Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

Yao, Ruizhe; Wang, Ning; Liu, Zhihui; Chen, Peng; Sheng, Xianjun

doi:10.3390/s21020626

Cited by 86 publications

(29 citation statements)

References 43 publications

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“…CNN layers are used as an extraction feature from the input data, while LSTM is combined with CNN to allow sequential prediction in the CNN-LSTM system. CNN takes information from spatial data, applies it to the LSTM structure to generate the description [ 54 , 55 ], and classifies the intrusion detection system. The CNN-LSTM network effectively preserves the spatiotemporal associations and continuously beats the connected LSTM (FC-LSTM) model in precipitation prediction, according to the results of the experiment.…”

Section: Methodsmentioning

confidence: 99%

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Aldhyani

Alkahtani

2022

Sensors

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Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security.

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Section: Methodsmentioning

confidence: 99%

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Aldhyani

Alkahtani

2022

Sensors

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“…However, the CNN model can also help design efficient systems for security purposes. e CNN algorithm is similar to the ordinary neural network: the CNN algorithm consists of four main layers, namely, the input layer, convolutional layer, pooling layer, and fully connected layer [41,42].…”

Section: Convolutional Neural Network (Cnn) Cnn Is a Deepmentioning

confidence: 99%

Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications

Alkahtani

Aldhyani

2021

Security and Communication Networks

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The Internet of Things (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices. An accurate system to identify malicious attacks on the IoT environment has become very important for minimizing security risks on IoT devices. Botnet attacks are among the most serious and widespread attacks, and they threaten IoT devices. Motionless IoT devices have a security weakness due to lack of sufficient memory and computation results for a security platform. In addition, numerous existing systems present themselves for finding unknown patterns from IoT networks to improve security. In this study, hybrid deep learning, a convolutional neural network and long short-term memory (CNN-LSTM) algorithm, was proposed to detect botnet attacks, namely, BASHLITE and Mirai, on nine commercial IoT devices. Extensive empirical research was performed by employing a real N-BaIoT dataset extracted from a real system, including benign and malicious patterns. The experimental results exposed the superiority of the CNN-LSTM model with accuracies of 90.88% and 88.61% in detecting botnet attacks from doorbells (Danminin and Ennio brands), whereas the proposed system achieved good accuracy (88.53%) in identifying botnet attacks from thermostat devices. The accuracies of the proposed system in detecting botnet attacks from security cameras were 87.19%, 89.23%, 87.76%, and 89.64%, with respect to accuracy metrics. Overall, the CNN-LSTM model was successful in detecting botnet attacks from various IoT devices with optimal accuracy.

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“…The used metrics are: accuracy, precision, recall and F-score, which are usually used in evaluation of methods for anomaly detection. The definitions of mentioned metrics are shown below [51]: Accuracy describes how the trained model is right.…”

Section: A Metricsmentioning

confidence: 99%

Network Anomaly Detection With Temporal Convolutional Network and U-Net Model

2021

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Anomaly detection in network traffic is one of the key techniques to ensure security in future networks. Today, the importance of this topic is even higher, since the network traffic is growing and there is a need to have smart algorithms, which can automatically adapt to new network conditions, detect threats and recognize the type of the possible network attack. Nowadays, there are a lot of different approaches, some of them have reached relatively sufficient accuracy. However, the majority of works are being tested on old datasets, which do not reflect current network conditions and it leads to overfitted results. This is caused by high redundancy of the data and because they fail to reflect the performance of the latest methods in the real-world anomaly detection applications. In this work, we applied a couple of new methods based on convolutional neural networks: U-Net based and Temporal convolutional network based for network attack classification. We trained and evaluated methods on the old dataset KDD99 and the modern large-scale one CSE-CIC-IDS2018. According to results, Temporal convolutional network with LSTM has achieved accuracy 92% and 97% on the KDD99 and the CSE-CIC-IDS2018 respectively, the U-Net model has accuracy 93% and 94% on the KDD99 and the CSE-CIC-IDS2018 respectively. Additionally, we utilized the focal loss function in the Temporal convolutional network with Long Short-Term Memory model, which has positive effect on class imbalance in time-series data. We showed, that the Temporal convolutional network in combination with Long Short-Term Memory network and U-Net model can give higher accuracy compared to other network architectures for network traffic classification. In this work we also proved, that methods trained on the old dataset can easily overfit during training and achieve relatively good results on the testing set, but at the same time, these methods are not so successful on more complex and actual data.

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Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

Cited by 86 publications

References 43 publications

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications

Network Anomaly Detection With Temporal Convolutional Network and U-Net Model

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