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Generative Adversarial Network and Auto Encoder based Anomaly Detection in Distributed IoT Networks

https://doi.org/10.1109/globecom42002.2020.9348244

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-658-40442-0_9openalex-snapshot

Generative Adversarial Nets

inferredcites →https://doi.org/10.1016/j.future.2019.05.041openalex-snapshot

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

inferredcites →https://doi.org/10.1016/j.jnca.2012.09.004openalex-snapshot

Intrusion detection system: A comprehensive review

inferredcites →https://doi.org/10.1080/02564602.2016.1141077openalex-snapshot

A Survey on 5G Network Technologies from Social Perspective

inferredcites →https://doi.org/10.1109/bigdata.2018.8622243openalex-snapshot

IoT Devices Recognition Through Network Traffic Analysis

inferredcites →https://doi.org/10.1109/icassp.2019.8683197openalex-snapshot

DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification

inferredcites →https://doi.org/10.1109/icassp40776.2020.9053949openalex-snapshot

Sequential IoT Data Augmentation Using Generative Adversarial Networks

inferredcites →https://doi.org/10.1109/icdcs.2019.00080openalex-snapshot

DÏoT: A Federated Self-learning Anomaly Detection System for IoT

inferredcites →https://doi.org/10.1109/icdm.2018.00088openalex-snapshot

Adversarially Learned Anomaly Detection

inferredcites →https://doi.org/10.1109/mprv.2018.03367731openalex-snapshot

N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders

inferredcites →https://doi.org/10.1109/spw.2018.00013openalex-snapshot

Machine Learning DDoS Detection for Consumer Internet of Things Devices

inferredcites →https://doi.org/10.1109/vtcspring.2019.8746594openalex-snapshot

Crossfire Attack Detection Using Deep Learning in Software Defined ITS Networks

inferredcites →https://doi.org/10.1145/3178582openalex-snapshot

A Survey of Random Forest Based Methods for Intrusion Detection Systems

inferredcites →https://doi.org/10.1155/2014/240217openalex-snapshot

A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network

inferredcites →https://doi.org/10.14722/diss.2020.23006openalex-snapshot

ADROIT: Detecting Spatio-Temporal Correlated Attack-Stages in IoT Networks

inferredcites →https://doi.org/10.23919/icact.2018.8323687openalex-snapshot

A deep auto-encoder based approach for intrusion detection system

inferredcites →https://doi.org/10.23919/icact.2018.8323688openalex-snapshot

A deep auto-encoder based approach for intrusion detection system

inferredcites →https://doi.org/10.3156/jsoft.29.5_177_2openalex-snapshot

GAN（Generative Adversarial Nets）

inferredcites →https://doi.org/10.48550/arxiv.1711.04340openalex-snapshot

Data Augmentation Generative Adversarial Networks

inferredcites →https://doi.org/10.48550/arxiv.1810.10863openalex-snapshot

GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

What points at this work

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

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Adversarial Attacks on Deep Learning-based Network Intrusion Detection Systems: A Taxonomy and Review

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Data-Driven Evaluation of Intrusion Detectors: A Methodological Framework

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An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review

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Enhancing Detection Accuracy and Security in Deep Learning

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Advances in deep learning intrusion detection over encrypted data with privacy preservation: a systematic review

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Security and privacy of IoT applications

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One-class IoT anomaly detection system using an improved interpolated deep SVDD autoencoder with adversarial regularizer

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Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review

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Evaluation of the Performance of Unsupervised Learning Algorithms for Intrusion Detection in Unbalanced Data Environments

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Wireless Network Simulation to Create Machine Learning Benchmark Data

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Clustering Enabled Robust Intrusion Detection System for Big Data Using Hadoop–PySpark

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Generative Adversarial Networks for Robust Anomaly Detection in Noisy IoT Environments

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Detecting Malicious Traffic using JA3 Fingerprints Attributed ML Approach

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IoT Attack Classification Based on Generative Adversarial Networks

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How Reliable are the Deep Learning-based Anomaly Detectors? A Comprehensive Reliability Analysis of Autoencoder-based Anomaly Detectors

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ADEPT: Detection and Identification of Correlated Attack Stages in IoT Networks

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IoT Network Intrusion Detection Using Machine Learning Techniques

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Asynchronous Decentralized Federated Anomaly Detection for 6G Networks

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Effective Multitask Deep Learning for IoT Malware Detection and Identification Using Behavioral Traffic Analysis

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Automated, Reliable Zero-Day Malware Detection Based on Autoencoding Architecture

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P 3 AD: Privacy-Preserved Payload Anomaly Detection for Industrial Internet of Things

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Fined-grained unmanned aerial vehicle anomaly detection by fusing multi-domain features

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Network based Anomaly detection using Self-defined One Class Support Vector Machine with Grey Wolf Optimizer for Internet of Things Environment

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Adversarial Attacks on Deep Learning-Based Network Intrusion Detection Systems: A Taxonomy and Review

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