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Memory-Efficient Deep Learning for Botnet Attack Detection in IoT Networks

https://doi.org/10.3390/electronics10091104

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What this work says

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

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Random Neural Network for Lightweight Attack Detection in the IoT

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Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks

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SVELTE: Real-time intrusion detection in the Internet of Things

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SDN orchestration to combat evolving cyber threats in Internet of Medical Things (IoMT)

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Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset

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Detecting botnet by using particle swarm optimization algorithm based on voting system

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A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework

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Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment

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A survey on internet of things security: Requirements, challenges, and solutions

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Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

inferredcites →https://doi.org/10.1016/j.neucom.2019.02.056openalex-snapshot

Application of deep learning to cybersecurity: A survey

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TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning

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Machine Learning and Deep Learning Methods for Cybersecurity

inferredcites →https://doi.org/10.1109/ccwc.2019.8666588openalex-snapshot

Deep Learning Models for Cyber Security in IoT Networks

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A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

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Botnet Communication Patterns

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A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection

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Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks

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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

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Efficient and Intelligent Attack Detection in Software Defined IoT Networks

inferredcites →https://doi.org/10.1109/icic47613.2019.8985853openalex-snapshot

DDoS Attack Detection Based on Simple ANN with SMOTE for IoT Environment

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Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach

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Real-Time Sensor Fault Detection, Isolation and Accommodation for Industrial Digital Twins

inferredcites →https://doi.org/10.1109/iit50501.2020.9299061openalex-snapshot

Botnet Attack Detection using Machine Learning

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Network Flow based IoT Botnet Attack Detection using Deep Learning

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A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks

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CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques

inferredcites →https://doi.org/10.1109/jiot.2020.3034156openalex-snapshot

Hybrid Deep Learning for Botnet Attack Detection in the Internet-of-Things Networks

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Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture

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A Comprehensive Survey of Databases and Deep Learning Methods for Cybersecurity and Intrusion Detection Systems

inferredcites →https://doi.org/10.1109/mc.2017.62openalex-snapshot

Botnets and Internet of Things Security

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Weaving the Wireless Web: Toward a Low-Power, Dense Wireless Sensor Network for the Industrial IoT

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A Large-Scale Empirical Study on the Vulnerability of Deployed IoT Devices

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DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids

inferredcites →https://doi.org/10.1109/tifs.2019.2898817openalex-snapshot

Modeling, Analysis, and Mitigation of Dynamic Botnet Formation in Wireless IoT Networks

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Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning

What points at this work

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

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Effective Internet of Things botnet classification by data upsampling using generative adversarial network and scale fused bidirectional long short term memory attention model

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RETRACTED: Internet of Things security framework based on near field communication with Otway Rees protocol

inferred← citeshttps://doi.org/10.1002/spy2.355openalex-snapshot

Botnet‐based IoT network traffic analysis using deep learning

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Leveraging Machine Learning and Deep Learning in IoT Security: A Review

inferred← citeshttps://doi.org/10.1007/978-3-031-21311-3_6openalex-snapshot

Resource Efficient Federated Deep Learning for IoT Security Monitoring

inferred← citeshttps://doi.org/10.1007/978-3-031-22677-9_17openalex-snapshot

Federated Learning-Based Intrusion Detection on Non-IID Data

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A Comparative Study of Novelty Detection Models for Zero Day Intrusion Detection in Industrial Internet of Things

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OptiXID: An optimized and eXplainable AI framework for intrusion detection in IoT networks

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Cyber Threat Intelligence Sharing Scheme Based on Federated Learning for Network Intrusion Detection

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Anomaly detection in IOT edge computing using deep learning and instance-level horizontal reduction

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Joined Bi-model RNN with spatial attention and GAN based IoT botnet attacks detection

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Security-aware IoT botnet attack detection framework using dilated and cascaded deep learning mechanism with conditional adversarial autoencoder-based features

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Network security based combined CNN-RNN models for IoT intrusion detection system

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BotDefender: A Collaborative Defense Framework Against Botnet Attacks using Network Traffic Analysis and Machine Learning

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A survey on the contribution of ML and DL to the detection and prevention of botnet attacks

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Development of an intelligent intrusion detection system for IoT networks using deep learning

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A novel feature selection-driven ensemble learning approach for accurate botnet attack detection

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DeBot: A deep learning-based model for bot detection in industrial internet-of-things

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Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm

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Pragmatic Study of Botnet Attack Detection In An IoT Environment

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Botnet detection in the internet-of-things networks using convolutional neural network with pelican optimization algorithm

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QAE-IDS: DDoS anomaly detection in IoT devices using Post-Quantization Training

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Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

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Multi-Stage Deep Learning for Intrusion Detection in Industrial Internet of Things

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A Systematic Literature Review: Classifying IoT Botnet Data Features Based on Its Lifecycle

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BoTNetShield: Intelligent IoT Security Framework using Convolution Neural Network

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Gaussian Weight Black Widow Optimization based Feature Selection and Deep Learning for Botnet Attack Detection in IoT Healthcare Systems

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MDUF: Multi-Dataset Unified Framework for IoT Network Anomaly Detection

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Comparative Study of Machine Learning and Deep Learning Models for IoT Botnet Detection

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A Comprehensive Review of Internet-of-Things (IoT) Botnet Detection Techniques

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