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Delwar TS, Aras U, Mukhopadhyay S, Kumar A, Kshirsagar U, Lee Y, Singh M, Ryu JY. The Intersection of Machine Learning and Wireless Sensor Network Security for Cyber-Attack Detection: A Detailed Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:6377. [PMID: 39409417 PMCID: PMC11479060 DOI: 10.3390/s24196377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 09/22/2024] [Accepted: 09/26/2024] [Indexed: 10/20/2024]
Abstract
This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML's potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment.
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Affiliation(s)
- Tahesin Samira Delwar
- Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea; (T.S.D.); (U.A.)
| | - Unal Aras
- Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea; (T.S.D.); (U.A.)
| | - Sayak Mukhopadhyay
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India; (S.M.); (A.K.); (U.K.)
| | - Akshay Kumar
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India; (S.M.); (A.K.); (U.K.)
| | - Ujwala Kshirsagar
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India; (S.M.); (A.K.); (U.K.)
| | - Yangwon Lee
- Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea;
| | - Mangal Singh
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India; (S.M.); (A.K.); (U.K.)
| | - Jee-Youl Ryu
- Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea; (T.S.D.); (U.A.)
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Islam U, Alsadhan AA, Alwageed HS, Al-Atawi AA, Mehmood G, Ayadi M, Alsenan S. SentinelFusion based machine learning comprehensive approach for enhanced computer forensics. PeerJ Comput Sci 2024; 10:e2183. [PMID: 39145216 PMCID: PMC11323197 DOI: 10.7717/peerj-cs.2183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/18/2024] [Indexed: 08/16/2024]
Abstract
In the rapidly evolving landscape of modern technology, the convergence of blockchain innovation and machine learning advancements presents unparalleled opportunities to enhance computer forensics. This study introduces SentinelFusion, an ensemble-based machine learning framework designed to bolster secrecy, privacy, and data integrity within blockchain systems. By integrating cutting-edge blockchain security properties with the predictive capabilities of machine learning, SentinelFusion aims to improve the detection and prevention of security breaches and data tampering. Utilizing a comprehensive blockchain-based dataset of various criminal activities, the framework leverages multiple machine learning models, including support vector machines, K-nearest neighbors, naive Bayes, logistic regression, and decision trees, alongside the novel SentinelFusion ensemble model. Extensive evaluation metrics such as accuracy, precision, recall, and F1 score are used to assess model performance. The results demonstrate that SentinelFusion outperforms individual models, achieving an accuracy, precision, recall, and F1 score of 0.99. This study's findings underscore the potential of combining blockchain technology and machine learning to advance computer forensics, providing valuable insights for practitioners and researchers in the field.
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Affiliation(s)
- Umar Islam
- Computer Science, IQRA National University, Peshawar, Swat Campus, Pakistan
| | - Abeer Abdullah Alsadhan
- Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | | | - Abdullah A. Al-Atawi
- Department of Computer Science, Applied College, University of Tabuk, Tabuk, Saudi Arabia
| | - Gulzar Mehmood
- Computer Science, IQRA National University, Peshawar, Swat Campus, Pakistan
| | - Manel Ayadi
- Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Shrooq Alsenan
- Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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Reddy CKK, Kaza VS, Anisha PR, Khubrani MM, Shuaib M, Alam S, Ahmad S. Optimising barrier placement for intrusion detection and prevention in WSNs. PLoS One 2024; 19:e0299334. [PMID: 38422084 PMCID: PMC10903853 DOI: 10.1371/journal.pone.0299334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 02/07/2024] [Indexed: 03/02/2024] Open
Abstract
This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method's effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.
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Affiliation(s)
- C. Kishor Kumar Reddy
- Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Abids, Hyderabad, Telangana, India
| | - Vijaya Sindhoori Kaza
- Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Abids, Hyderabad, Telangana, India
| | - P. R. Anisha
- Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Abids, Hyderabad, Telangana, India
| | | | - Mohammed Shuaib
- College of Computer Science & IT, Jazan University, Jazan, Saudi Arabia
| | - Shadab Alam
- College of Computer Science & IT, Jazan University, Jazan, Saudi Arabia
| | - Sadaf Ahmad
- Department of Computer Science, Aligarh Muslim University, Aligarh, India
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Fay CD, Corcoran B, Diamond D. Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks. SENSORS (BASEL, SWITZERLAND) 2023; 24:162. [PMID: 38203023 PMCID: PMC10781252 DOI: 10.3390/s24010162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/23/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024]
Abstract
This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8-99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.
