Dynamic Arithmetic Optimization Algorithm with Deep Learning-based Intrusion Detection System in Wireless Sensor Networks DOI Open Access

K. Nirmal,

S. Murugan

Engineering Technology & Applied Science Research, Journal Year: 2024, Volume and Issue: 14(6), P. 18453 - 18458

Published: Dec. 2, 2024

A Wireless Sensor Network (WSN) encompasses interconnected Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers protocols measures implemented for overall functionality network, along with protecting availability, confidentiality, integrity data against tampering, unauthorized access, other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) Feature Selection (FS) leverages advanced methods enhance effectiveness detection malicious activities a network by enhancing relevant features leveraging power Neural Networks (DNNs). This study presents Dynamic Arithmetic Optimization Algorithm within DL-based IDS (DAOADL-IDS) WSNs. The purpose DAOADL-IDS is recognize classify intrusions WSN using metaheuristic algorithm DL models. To accomplish this, technique utilizes Z-score normalization approach resize input dataset compatible format. In addition, employs DAOA-based FS (DAOA-FS) model select an optimum set features. Stacked Belief (SDBN) employed (ID) process. hyperparameter selection SDBN accomplished Bird Swarm (BSA). wide experimental analysis proposed method was performed on benchmark dataset. performance validation showed accuracy 99.68%, demonstrating superior over existing techniques under various measures.

Language: Английский

Dynamic Trust-based Access Control with Hybrid Encryption for Secure IoT Applications DOI Creative Commons

A Velliangiri,

Madhavi Damle,

Peter K. Abraham

et al.

Measurement Science Review, Journal Year: 2025, Volume and Issue: 25(2), P. 48 - 52

Published: April 1, 2025

Abstract The rapid growth of internet things (IoT) applications, especially in wireless sensor networks (WSNs), has led to the generation large amounts real-time data from interconnected devices. This leads challenges securing access and managing resources efficiently. To address these challenges, we propose a dynamic trust-based control (DTAC) model for IoT WSN applications. DTAC integrates behavioral trust evaluation context-aware decision making dynamically adapt permissions network conditions real-time. scores are calculated using fuzzy logic machine learning techniques, which enable adaptive decision-making. increase security, uses hybrid encryption scheme that combines elliptic curve cryptography (ECC) with lightweight symmetric encryption, ensuring confidentiality minimal computational overhead. In addition, decisions refined by contextual factors such as user roles, device locations, sensitivity. includes collaborative re-evaluation mechanism periodically updates isolates malicious nodes without compromising stability. is evaluated on key metrics security resilience, energy efficiency, latency demonstrates better performance than existing solutions. provides scalable, energy-efficient, secure framework applications ensures reliable privacy diverse environments.

Language: Английский

Citations

0
An adaptive hybrid framework for IIoT intrusion detection using neural networks and feature optimization using genetic algorithms DOI Creative Commons
Mohammad Zubair Khan, Aijaz Ahmad Reshi,

Shabana Shafi

et al.

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: May 8, 2025

Language: Английский

Citations

0
Enhancing Trust Management Using Locally Weighted Salp Swarm Algorithm with Deep learning for SIoT Networks DOI Creative Commons
Murugesan Gurusamy,

Maheswara Venkatesh Panchavarnam,

T. Jayasankar

et al.

Brazilian Archives of Biology and Technology, Journal Year: 2024, Volume and Issue: 67

Published: Jan. 1, 2024

Language: Английский

Citations

1
Blockchain Enabled Secure Medical Data Transmission and Diagnosis Using Golden Jackal Optimization Algorithm with Deep Learning DOI Creative Commons

Kiruthikadevi Kulandaivelu,

Sivaraj Rajappan,

Vijayakumar Murugasamy

et al.

Brazilian Archives of Biology and Technology, Journal Year: 2024, Volume and Issue: 67

Published: Jan. 1, 2024

Language: Английский

Citations

1
Modeling of Tuna Swarm Algorithm Based Unequal Clustering Approach on Internet of Things Assisted Networks DOI Creative Commons

B. Srinivasan,

Vinoth Kumar Kalimuthu,

Thiruppathi Muthu

et al.

Brazilian Archives of Biology and Technology, Journal Year: 2024, Volume and Issue: 67

Published: Jan. 1, 2024

Language: Английский

Citations

0
Enhancing Intrusion Detection Using Binary Arithmetic Optimization with Sparse Auto Encoder for Fog-Assisted Wireless Sensor Networks DOI Creative Commons

Thiruppathi Muthu,

Vinoth Kumar Kalimuthu,

B. Srinivasan

et al.

Brazilian Archives of Biology and Technology, Journal Year: 2024, Volume and Issue: 67

Published: Jan. 1, 2024

Language: Английский

Citations

0
Dynamic Arithmetic Optimization Algorithm with Deep Learning-based Intrusion Detection System in Wireless Sensor Networks DOI Open Access

K. Nirmal,

S. Murugan

Engineering Technology & Applied Science Research, Journal Year: 2024, Volume and Issue: 14(6), P. 18453 - 18458

Published: Dec. 2, 2024

A Wireless Sensor Network (WSN) encompasses interconnected Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers protocols measures implemented for overall functionality network, along with protecting availability, confidentiality, integrity data against tampering, unauthorized access, other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) Feature Selection (FS) leverages advanced methods enhance effectiveness detection malicious activities a network by enhancing relevant features leveraging power Neural Networks (DNNs). This study presents Dynamic Arithmetic Optimization Algorithm within DL-based IDS (DAOADL-IDS) WSNs. The purpose DAOADL-IDS is recognize classify intrusions WSN using metaheuristic algorithm DL models. To accomplish this, technique utilizes Z-score normalization approach resize input dataset compatible format. In addition, employs DAOA-based FS (DAOA-FS) model select an optimum set features. Stacked Belief (SDBN) employed (ID) process. hyperparameter selection SDBN accomplished Bird Swarm (BSA). wide experimental analysis proposed method was performed on benchmark dataset. performance validation showed accuracy 99.68%, demonstrating superior over existing techniques under various measures.

Language: Английский

Citations

0