A Deep-Learning-Integrated Blockchain Framework for Securing Industrial IoT DOI
Ahamed Aljuhani, Prabhat Kumar, Rehab Alanazi

et al.

IEEE Internet of Things Journal, Journal Year: 2023, Volume and Issue: 11(5), P. 7817 - 7827

Published: Sept. 18, 2023

The Industrial Internet of Things (IIoT) is a collection interconnected smart sensors and actuators with industrial software tools applications. IIoT aims to enhance manufacturing processes by capturing analyzing real-time data. However, the heterogeneous homogeneous nature networks makes them vulnerable several security threats. As data transmitted over an insecure communication medium, intruders may intercept among different entities perform malicious activities. Consequently, ensuring privacy in essential. Motivated aforementioned challenges, this article presents deep-learning-integrated blockchain framework for securing networks. Specifically, first, we design private blockchain-based secure using session-based mutual authentication key agreement mechanism. In approach, Proof-of-Authority (PoA) consensus mechanism used verification transactions block creation based on voting miners cloud server. Second, novel deep-learning-based intrusion detection system that combines contractive sparse autoencoder (CSAE), attention-based bidirectional long short-term memory (ABiLSTM) networks, softmax classifier cyberattack detection. practical implementation deep-learning techniques proves effectiveness proposed framework.

Language: Английский

SMWE-GFPNNet: A high-precision and robust method for forest fire smoke detection DOI
Rui Li, Yaowen Hu, Lin Li

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 289, P. 111528 - 111528

Published: Feb. 15, 2024

Language: Английский

Citations

15
Network intrusion detection: An optimized deep learning approach using big data analytics DOI

D. Suja Mary,

L. Jaya Singh Dhas,

A. Deepa

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 251, P. 123919 - 123919

Published: April 5, 2024

Language: Английский

Citations

9
Temporal Convolutional Network Approach to Secure Open Charge Point Protocol (OCPP) in Electric Vehicle Charging DOI Creative Commons

Ikram Benfarhat,

Vik Tor Goh, Siow Chun Lim

et al.

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 15272 - 15289

Published: Jan. 1, 2025

Language: Английский

Citations

1
MAG-FSNet:A high-precision robust forest fire smoke detection model integrating local features and global information DOI
Chunman Yan, Jun Wang

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116813 - 116813

Published: Jan. 1, 2025

Language: Английский

Citations

1
A Stacking Ensemble Model with Enhanced Feature Selection for Distributed Denial-of-Service Detection in Software-Defined Networks DOI Open Access
Tariq Emad Ali, Yung-Wey Chong,

Selvakumar Manickam

et al.

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(1), P. 19232 - 19245

Published: Feb. 2, 2025

The proliferation of Distributed Denial Service (DDoS) attacks poses a significant threat to network accessibility and performance. Traditional feature selection methods struggle with the complexity traffic data, leading poor detection To address this issue, Genetic Algorithm Wrapper Feature Selection (GAWFS) is proposed, integrating Chi-squared (GA) approaches correlation method select most correlated features. GAWFS effectively reduces dimensions, eliminates redundancy, identifies crucial features for classification. Detection accuracy further improved by employing stacking ensemble model, combining Multi-Layer Perceptron (MLP) Support Vector Machine (SVM) as base models, Random Forest (RF) metamodel. proposed classifier achieves impressive accuracies 99.86% training data 98.89% test representing improvements approximately 5% 40%, respectively, over previous studies. time was also reduced 2,593 s, substantial improvement 29.92%. Validation on various benchmark datasets confirmed efficacy approach, underscoring importance enhanced model against DDoS attacks.

Language: Английский

Citations

1
Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks DOI Creative Commons

Bright Agbor Agbor,

Bliss Utibe-Abasi Stephen, Philip Asuquo

et al.

