A Snapshot Ensemble Deep Neural Network Model for Attack Detection in Industrial Internet of Things

Rouzbahani, Hossein Mohammadi; Bahrami, Amir Hossein; Karimipour, Hadis

doi:10.1007/978-3-030-76613-9_10

Cited by 9 publications

(6 citation statements)

References 42 publications

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“…𝑜𝑠𝑡 𝑖 , ∀𝑖 ∈ 𝑛 𝑃𝑜𝑝 (5) cos 𝑡 𝑖 = { Similar to last evolutionary that is generated on optimizer, superiority was demoralized in order to supply an optimum answer at each iteration. At the time of mating, males alike all bird uses their energies for garnishing as well as constructing the bower.…”

Section: 𝑝𝑜𝑝 (𝑖) 𝑃𝑜𝑠 = 𝑟𝑎𝑛𝑑(1 𝑛 𝑣𝑎𝑟 ) ⋅ (𝑉𝑎𝑟mentioning

confidence: 99%

“…Generally, the security aims include privacy, in terms of data that is not improperly released to the individuals must be destroyed [4]. Hence, society is becoming helpless to cyber-threats like denial-of-service (DoS) attacks by hackers because of the limitless present IoT-based connected devices [5]. For example, that reject direct access to devices, etc.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

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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: 𝑝𝑜𝑝 (𝑖) 𝑃𝑜𝑠 = 𝑟𝑎𝑛𝑑(1 𝑛 𝑣𝑎𝑟 ) ⋅ (𝑉𝑎𝑟mentioning

confidence: 99%

Section: ░ 1 Introductionmentioning

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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show abstract

“…Consequently, a snapshot ensemble that develops multiple models from a single training process is introduced as the solution. This technique combines different models' predictions while saving models during the training phase and employing them to create an ensemble setup [27]. Furthermore, the learning rate used during the training stage is aggressively altered using the Cosine annealing technique defining the initial learning rate and the number of training epochs to avoid similarity among models.…”

Section: ) the Proposed Snapshot Ensemble Deep Neural Network (Sednn)...mentioning

confidence: 99%

Multi-layer defense algorithm against deep reinforcement learning-based intruders in smart grids

Rouzbahani

Karimipour

Lei

2023

International Journal of Electrical Power & Energy Systems

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“…DL is a sub-field of machine learning (ML) that has increased great detection in numerous regions owing to its development in accuracy in difficult tasks and the current growth in software and hardware [8]. DL models enhance cybersecurity methods that prevent attacks by recognizing patterns that are dissimilar from usual behavior [9]. Cyberattacks share a general feature with image detection because more than 99 percent of the novel attacks are little mutants of present ones; in a similar technique modification in pictures can be recognized by little variations in their pixels.…”

Section: Introductionmentioning

confidence: 99%

Empowering Cybersecurity Using Enhanced Rat Swarm Optimization With Deep Stack-Based Ensemble Learning Approach

Manickam,

Girija,

Kumar Dutta

et al. 2024

IEEE Access

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Cybersecurity is a vital technology and measures intended to protect networks, computers, information, and programs from threats and illegal access, modification, or damage. A security model covers a network and a computer safety method. Each system has antivirus software, firewalls, and an intrusion detection system (IDS). IDS helps in discovering and identifying illegal system behavior such as usage, copying, alteration, and damage. By estimating network traffic anomalies and patterns, deep learning (DL) models can enhance the detection abilities of IDS when compared to traditional rule-based methods. These models learn complex representations from data, authorizing them to recognize subtle and developing attack patterns. Techniques like recurrent neural network (RNN) and convolutional neural network (CNN) can be applied to progress consecutive or spatial features in network data, correspondingly. This manuscript empowers Cybersecurity by utilizing an Enhanced Rat Swarm Optimizer with a Deep Stack-Based Ensemble Learning (ERSO-DSEL) model. The ERSO-DSEL approach leverages feature selection (FS) with EL strategies to boost cybersecurity. In the ERSO-DSEL system, Z-score normalization is employed to measure the input data. Besides, an improved equilibrium optimizer (IEO) based FS approach is applied to choose a set of features. For cyberattack recognition, the ERSO-DSBEL approach uses the DSEL approach comprising three models namely deep neural network (DNN), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Furthermore, the hyperparameter selection of these DL models takes place using the ERSO system. The solution result of the ERSO-DSBEL model is executed on a benchmark IDS database. A widecontrast study reported that the ERSO-DSBEL model accomplishes an enhanced accuracy outcome of 99.67% over other models of cybersecurity.

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A Snapshot Ensemble Deep Neural Network Model for Attack Detection in Industrial Internet of Things

Cited by 9 publications

References 42 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

Multi-layer defense algorithm against deep reinforcement learning-based intruders in smart grids

Empowering Cybersecurity Using Enhanced Rat Swarm Optimization With Deep Stack-Based Ensemble Learning Approach

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