Abstract
Wireless Sensor Networks (WSNs) play a vital role in various applications, necessitating robust network security to protect sensitive data. Intrusion Detection Systems (IDSs) are crucial for preserving the integrity, availability, and confidentiality of WSNs by detecting and countering potential attacks. Despite significant research efforts, the existing IDS solutions still suffer from challenges related to detection accuracy and false alarms. To address these challenges, in this paper, we propose a Bayesian optimization-based Deep Learning (DL) model. However, the proposed optimized DL model, while showing promising results in enhancing security, encounters challenges such as data dependency, computational complexity, and the potential for overfitting. In the literature, researchers have employed Reinforcement Learning (RL) to address these issues. However, it also introduces its own concerns, including exploration, reward design, and prolonged training times. Consequently, to address these challenges, this paper proposes an Innovative Integrated RL-based Advanced DL Algorithm (IRADA) for attack detection in WSNs. IRADA leverages the convergence of DL and RL models to achieve superior intrusion detection performance. The performance analysis of IRADA reveals impressive results, including accuracy (99.50%), specificity (99.94%), sensitivity (99.48%), F1-Score (98.26%), Kappa statistics (99.42%), and area under the curve (99.38%). Additionally, we analyze IRADA’s robustness against adversarial attacks, ensuring its applicability in real-world security scenarios.
Similar content being viewed by others
Data availability
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
References
Vembu G, Ramasamy D (2022) Optimized deep learning-based intrusion detection for wireless sensor networks. Int J Commun Syst . Accessed 8 June 2022
Raveendranadh B, Tamilselvan S (2023) An accurate attack detection framework based on exponential polynomial kernel-centered deep neural networks in the wireless sensor network. Trans Emerg Telecommun Technol vol. 34
Maheswari M, Karthika RA (2022) A novel hybrid deep learning framework for intrusion detection systems in wsn-iot networks. Intell Autom Soft Comput 33(1):365–382
Selvi ST, and Visalakshi P (2022) Detection of unique delete attack in wireless sensor network using gradient thresholding-long short-term memory algorithm. Concurr Comput-Pract & Experience vol. 34. Accessed 10 Dec 2022
Pawar MV, AJ (2023) Detection and prevention of black-hole and wormhole attacks in wireless sensor network using optimized lstm. Int J Pervasive Comput Commun 19:124–153. Accessed 6 Jan 2023
Subasini CA, Karuppiah SP, Sheeba A, Padmakala S (2021) Developing an attack detection framework for wireless sensor network-based healthcare applications using hybrid convolutional neural network. Trans Emerg Telecommun Technol 32
Naser SM, Ali YH, Obe DA-J (2022) Hybrid cyber-security model for attacks detection based on deep and machine learning. Int J Online Biomedical Eng 18(11):17–30
Premkumar M, Sundararajan TVP (2020) Dldm: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks. Microprocess Microsyst vol 79
Rameshkumar S, Ganesan R, Merline A (2023) Progressive transfer learning-based deep q network for ddos defence in wsn. Comput Syst Scie Eng 44(3):2379–2394
Salmi S, Oughdir L, (2023) Performance evaluation of deep learning techniques for dos attacks detection in wireless sensor network. J Big Data vol 10. Accessed 7 Feb 2023
Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) Deep learning approach for intelligent intrusion detection system. IEEE ACCESS 7:41525–41550
Manjula P, Priya SB (2022) An effective network intrusion detection and classification system for securing wsn using vgg-19 and hybrid deep neural network techniques. J Int & Fuzzy Syst 43(5):6419–6432
Gao B, Maekawa T, Amagata D, Hara T (2020) Detecting reinforcement learning-based grey hole attack in mobilewireless sensor networks. IEICE Trans Commun vol E103B, pp 504–516
Juneja V, Dinkar SK, Gupta DV (2022) An anomalous co-operative trust & pg-drl based vampire attack detection & routing. Concurr Comput-Pract & Experience vol 34. Accessed 1 Feb 2022