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Affiliation(s)
- Cormac D. Fay
- SMART Infrastructure Facility, Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Brian Corcoran
- School of Mechanical and Manufacturing Engineering, Faculty of Engineering and Computing, Dublin City University, Glasnevin, D09 V209 Dublin, Ireland;
| | - Dermot Diamond
- Insight Centre for Data Analytics, Dublin City University, Glasnevin, D09 V209 Dublin, Ireland;
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Jabeen T, Jabeen I, Ashraf H, Jhanjhi NZ, Yassine A, Hossain MS. An Intelligent Healthcare System Using IoT in Wireless Sensor Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115055. [PMID: 37299782 DOI: 10.3390/s23115055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
The Internet of Things (IoT) uses wireless networks without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental factors. There are a variety of WSN uses, and some well-known application factors include energy consumption and lifespan duration for routing purposes. The sensors have detecting, processing, and communication capabilities. In this paper, an intelligent healthcare system is proposed which consists of nano sensors that collect real-time health status and transfer it to the doctor's server. Time consumption and various attacks are major concerns, and some existing techniques contain stumbling blocks. Therefore, in this research, a genetic-based encryption method is advocated to protect data transmitted over a wireless channel using sensors to avoid an uncomfortable data transmission environment. An authentication procedure is also proposed for legitimate users to access the data channel. Results show that the proposed algorithm is lightweight and energy efficient, and time consumption is 90% lower with a higher security ratio.
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Affiliation(s)
- Tallat Jabeen
- School of Interdisciplinary Engineering & Sciences (SINES) NUST, Islamabad 44000, Pakistan
| | - Ishrat Jabeen
- School of Interdisciplinary Engineering & Sciences (SINES) NUST, Islamabad 44000, Pakistan
| | - Humaira Ashraf
- Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan
| | - N Z Jhanjhi
- School of Computer Science, SCS Taylor's University, Subang Jaya 47500, Malaysia
| | - Abdulsalam Yassine
- Department of Software Engineering, Faculty of Engineering, Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada
| | - M Shamim Hossain
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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A Hybrid Cross Layer with Harris-Hawk-Optimization-Based Efficient Routing for Wireless Sensor Networks. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
Abstract
Efficient clustering and routing is a main challenge in a wireless sensor network (WSN). To achieve better quality-of-service (QoS) performance, this work introduces k-medoids with improved artificial-bee-colony (K-IABC)-based energy-efficient clustering and the cross-layer-based Harris-hawks-optimization-algorithm (CL-HHO) routing protocol for WSN. To overcome the power-asymmetry problem in wireless sensor networks, a cross-layer-based optimal-routing solution is proposed. The goal of cross-layer routing algorithms is to decrease network-transmission delay and power consumption. This algorithm which was used to evaluate and select the effective path route and data transfer was implemented using MATLAB, and the results were compared to some existing techniques. The proposed CL-HHO performs well in packet-loss ratio (PLR), throughput, end-to-end delay (E2E), jitter, network lifetime (NLT) and buffer occupancy. These results are then validated by comparing them to traditional routing strategies such as hierarchical energy-efficient data gathering (HEED), energy-efficient-clustering routing protocol (EECRP), Grey wolf optimization (GWO), and cross-layer-based Ant-Lion optimization (CL-ALO). Compared to the HEED, EECRP, GWO, and CL-ALO algorithms, the proposed CL-HHO outperforms them.
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Rehman A, Haseeb K, Jeon G, Bahaj SA. Secure Edge-Based Energy Management Protocol in Smart Grid Environments with Correlation Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:9236. [PMID: 36501937 PMCID: PMC9737205 DOI: 10.3390/s22239236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
For the monitoring and processing of network data, wireless systems are widely used in many industrial applications. With the assistance of wireless sensor networks (WSNs) and the Internet of Things (IoT), smart grids are being explored in many distributed communication systems. They collect data from the surrounding environment and transmit it with the support of a multi-hop system. However, there is still a significant research gap in energy management for IoT devices and smart sensors. Many solutions have been proposed by researchers to cope with efficient routing schemes in smart grid applications. But, reducing energy holes and offering intelligent decisions for forwarding data are remain major problems. Moreover, the management of network traffic on grid nodes while balancing the communication overhead on the routing paths is an also demanding challenge. In this research work, we propose a secure edge-based energy management protocol for a smart grid environment with the support of multi-route management. It strengthens the ability to predict the data forwarding process and improves the management of IoT devices by utilizing a technique of correlation analysis. Moreover, the proposed protocol increases the system's reliability and achieves security goals by employing lightweight authentication with sink coordination. To demonstrate the superiority of our proposed protocol over the chosen existing work, extensive experiments were performed on various network parameters.
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Affiliation(s)
- Amjad Rehman
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Khalid Haseeb
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia
- Department of Computer Science, Islamia College Peshawar, Peshawar 25120, Pakistan
| | - Gwanggil Jeon
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia
- Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Republic of Korea
| | - Saeed Ali Bahaj
- MIS Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
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