An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Lu, Guanyu; Tian, Xin

doi:10.1155/2021/6631075

Cited by 22 publications

(11 citation statements)

References 45 publications

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“…Lu et al [27] proposed Improved LSTM which is a combination of SAE and Attention-BiLSTM, for Efficient Communication Intrusion Detection. The proposed model was trained with the UNSW-NB15 dataset and achieved a 99.41% accuracy rate in detection.…”

Section: Related Workmentioning

confidence: 99%

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE

et al. 2023

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Machine learning and deep learning techniques are widely used to assess intrusion detection systems (IDS) capable of rapidly and automatically recognizing and classifying cyber-attacks on networks and hosts. However, when destructive attacks are becoming more extensive, more challenges develop, needing a comprehensive response. Numerous intrusion detection datasets are publicly accessible for further analysis by the cybersecurity research community. However, no previous research has examined the performance of the proposed model on a variety of publicly accessible datasets in detail. Due to the dynamic nature of the attack and its rapidly changing attack techniques, the publicly accessible intrusion datasets must be updated and benchmarked regularly. The deep neural network (DNN) and convolutional neural network (CNN) are examined in this article as types of deep learning models for developing a flexible and effective IDS capable of detecting and comparing them with the proposed model in detecting cyber-attacks. The constant development of network behavior and the fast growth of attacks need the development of IDS and the evaluation of many datasets produced over time through static and dynamic methods. This kind of research enables the identification of the most efficient algorithm for identifying future cyber-attacks. We proposed a novel two-stage deep learning technique hybridizing Long-Short Term Memory (LSTM) and Auto-Encoders (AE) for detecting attacks. The CICIDS2017 and CSE-CICDIS2018 datasets are used to determine the optimum network parameters for the proposed LSTM-AE. The experimental results show that the proposed hybrid model works well and is applicable for detecting attacks in modern scenarios.

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Section: Related Workmentioning

confidence: 99%

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE

et al. 2023

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“…If you want the average of the results of the forward and backward directions, it is 'ave'. Generally, the default is concatenation operator, which is a common method of combining forward and backward output vectors [36].…”

Section: B Feature Extraction Of Airborne Network Packets Sequence Ba...mentioning

confidence: 99%

An Abnormal Traffic Detection Model Combined BiIndRNN With Global Attention

Huang

Yang

et al. 2022

IEEE Access

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As time series data with internal correlation, airborne networks traffic data can be used for abnormal detection using Recurrent Neural Network (RNN) and its variants, but existing models are difficult to calculate in parallel and gradient explosion or vanishing easily occurs. To address this problem, we propose a Bidirectional Independent Recurrent Neural Network (BiIndRNN) with parallel computation and adjustable gradient, which can extract the bidirectional structural features of network traffic by forward and backward input and capture the spatial influence in the data flow. In order to establish the dependencies on the forward and backward moments of airborne networks traffic, a model combining Global Attention (GA) with BiIndRNN is proposed to pay more attention to the moments containing essential information. Taking the UNSW-NB15 dataset as the object, the GA expression of the packets feature vector of the airborne networks is derived, feature fusion as well as loss calculation is performed for multiple fully connected layers. The experimental results show that, compared with traditional deep and shallow machine learning and other state-of-the-art technologies, our GA-BiIndRNN model converges faster, the accuracy, precision and F1 scores are all above 99%, and the false positive rate (FPR) is close to 0.36%, which can effectively identify normal and malicious network activities. These results provide a theoretical basis of rapid implementation of protective measures.

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“…In the era of big data, machine learning approaches have been widely implemented in intrusion detection systems (IDS), and part of the research has employed classic machine learning algorithms or their enhancements, such as SVM, K-means, KNN, RF, and so on 1,[7][8][9] , and deep learning algorithms, such as ANN, CNN, LSTM, etc [10][11][12][13][14][15][16] . In the literature 17 , the authors suggest an IDS based on spark and Conv-AE that employs public datasets such as KDD99 for performance evaluation, and the findings indicate that imbalanced datasets affect model performance.…”

Section: Related Workmentioning

confidence: 99%

ID-RDRL: A Deep Reinforcement Learning-based Feature Selection Intrusion Detection Model

Ren

Zeng

Cao

et al. 2022

Preprint

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Network assaults pose significant security concerns to network services; hence, new technical solutions must be used to enhance the efficacy of intrusion detection systems. Existing approaches pay insufficient attention to data preparation and inadequately identify unknown network threats. This paper presents a network intrusion detection model (ID-RDRL) based on RFE feature extraction and reinforcement learning. ID-RDRL filters the optimum subset of features using the RFE feature selection technique, feeds them into a neural network to extract feature information and then trains a classifier using DRL to recognize network intrusions. We utilized CSE-CIC-IDS2018 as a dataset and conducted tests to evaluate the model's performance, which is comprised of a comprehensive collection of actual network traffic. The experimental results demonstrate that the proposed ID-RDRL model can select the optimal subset of features, remove approximately 80 percent of redundant features, and learn the selected features through DRL to enhance the IDS performance for network attack identification. In a complicated network environment, it has promising application potential in IDS.

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An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Cited by 22 publications

References 45 publications

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE

An Abnormal Traffic Detection Model Combined BiIndRNN With Global Attention

ID-RDRL: A Deep Reinforcement Learning-based Feature Selection Intrusion Detection Model

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