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Network Anomaly Detection with Stochastically Improved Autoencoder Based Models

https://doi.org/10.1109/cscloud.2017.39

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

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A survey of network anomaly detection techniques

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A Survey of Outlier Detection Methods in Network Anomaly Identification

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A detailed analysis of the KDD CUP 99 data set

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Fuzzy classification by evolutionary algorithms

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Network Anomaly Detection: Methods, Systems and Tools

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SA-EAST

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Learning Deep Architectures for AI

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A Deep Learning Approach for Network Intrusion Detection System

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inferred← citeshttps://doi.org/10.52167/1609-1817-2022-121-2-445-457openalex-snapshot

АНАЛИЗ И РАЗРАБОТКА МОДЕЛЕЙ И МЕТОДОВ ОБРАБОТКИ ДАННЫХ ПО ВЫЯВЛЕНИЮ ТИПОВ DDOS-АТАК

inferred← citeshttps://doi.org/10.53730/ijhs.v6ns4.9457openalex-snapshot

DDoS attacks in cloud environment

inferred← citeshttps://doi.org/10.54644/jte.2024.1552openalex-snapshot

Học Đặc Trưng Không Gian Dùng CNN trong Hệ Thống Phát Hiện Xâm Nhập Mạng

inferred← citeshttps://doi.org/10.55730/1300-0632.4091openalex-snapshot

Network intrusion detection based on machine learning strategies: performance comparisons on imbalanced wired, wireless, and software-defined networking (SDN) network traffics

inferred← citeshttps://doi.org/10.59313/jsr-a.1333839openalex-snapshot

Deep learning-based distributed denial of service detection system in the cloud network

inferred← citeshttps://doi.org/10.70675/6e30d74cz483fz44c5za618z9fd5f8aab5dbopenalex-snapshot

Détection d'attaques sur les équipements d'accès à Internet

inferred← citeshttps://doi.org/10.70675/dff50d0dzeb47z4040z8a6bz60efa6e7631bopenalex-snapshot

Optimization of security risk for learning on heterogeneous quality data

inferred← citeshttps://doi.org/10.70675/e3a6c6bcz59d0z4a4az8a42zf0199d456fa0openalex-snapshot

Detecting and visualizing anomalies in heterogeneous network events : Modeling events as graph structures and detecting communities and novelties with machine learning

inferred← citeshttps://doi.org/10.7287/peerj-cs.327v0.1/reviews/1openalex-snapshot

Peer Review #1 of "Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.327v0.1/reviews/2openalex-snapshot

Peer Review #2 of "Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation (v0.1)"

inferred← citeshttps://doi.org/10.7717/peerj-cs.327openalex-snapshot

Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.