An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units

Xu, Congyuan; Shen, Jizhong; Du, Xin; Zhang, Fan

doi:10.1109/access.2018.2867564

Cited by 258 publications

(113 citation statements)

References 28 publications

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“…The LSTM algorithm has good performance at capturing long-term dependencies within a sequence. It is therefore suitable for many industrial applications [25]. The LSTM algorithm's performance has been proved in dealing with time series data [26], thanks to its excellent handling of the long-term dependency issue [27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

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Section: Introductionmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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“…Precision: say the right intrusion estimate fraction with predictable overall intrusions as in (11) Recall: say the allowed intrusion estimate fraction separated by the full amount of valid intrusion possibilities in the test set in (12).…”

Section: Evaluation Discussionmentioning

confidence: 99%

“…NSL-KDD [7] is a data set that is planned to resolve some of KDD99's key difficulties. The attacks in NSLKDD are classified mainly into four types as in Table I [11]. The protocol types in the dataset are shown in the Table II. Preprocessing can be performed to remove symbolic characteristics in the procedure of identification.…”

Section: Proposed Researchmentioning

confidence: 99%

An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

T¹,

Saravanan²

2020

IJACSA

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The necessity for safety of information in a network has inflated due to the impressive growth of web applications. Several methods of intrusion detection are used to detect irregularities which depend on precision, detection frequency, other parameters and are anticipated to familiarize to vigorously varying risk scenes. To accomplish consistent abnormalities detection in a network many machine learning algorithms have been formulated by researchers. A technique based on unsupervised machine learning that use two separate machine learning algorithms to identify anomalies in a network viz convolutional autoencoder and softmax classifier is proposed. These profound models were skilled as well as evaluated on NSLKDD test data sets on the NSLKDD training dataset. Using well-known classification metrics such as accuracy, precision and recall, these machine learning models were assessed. The developed intrusion detection system model experimental findings showed promising outcomes in anomaly detection systems for real-world implementation and is compared with the prevailing definitive machine learning techniques. This strategy increases the detection of network intrusion and offers a renewed intrusion detection study method.

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“…Many scholars used GBDT to deal with network traffic [37]- [41], and get a great result. Hao et al [34] and Xu et al [35] used GRU to utilize the temporal relationship of network flow, however, GRU is a version of RNN model which focuses on the inner long-term temporal relationship in dataflow but have a relatively poor ability to catch spatial information. Therefore, we hope to propose a method to comprehensively utilize the spatial-temporal features of network data to complete intrusion detection with good performance.…”

Section: Related Work a Intrusion Detection Techniquesmentioning

confidence: 99%

A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System

2020

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The development of computer and network technology has provided convenience to our daily life, however, attack and intrusion in network emerge endlessly. Intrusion Detection System (IDS) has been developed to confront network attacks. As a result, the research of IDS is one of the most popular fields in recent years. This paper proposes a Gradient Boosting Decision Tree (GBDT)-paralleled quadratic ensemble learning method for intrusion detection system. We use GBDT to deal with the spatial part of traffic data and use Gated Recurrent Unit (GRU) model with special modification for network traffic to deal with temporal data. Then, in order to combine the spatial feature and temporal feature, we fuse GBDT model and GRU model to make a quadratic ensemble model as our final intrusion detection system. The experimental results based on CICIDS2017 dataset show that the advanced spatial-temporal intrusion detection system based on ensemble learning achieves better accuracy, recall, precision and F1 score than the state-of-the-art methods. The accuracies of detecting benign, port scan, Distributed Denial of Service (DDoS), infiltration and web attack traffic are up to 99.9%, 99.9%, 99.9%, 99.9%, and 99.9%, respectively. We also use our method in Information-Centric Networking (ICN) dataset and the results show our method achieves much better performance compared with existing methods.

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An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units

Cited by 258 publications

References 28 publications

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System

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