Rahman UA, Jayakumar C (2022) Security enhanced optimal trajectory selection for mobile sink using reinforcement learning. J Intell & Fuzzy Syst 42(6):6145–6157
Qamar S (2023) Optimal sensor network routing with secure network monitoring using deep learning architectures. Neural Comput & Appl. Accessed 16 June 2023
Ramana TV, Thirunavukkarasan M, Mohammed AS, Devarajan GG, Murugan S (2022) Ambient intelligence approach: Internet of things based decision performance analysis for intrusion detection. Comput Commun 195:315–322. Accessed 1 Nov 2022
Revanesh M, Sridhar V (2021) A trusted distributed routing scheme for wireless sensor networks using blockchain and meta-heuristics-based deep learning technique. Trans Emerg Telecommun Technol vol 32
Francis EG, Sheeja S (2023) Shake-esdrl-based energy efficient intrusion detection and hashing system. Ann Telecommun. Accessed 31 May 2023
Ravi V, Chaganti R, Alazab M (2022) Recurrent deep learning-based feature fusion ensemble meta-classifier approach for intelligent network intrusion detection system. Comput & Electr Eng vol 102
Alikh N, Rajabzadeh A (2022) Using a lightweight security mechanism to detect and localize jamming attack in wireless sensor networks. OPTIK vol 271
Ahmad R, Wazirali R, Bsoul Q, Abu-Ain T, Abu-Ain W (2021) Feature-selection and mutual-clustering approaches to improve dos detection and maintain wsns’ lifetime. SENSORS vol 21
Anitha R, Bapu BRT (2022) A deep-drpxml and iag-gwo based chst fostered blockchain technology for secured dynamic optimal routing for wireless sensor networks. 43(6):7525–7543
Dener M, Okur C, Al S, Orman A (2023) Wsn-bfsf: A new dataset for attacks detection in wireless sensor networks. IEEE Internet of Things J pp 1–1
Ramana K, Revathi A, Gayathri A, Jhaveri RH, Narayana CVL, Kumar BN (2022) Wogru-ids - an intelligent intrusion detection system for iot assisted wireless sensor networks. Comput Commun 196:195–206. Accessed 1 Dec 2022
Zhiqiang L, Mohiuddin G, Jiangbin Z, Asim M, Sifei W (2022) Intrusion detection in wireless sensor network using enhanced empirical based component analysis. Future Gener Comput Syst-The Int J Escience 135:181–193
Demidov RA, Zegzhda PD, Kalinin MO (2018) Threat analysis of cyber security in wireless adhoc networks using hybrid neural network model. Autom Control Comput Scie 52:971–976
Chithaluru P, Al-Turjman F, Kumar M, Stephan T (2021) Mtcee-lln: Multilayer threshold cluster-based energy-efficient low-power and lossy networks for industrial internet of things. IEEE Internet Things J 9(7):4940–4948
Chithaluru PK, Khan MS, Kumar M, Stephan T (2021) Eth-leach: An energy enhanced threshold routing protocol for wsns. Int J Commun Syst 34(12):e4881
Jayaraman R, Rao D, Kumar M, Mishra A (2023) Understanding the salient features related to resource management in broadband wireless networks. Resour Manag Adv Wirel Netw pp 81–97
Chithaluru P, Al-Turjman F, Kumar M, Stephan T (2023) Energy-balanced neuro-fuzzy dynamic clustering scheme for green & sustainable iot based smart cities. Sustain Cities Soc 90:10436
Author information
Authors and Affiliations
Contributions
Vandana Shakya: Conceptualization, Methodology, Software. Data curation, Writing- Original draft preparation. Jaytrilok Choudhary and Dhirendra Pratap Singh:: Visualization, Investigation, Supervision, Writing- Reviewing and Editing.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare no conflict of interest regarding the publication of this paper.
Animal research
This study has not involved any kind of experiments on animals, therefore, no ethical approval is required.
Human research
This study has not involved any kind of experiments on human beings, therefore, no ethical approval is required.
Consent to participate and consent to publish statements
No individuals were involved in this study, therefore, no informed participate and consent was required.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shakya, V., Choudhary, J. & Singh, D.P. IRADA: integrated reinforcement learning and deep learning algorithm for attack detection in wireless sensor networks. Multimed Tools Appl 83, 71559–71578 (2024). https://doi.org/10.1007/s11042-024-18289-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-024-18289-7