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Capturing low-rate DDoS attack based on MQTT protocol in software Defined-IoT environment

https://doi.org/10.1016/j.array.2023.100316

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What this work says

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

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Real-Time Data Analytics for Large Scale Sensor Data

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Flow level detection and filtering of low-rate DDoS

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Cyber threat intelligence using PCA-DNN model to detect abnormal network behavior

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Low-rate DoS attack detection method based on hybrid deep neural networks

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Efficient DDoS attacks mitigation for stateful forwarding in Internet of Things

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Low-Rate DDoS Attack Detection Based on Factorization Machine in Software Defined Network

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2023 IEEE Conference on Network Function Virtualization and Software Defined Networks

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Exposing Bot Attacks Using Machine Learning and Flow Level Analysis

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A Heterogeneous Framework to Detect Intruder Attacks in Wireless Sensor Networks

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Artificial Intelligence Algorithms for Detecting and Classifying MQTT Protocol Internet of Things Attacks

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An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

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SlowTT: A Slow Denial of Service against IoT Networks

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SlowITe, a Novel Denial of Service Attack Affecting MQTT

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Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

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DDoS Attack Prevention for Internet of Thing Devices Using Ethereum Blockchain Technology

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SOFT COMPUTING BASED AUTONOMOUS LOW RATE DDOS ATTACK DETECTION AND SECURITY FOR CLOUD COMPUTING

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Detection of IoT based DDoS Attacks by Network Traffic Analysis using Feedforward Neural Networks

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What points at this work

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

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