4018/978-1-5225-9866-4.ch030openalex-snapshot
Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy
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Machine learning for mobile network payment security evaluation system
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A Novel Multi‐Wavelet Oriented Auto‐Encoder for Intrusion Detection in IoT System
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Toward a Unique IoT Network via Single Sign-On Protocol and Message Queue
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Evaluation of Supervised Machine Learning Algorithms for Multi-class Intrusion Detection Systems
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IoT Security: Challenges, IDS Evolution and AI Defensive Schemes: A Review
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An Overview of Security Intelligence in IoT Applications with Learning Approaches
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A Multi-Agent-Based System for Intrusion Detection
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Multi-class Classification Approach for Smart Intrusion Attack Detection Using AI and ML Classifiers
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Architecture Based Classification for Intrusion Detection System Using Artificial Intelligence and Machine Learning Classifiers
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Hybrid Machine Learning Framework for Network Intrusion Detection in IoT-Based Environments
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Intrusion Detection System for IOT Botnet Attacks Using Deep Learning
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Cost based Random Forest Classifier for Intrusion Detection System in Internet of Things
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An Enhanced Approach for Intrusion Detection Modeling to Secure IoT Network by Using Big Data Analytics
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Scalable Intrusion Detection in IoT Networks: Evaluating PySpark Pipelines and Design Trade-Offs
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