Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection

Kim, Jihyun; Kim, Jae Hyun; Le, Thi-Thu-Huong; Kim, Howon

doi:10.1109/platcon.2016.7456805

Cited by 503 publications

(231 citation statements)

References 7 publications

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“…Recently, RNNs [7], especially Long Short-Term Memory (LSTM) [8] model, is being studied due to the computational capabilities for solving many challenging problems, such as intrusion detection [9], action recognition [10], [11], multilingual machine translation [12], multimodal translation between videos and robot control [13]. In these applications, learning the correlations between different time steps is an important step in achieving artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Chemical substance classification using long short-term memory recurrent neural network

Zhang

Liu

Luo

et al. 2017

2017 IEEE 17th International Conference on Communication Technology (ICCT)

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Abstract-This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks (LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy.

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Section: Introductionmentioning

confidence: 99%

Chemical substance classification using long short-term memory recurrent neural network

Zhang

Liu

Luo

et al. 2017

2017 IEEE 17th International Conference on Communication Technology (ICCT)

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“…2) DL-based IDS in IoT: Deep learning is also leveraged for IDS in heterogeneous IoT networks. For instance, Recurrent Neural Network (RNN) is used by Kim et al [134] to train the IDS model which is based on Long Short Term Memory (LSTM) architecture. Similarly, Saeed et al [135] used Random Neural Networks (RaNN) for the realization of efficient and fast anomaly-based intrusion detection in lowpower IoT networks.…”

Section: Anomaly/intrusion Detectionmentioning

confidence: 99%

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

593

270

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The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resourceconstrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML-and DL-based IoT security.

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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%

“…Alrawashdeh and Khaled implemented deep restricted Boltzmann machine and deep belief network and obtained 97.9% detection rate using KDDCup99 dataset 32 . 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 .…”

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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Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection

Cited by 503 publications

References 7 publications

Chemical substance classification using long short-term memory recurrent neural network

Chemical substance classification using long short-term memory recurrent neural network

Machine Learning in IoT Security: Current Solutions and Future Challenges

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

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