Intrusion detection with autoencoder based deep learning machine

Kaynar, Oğuz; Yüksek, Ahmet Gürkan; Görmez, Yasin; Işık, Yunus Emre

doi:10.1109/siu.2017.7960180

Cited by 11 publications

(8 citation statements)

References 3 publications

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“…The model uses backpropagation to adjust weights and increase accuracy. Deep Neural Networks with three or more hidden layers support higher generalization capability in comparison to ANN [11]. In [12], a deep belief network for malicious code detection method was reported that included an autoencoder based dimensionality reduction technique.…”

Section: Background Literaturementioning

confidence: 99%

Intrusion Detection and Classification with Autoencoded Deep Neural Network

Rezvy

Petridis

Lasebae

et al. 2019

Innovative Security Solutions for Information Technology and Communications

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Section: Background Literaturementioning

confidence: 99%

Intrusion Detection and Classification with Autoencoded Deep Neural Network

Rezvy

Petridis

Lasebae

et al. 2019

Innovative Security Solutions for Information Technology and Communications

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“…Various methods have been proposed in the literature for network anomaly detection including standard machine learning classifiers 4–29 and deep learning techniques 30–47 . Muda et al performed clustering before classification and compared the single classifiers with hybrid classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Kim et al implemented long short‐term memory recurrent neural network using KDDCup99 dataset and obtained 96.93% accuracy 34 . Kaynar et al obtained 99.42% accuracy with a method that uses stacked autoencoder followed by the softmax classification layer on KDDCup99 dataset 35 . Van et al implemented a deep belief network on KDDCup99 dataset and obtained 98% accuracy 36 .…”

Section: Related Workmentioning

confidence: 99%

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Görmez

Aydın

Karademir³

et al. 2020

Int J Communication

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Summary Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.

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“…The model is trained on time series data and it detects anomalies collectively by observing the prediction error from a number of time steps. The application of AE is discussed for ID by [445]. In [447], SAE and stacked RBM models are studied for anomaly based network IDS.…”

Section: A Deep Learning In Intrusion Detectionmentioning

confidence: 99%