Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

Marzouk, Radwa; Alrowais, Fadwa; Negm, Noha; Alkhonaini, Mimouna Abdullah; Hamza, Manar Ahmed; Rizwanullah, Mohammed; Yaseen, Ishfaq; Motwakel, Abdelwahed

doi:10.32604/cmc.2022.027483

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Marzouk et al [13] projected a new hybrid DL with metaheuristics-assisted intrusion detection (HDL-MEID) method. This approach devises a novel chaotic mayfly optimizer (CMFO) based clustering model for efficiently selecting the CH and establishing clusters.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Anbalagan,

Rao,

Alluri

et al. 2024

IJEER

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Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility. Next, the IID-SBODL technique applies Echo State Network (ESN) model for effectual recognition and classification of the intrusions. Finally, the SBO algorithm optimizes the configuration of the ESN, boosting its capability for precise identification of anomalies and significant security breaches within IIoT networks. By widespread simulation evaluation, the experimental results pointed out that the IID-SBODL technique reaches maximum detection rate and improves the security of the IIoT environment. Through comprehensive experimentation on both UNSW-NB15 and UCI SECOM datasets, the model exhibited exceptional performance, achieving an average accuracy of 99.55% and 98.87%, precision of 98.90% and 98.93%, recall of 98.87% and 98.80%, and F-score of 98.88% and 98.87% for the respective datasets. The IID-SBODL model contributes to the development of robust intrusion detection mechanisms for safeguarding critical industrial processes in the era of interconnected and smart IIoT environments.

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Section: ░ 2 Related Workmentioning

confidence: 99%

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Anbalagan,

Rao,

Alluri

et al. 2024

IJEER

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“…A number of reviews has been introduced to compare between various symmetric and nonsymmetric cryptography algorithms [11][12][13][14][15][16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Split-n-Swap: A New Modification of the Twofish Block Cipher Algorithm

Sayed¹,

Mahrous²,

Elgeldawi³

2023

Computers, Materials &Amp; Continua

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Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern. A robust data security system should protect the information assets of any organization against cybercriminal activities. The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence. But when it comes to security, it is not the preferred cryptographic algorithm to use compared to other algorithms that have shown better security. Many applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard (AES) algorithm to construct their main security wall. In this paper, a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast convergence. The new algorithm has been named Split-n-Swap (SnS). Performance analysis of the new modification algorithm has been performed using different measurement metrics. The experimental results show that the complexity of the SnS algorithm exceeds that of the original Twofish algorithm while maintaining reasonable values for encryption and decryption times as well as memory utilization. A detailed analysis is given with the strength and limitation aspects of the proposed algorithm.

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“…Intrusion detection technology is an active security defense strategy, which can actively detect and identify abnormal behaviors in network access data. It is an important security defense measure deployed behind the firewall and is the second firewall for system security [12][13][14]. e Internet is only a small part of the huge industrial Internet of things system [15].…”

Section: Related Workmentioning

confidence: 99%

Network Intrusion Detection Method Based on Multi-Scale CNN in Internet of Things

Yin

Chen

2022

Mobile Information Systems

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Network intrusion detection is a powerful means to identify and analyze the state of the Internet of things. For the reliability requirements of the Internet of things, an intrusion detection analysis method of the Internet of things based on a deep network model is proposed. First, based on the Inception network architecture as the backbone network, this method constructs a multi-scale convolutional neural network (M-CNN) intrusion detection analysis network model. The long-term and short-term memory network models are introduced into M-CNN to enhance the local feature extraction ability of the model. At the same time, batch normalization and global average pooling layers are introduced to make the data distribution of each layer uniform, reduce the model training time, reduce the model calculation gradient, and further improve the efficiency of the network. The simulation experiment takes the KDDcup99 data set as an example, and the results show that the M-CNN intrusion detection model can achieve better results. The precision P r e and recall R e of the detection model are 93.90% and 93.59%, respectively.

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Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

Cited by 4 publications

References 24 publications

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Split-n-Swap: A New Modification of the Twofish Block Cipher Algorithm

Network Intrusion Detection Method Based on Multi-Scale CNN in Internet of Things

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