LSTM-BA: DDoS Detection Approach Combining LSTM and Bayes

Li, Yan; Lu, Yifei

doi:10.1109/cbd.2019.00041

Cited by 61 publications

(25 citation statements)

References 20 publications

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“…Therefore, RNNs are suitable for IDSs to learn the different features of network traffic and recognize attacks. In particular, we employ an advanced RNN model, namely, the long short-term memory (LSTM) model [59,60]. The LSTM model is composed of complex cells, which allows the learning of both long-and short-term dependencies.…”

Section: Figure 1: General Architecture Of Autoencoder-based Representation Learning Of Traffic Featuresmentioning

confidence: 99%

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Lutsiv¹,

Maksymyuk²,

Beshley³

et al. 2022

Computers, Materials &Amp; Continua

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The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. An additional feature of the proposed IDS is that it is trained with an optimized dataset, where the number of features is reduced by 94% without classification accuracy loss. Thus, the proposed IDS remains stable in response to slight system perturbations, which do not represent network anomalies. The proposed approach is evaluated under different simulation scenarios and provides a 99% detection accuracy over known datasets while reducing the training time by an order of magnitude.

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Section: Figure 1: General Architecture Of Autoencoder-based Representation Learning Of Traffic Featuresmentioning

confidence: 99%

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Lutsiv¹,

Maksymyuk²,

Beshley³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Figure 1 shows the inner design of an LSTM unit cell, according to Li and Lu [60]. Formally, the LSTM cell model is characterized as follows:…”

Section: Long Short-term Memorymentioning

confidence: 99%

Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

et al. 2021

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The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

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“…LSTM is another recurrent model that can address the RNN memory problem. In previous studies [12,20,21], LSTM showed major improvements over what RNNs could accomplish. LSTM is designed to avoid long dependency problems and can remember long historical information and gain high accuracy in EDoS detection with a sequence flow-based method.…”

Section: Related Workmentioning

confidence: 99%

MAN-EDoS: A Multihead Attention Network for the Detection of Economic Denial of Sustainability Attacks

Park

2021

Electronics

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Cloud computing is one of the most modernized technology for the modern world. Along with the developments in the cloud infrastructure comes the risk of attacks that exploit the cloud services to exhaust the usage-based resources. A new type of general denial attack, called “economic denial of sustainability” (EDoS), exploits the pay-per-use service to scale-up resource usage normally and gradually over time, finally bankrupting a service provider. The stealthiness of EDoS has made it challenging to detect by most traditional mechanisms for the detection of denial-of-service attacks. Although some recent research has shown that multivariate time recurrent models, such as recurrent neural networks (RNN) and long short-term memory (LSTM), are effective for EDoS detection, they have some limitations, such as a long processing time and information loss. Therefore, an efficient EDoS detection scheme is proposed, which utilizes an attention technique. The proposed attention technique mimics cognitive attention, which enhances the critical features of the input data and fades out the rest. This reduces the feature selection processing time by calculating the query, key and value scores for the network packets. During the EDoS attack, the values of network features change over time. The proposed scheme inspects the changes of the attention scores between packets and between features, which can help the classification modules distinguish the attack flows from network flows. On another hand, our proposal scheme speeds up the processing time for the detection system in the cloud. This advantage benefits the detection process, but the risk of the EDoS is serious as long as the detection time is delayed. Comprehensive experiments showed that the proposed scheme can enhance the detection accuracy by 98%, and the computational speed is 60% faster compared to previous techniques on the available datasets, such as KDD, CICIDS, and a dataset that emerged from the testbed. Our proposed work is not only beneficial to the detection system in cloud computing, but can also be enlarged to be better with higher quality of training and technologies.

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LSTM-BA: DDoS Detection Approach Combining LSTM and Bayes

Cited by 61 publications

References 20 publications

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press

MAN-EDoS: A Multihead Attention Network for the Detection of Economic Denial of Sustainability Attacks

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