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Predicting DDoS Attacks Using Machine Learning Algorithms in Building Management Systems

https://doi.org/10.3390/electronics12194142

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What this work says

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

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DDoS attack detection with feature engineering and machine learning: the framework and performance evaluation

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A survey of DDoS attacking techniques and defence mechanisms in the IoT network

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An edge based hybrid intrusion detection framework for mobile edge computing

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SmartDefense: A distributed deep defense against DDoS attacks with edge computing

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A fog computing based approach to DDoS mitigation in IIoT systems

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Implementation of intrusion detection model for DDoS attacks in Lightweight IoT Networks

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Reorganization and discovery of grid information with epidemic tuning

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AIMM: Artificial Intelligence Merged Methods for flood DDoS attacks detection

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A Software Defined Networking Architecture for DDoS-Attack in the Storage of Multimicrogrids

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Generalized Optimal Game Theory in virtual decision-makings

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Prediction of Distributed Denial of Service Attacks in SDN using Machine Learning Techniques

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ENFW: an Industrial Firewall for Edge Computing

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Assessment of Protection Schemes and their Security under Denial of Service Attacks

inferredcites →https://doi.org/10.1109/infocom41043.2020.9155493openalex-snapshot

Scheduling DDoS Cloud Scrubbing in ISP Networks via Randomized Online Auctions

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Load Distributed and Benign-Bot Mitigation Methods for IoT DNS Flood Attacks

inferredcites →https://doi.org/10.1109/lcomm.2014.2328587openalex-snapshot

Can a DDoS Attack Meltdown My Data Center? A Simulation Study and Defense Strategies

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Securing building management systems using named data networking

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Towards the Development of a Realistic Multidimensional IoT Profiling Dataset

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New Trends in Intelligent Software Methodologies, Tools and Techniques

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Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic

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Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)

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A BiLSTM-Based DDoS Attack Detection Method for Edge Computing

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Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder

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Preventing MQTT Vulnerabilities Using IoT-Enabled Intrusion Detection System

What points at this work

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

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