Computers, Journal Year: 2025, Volume and Issue: 14(2), P. 58 - 58

Published: Feb. 10, 2025

The Internet of Things (IoT) ecosystem is rapidly expanding. It driven by continuous innovation but accompanied increasingly sophisticated cybersecurity threats. Protecting IoT devices from these emerging vulnerabilities has become a critical priority. This study addresses the limitations existing threat detection methods, which often struggle with dynamic nature environments and growing complexity cyberattacks. To overcome challenges, novel hybrid architecture combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), Deep (DNN) proposed for accurate efficient detection. model’s performance evaluated using IoT-23 Edge-IIoTset datasets, encompass over ten distinct attack types. framework achieves remarkable 99% accuracy on both outperforming state-of-the-art solutions. Advanced optimization techniques, including model pruning quantization, are applied to enhance deployment efficiency in resource-constrained environments. results highlight robustness its adaptability diverse scenarios, address key prior approaches. research provides robust solution detection, establishing foundation advancing security addressing evolving landscape cyber threats while driving future innovations field.

Language: Английский

Citations

1
Online ensemble learning-based anomaly detection for IoT systems DOI
Y.-T. Wu, Lan Liu, Yong‐Jie Yu

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112931 - 112931

Published: March 1, 2025

Language: Английский

Citations

1
CRSF: An Intrusion Detection Framework for Industrial Internet of Things Based on Pretrained CNN2D-RNN and SVM DOI Creative Commons

Shiming Li,

Guangzhao Chai, Yuhe Wang

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 92041 - 92054

Published: Jan. 1, 2023

The traditional support vector machine (SVM) requires manual feature extraction to improve classification performance and relies on the expressive power of manually extracted features. However, this characteristic poses limitations in complex Industrial Internet Things (IIoT) environments. Traditional may fail capture all relevant information, thereby restricting application effectiveness SVM IIoT settings. CNN-RNN, as a deep learning network capable simultaneously extracting spatial temporal features, can alleviate researchers' burden. In paper, we propose novel intrusion detection system (IDS) framework based anomalies, called CRSF. framework's pre-training part employs dimension transformation function process input data into two-dimensional images. Two-dimensional convolutional kernels are then employed extract sequences passed an RNN richer After sufficient pre-training, is used classifier map from space high-dimensional learn nonlinear decision boundaries, enabling accurately differentiate representations different classes. Simulation experiments TON_IoT-Datasets demonstrate CRSF detection. When using "linear" kernel SVM, achieves accuracy, F1-score, AUC 0.9959, 0.9977, respectively, indicating its capability superiority

Language: Английский

Citations

20
Security Information Event Management data acquisition and analysis methods with machine learning principles DOI Creative Commons

Noyan Tendikov,

Leila Rzayeva, Bilal Saoud

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102254 - 102254

Published: May 14, 2024

In the face of increasing global disruptions, cybersecurity field is confronting rising threats posed by offensive groups and individual hackers. Traditional security measures often fall short in detecting mitigating these sophisticated attacks, necessitating advanced intrusion detection methods. The goal our study to develop robust network methods using machine learning techniques. addition, we evaluate effectiveness various models intrusions. Model performances are optimized through hyperparameter tuning feature selection. A range classification clustering have been employed. Data from SIEM systems capturing real-time statistics cloud-hosted Windows virtual machines has gathered augmented with web attack logs CICIDS2017, each comprising approximately fifteen thousand rows. Hyperparameter tuning, data normalization, standardization selection techniques for model optimization used study. research showcases potential enhancing capabilities. findings underscore Random Forest Classifier (0.97) highlight importance utilizing diverse datasets This offers valuable insights sets a foundation future advancements strategies systems.

Language: Английский

Citations

8
Detecting lateral movement: A systematic survey DOI Creative Commons
Christos Smiliotopoulos, Georgios Kambourakis, Constantinos Kolias

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e26317 - e26317

Published: Feb. 1, 2024

Within both the cyber kill chain and MITRE ATT&CK frameworks, Lateral Movement (LM) is defined as any activity that allows adversaries to progressively move deeper into a system in seek of high-value assets. Although this timely subject has been studied cybersecurity literature significant degree, so far, no work provides comprehensive survey regarding identification LM from mainly an Intrusion Detection System (IDS) viewpoint. To cover noticeable gap, systematic, holistic overview topic, not neglecting new communication paradigms, such Internet Things (IoT). The part, spanning time window eight years 53 articles, split three focus areas, namely, Endpoint Response (EDR) schemes, machine learning oriented solutions, graph-based strategies. On top that, we bring light interrelations, mapping progress field over time, offer key observations may propel research forward.

Language: Английский

Citations

7