WOGRU-IDS — An intelligent intrusion detection system for IoT assisted Wireless Sensor Networks

Ramana, Kadiyala; Revathi, A.; Gayathri, A.; Jhaveri, Rutvij H.; Narayana, C.V. Lakshmi; Kumar, Bhupender

doi:10.1016/j.comcom.2022.10.001

Cited by 34 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ramana et al [11] proposed a Whale Optimized GRU-IDS (WOGRU-IDS) for WSN-IoT network. In the presented method, the WOA is used to fine-tune the hyperparameter of the deep LSTM.…”

Section: Related Workmentioning

confidence: 99%

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Alruwaili,

Asiri,

Alrayes

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The Internet of Things (IoT) based Wireless Sensor Networks (WSNs) contain interconnected autonomous sensor nodes (SN), which wirelessly communicate with each other and the wider internet structure. Intrusion detection to secure IoT-based WSNs is critical for identifying and responding to great security attacks and threats that can cooperate with the integrity, availability, and privacy of the network and its data. Machine learning (ML) algorithms are deployed for detecting difficult patterns and subtle anomalies in IoT data. Artificial intelligence (AI) driven methods are learned and adapted from novel data for improving detection accuracy over time. In this article, we introduce a Red Kite Optimization Algorithm with an Average Ensemble Model for Intrusion Detection (RKOA-AEID) technique for Secure IoT-based WSN. The purpose of the RKOA-AEID methodology is to accomplish security solutions for IoT-assisted WSNs. To accomplish this, the RKOA-AEID technique performs pre-processing to scale the input data using min-max normalization. In addition, the RKOA-AEID technique performs an RKOA-based feature selection approach to elect an optimum set of features. For intrusion detection, an average ensemble learning model is used. Finally, the Lévy-fight chaotic whale optimization Algorithm (LCWOA) can be executed for the optimum hyperparameter chosen for the ensemble models. The performance evaluation of the RKOA-AEID algorithm can be tested on the benchmark WSN-DS dataset. The extensive experimental outcomes stated the higher outcome of the RKOA-AEID algorithm with other approaches with an improved accuracy of 98.94%.

show abstract

“…Ramana et al [11] proposed a Whale Optimized GRU-IDS (WOGRU-IDS) for WSN-IoT network. In the presented method, the WOA is used to fine-tune the hyperparameter of the deep LSTM.…”

Section: Related Workmentioning

confidence: 99%

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Alruwaili,

Asiri,

Alrayes

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The IoT denotes to a network of interconnected devices communicating over the internet 1 . Within this IoT framework, Wireless Sensor Networks (WSNs) assume a vital role by continuously generating data that significantly influences the longevity and performance of the overall network.…”

Section: Introductionmentioning

confidence: 99%

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

Karthikeyan,

Manimegalai,

RajaGopal

2024

Sci Rep

View full text Add to dashboard Cite

A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

show abstract

“…Conversely, the Internet of Things (IoT) [4,5] embodies an intricate network of interconnected smart devices tasked with the collection, processing, optimization, and dissemination of valuable data through internet channels. Each device, identifiable by a unique IP address or identifier, facilitates autonomous data exchange, enhancing the convenience and efficiency of daily activities through technological advancements [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security

Krishnasamy,

Alotaibi,

Alehaideb

et al. 2023

Sensors

View full text Add to dashboard Cite

In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (F3S) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks.

show abstract

WOGRU-IDS — An intelligent intrusion detection system for IoT assisted Wireless Sensor Networks

Cited by 34 publications

References 25 publications

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security

Contact Info

Product

Resources

About