Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM

Li, Aichuan; Yi, Shujuan

doi:10.1155/2022/5448647

Cited by 13 publications

(8 citation statements)

References 29 publications

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“…An improved intelligent intrusion detection method was investigated in [31] via DL. However, the efficient algorithms failed to improve the accuracy of the designed model.…”

Section: Related Workmentioning

confidence: 99%

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

2024

IJATEE

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Cloud computing (CC) permits more users for storing as well as distributing their applications and data. CC continuously evolving technology that can improve agility, availability, collaboration, and scalability of data. It offers on-demand solution for different applications namely data storage, servers, databases, networking, and software. One of the most challenging issues in CC is detecting attacks. Parallel sponge-based authenticated encryptionwith sidechannel protection and adversary-invisible nonces (PSASPIN) to sponge construction was designed [1]. Moreover, leveled implementation has also utilized in implementing key generation via pseudo random function (PRF). Next, data preprocessing has utilized to minimize the code size. Finally, security proof has ensured using game theory and therefore processing shorter messages significantly. The authenticated encryption (AE) schemes such as security, performance, and efficiency are enhanced.*Author for correspondence Despite improvement found in terms of communication complexity and throughput were not focused.Side channel assault also utilizes physical data which leaks as of cryptographic tool. For avoiding from these attacks, divide-and-conquer approach is employed. Here, malicious user splits key into subkeys, endeavors for recuperating sub-keys distinctly as well as integrates them to create key. Reinforcement-strategy-based side-channel attack has designed in [2] with divide-and-conquer. Initially, the power leakage models are modified, and accuracy has improved. Next, the recovered sub-keys are integrated during the attack phase through validation therefore mitigating side-channel attacks significantly and improving the successful recovery rate. Though successful recovery rate has ensured but the detection time and accuracy have not focused. The disclosure of the cloud storage in the recent years carries enormous east to person life. Moreover, business establishments as well as organizations transmitted huge data for storage also. However, the data privacy

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“…An improved intelligent intrusion detection method was investigated in [31] via DL. However, the efficient algorithms failed to improve the accuracy of the designed model.…”

Section: Related Workmentioning

confidence: 99%

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

2024

IJATEE

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“…Data was preprocessed using one-hot encoding and min-max normalization by authors in [15], which achieved an accuracy of 96.3% on CNN and Bi-LSTM hybrid methods on the multiclass NSL-KDD dataset. Using preprocessed on given NSL-KDD data, researchers in [16] applied the hybrid model using CNN and BLSTM algorithm with a 95.4% accuracy rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing the Network Anomaly Detection using CNN-Bidirectional LSTM Hybrid Model and Sampling Strategies for Imbalanced Network Traffic Data

Acharya,

Annamalai,

Chouikha

2024

Adv. sci. technol. eng. syst. j.

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The cybercriminal utilized the skills and freely available tools to breach the networks of internet-connected devices by exploiting confidentiality, integrity, and availability. Network anomaly detection is crucial for ensuring the security of information resources. Detecting abnormal network behavior poses challenges because of the extensive data, imbalanced attack class nature, and the abundance of features in the dataset. Conventional machine learning approaches need more efficiency in addressing these issues. Deep learning has demonstrated greater effectiveness in identifying network anomalies. Specifically, a recurrent neural network model is created to recognize the serial data patterns for prediction. We optimized the hybrid model, the convolutional neural network combined with Bidirectional Long-Short Term Memory (BLSTM), to examine optimizers (Adam, Nadam, Adamax, RMSprop, SGD, Adagrad, Ftrl), number of epochs, size of the batch, learning rate, and the Neural Network (NN) architecture. Examining these hyperparameters yielded the highest accuracy in anomaly detection, reaching 98.27% for the binary class NSL-KDD and 99.87% for the binary class UNSW-NB15. Furthermore, recognizing the inherent class imbalance in network-based anomaly detection datasets, we explore the sampling techniques to address this issue and improve the model's overall performance. The data imbalance problem for the multiclass network anomaly detection dataset is addressed by using the sampling technique during the data preprocessing, where the random over-sampling methods combined with the CNN-based BLSTM model outperformed by producing the highest performance metrics, i.e., detection accuracy for multiclass NSL-KDD and multiclass UNSW-NB15 of 99.83% and 99.99% respectively. Evaluation of performance, considering accuracy and F1-score, indicated that the proposed CNN BLSTM hybrid network-based anomaly detection outperformed other existing methods for network traffic anomaly detection. Hence, this research contributes valuable insights into selecting hyperparameters of deep learning techniques for anomaly detection in imbalanced network datasets, providing practical guidance on choosing appropriate hyperparameters and sampling strategies to enhance model robustness in realworld scenarios.

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“…Tis is done by fxing the means and variances of the input layers during a normalization stage. At each hidden layer, batch normalization is the hidden weapon that addresses the unstable gradient issue for many of the deep learning architectures [18]. Tis eliminates the possibility of divergence and enables the use of considerably greater learning rates.…”

Section: Batch Normalizationmentioning

confidence: 99%

Intrusion Detection for Cyber-Physical Security System Using Long Short-Term Memory Model

Bashar

Kashem

Paul

2022

Scientific Programming

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In the present context, the deep learning approach is highly applicable for identifying cyber-attacks on intrusion detection systems (IDS) in cyber-physical security systems. As a key part of network security defense, cyber-attacks can change and penetrate the security of the network system, then, the role of an IDS is to detect suspicious behaviors and act appropriately to protect the network from the onset of attacks. Machine learning and deep learning techniques are important for current intrusion detection systems. However, traditional intrusion detection systems are far from being able to quickly and accurately identify complex and diverse network attacks and obtained low accuracy and detection rates, thus, these methods frequently fail to manage big amounts of data in a vast network infrastructure and utilize a lot of features leads to poor performance. For addressing these issues and improving the accuracy and scalability, in this paper, we have implemented the deep learning method based on a new approach multilayer long short-term memory (LSTM) model for detecting attacks on a network. The novelty of the proposed scheme is that the optimum multilayer architecture is built to achieve maximum accuracy in the network architecture in order to boost performance using stacking multiple layers of LSTM cells in a more effective manner, and better stability to perform consistently in both binary classification and multiclass classification on NSL-KDD datasets. Experimental tests with KDDTest + datasets show that the proposed multilayer LSTM model provides outstanding results with 95% and 96% accuracy, respectively, in binary and multiclass classification. In order to deal with actual datasets and obtain good performance in the network design, our optimum multilayer architecture must be put into practice in order to execute real-time applications. Therefore, the results are better and more robust than the existing state-of-the-art methods.

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Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM

Cited by 13 publications

References 29 publications

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

Enhancing the Network Anomaly Detection using CNN-Bidirectional LSTM Hybrid Model and Sampling Strategies for Imbalanced Network Traffic Data

Intrusion Detection for Cyber-Physical Security System Using Long Short-Term Memory Model

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