A Realtime Adaptive Trust Model Based on Artificial Neural Networks for Wireless Sensor Networks

Authors

  • Khaled Mohammed Ali Hassan Al-Azhar University, Faculty of Engineering, Computers and Systems Engineering Department, Cairo, Egypt
  • Mohamed Ashraf Madkour Al-Azhar University, Faculty of Engineering, Computers and Systems Engineering Department, Cairo, Egypt
  • Sayed Abd El Hady Nouh Al-Azhar University, Faculty of Engineering, Computers and Systems Engineering Department, Cairo, Egypt

DOI:

https://doi.org/10.13052/jcsm2245-1439.1244

Keywords:

WSNs, trust and reputation management models, ATSR, ML, ANN, backpropagation algorithm, routing cost function (RCF), internal attacks

Abstract

Wireless sensor networks (WSNs) are vulnerable to security attacks due to the unbounded nature of the wireless medium, restricted node resources, and cooperative routing. Standard cryptography and authentication mechanisms help protect against external attacks, but a compromised node can easily bypass them. This work aims to protect WSNs against internal attacks, which are mostly launched from compromised nodes to disrupt the network’s operation and/or reduce its performance. The trust and reputation management framework provides a routing cost function for selecting the best secure next hop. Tuning the trust weights is essential to cope with the constant changes in the network environment, such as the sensor nodes’ behaviours and locations. To allow real-time operation, the proposed framework introduces an artificial neural network (ANN) in each sensor node that automatically adjusts the weights of the considered trust metrics according to the WSN state. A large dataset is generated to train and test the ANN using a multitude of simulated cases. A prototype is developed and tested using the J-Sim simulator to show the performance gain resulting from applying the adaptive trust model. The experimental results showed that the adaptive model has robust performance and has achieved an improved packet delivery ratio with reduced power consumption and reduced average packet loss. The results showed that when sensor nodes were static and malicious nodes were present, the average accuracy was 99.6%, while when they were in motion, it was 88.1%.

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Author Biographies

Khaled Mohammed Ali Hassan, Al-Azhar University, Faculty of Engineering, Computers and Systems Engineering Department, Cairo, Egypt

Khaled Mohammed Ali Hassan has received a bachelor’s degree in Computer Engineering from the Electronics and Electricity Faculty of Engineering at Aleppo University in Syria. He received his master’s degree in Computer Engineering from the Faculty of Engineering at Cairo University in 2015. He is currently working as a researcher on his Ph.D. in the Systems and Computers Engineering Department of the Faculty of Engineering at Al-Azhar University in Cairo, Egypt.

Mohamed Ashraf Madkour, Al-Azhar University, Faculty of Engineering, Computers and Systems Engineering Department, Cairo, Egypt

Mohamed Ashraf Madkour has got his B. Sc. and M. Sc., degrees in Electrical Engineering in 1968 and 1974, respectively, and got his Ph. D. degree in computer networking from the Electrical Engineering Department, Ain Shams University in 1981. Dr. Madkour is a professor emeritus in the Systems and Computers Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt, where he teaches courses and does research on computer networking, internetworking, and systems and data security. Dr. Madkour has published more than 40 research papers in international and local journals and conferences, and his research interests include internetworking and wireless networks, cyber security, artificial intelligence, and data science.

Sayed Abd El Hady Nouh, Al-Azhar University, Faculty of Engineering, Computers and Systems Engineering Department, Cairo, Egypt

Sayed Abd El Hady Nouh is a computer network professor in the Department of Computers and Systems Engineering at Al-Azhar University in Cairo, Egypt. He received his B.Sc. degree in Communications Engineering and his M.Sc. degree in Computer Engineering from Al-Azhar University in 1978 and 1982, respectively. He received his Ph.D. degree in Computer Engineering from AGH University, Cracov, Poland, in 1992. From 2006–2010, he served as the Egyptian Consultant at the African Union, in Addis Ababa, Ethiopia. From 2012 to 2015, he served as the chairman of the Computers and Systems Engineering Department at Al-Azhar University. He is the chairman of the committee for upgrading professors and associate professors. He has been an IEEE member since 1991. He has been involved with research in performance analysis and evaluation of computer networks, Ad-hoc routing protocols, routing and security protocols for wireless sensor networks, mobile computing and wireless networking, modeling and computer simulation techniques, and data communications networks.

References

C. D. McDermott and A. Petrovski, ‘Investigation of computational intelligence techniques for intrusion detection in wireless sensor networks’, international journal of computer networks and communications, vol. 9, no. 4, 2017.

Z. Ye, T. Wen, Z. Liu, X. Song, and C. Fu, ‘An efficient dynamic trust evaluation model for wireless sensor networks’, In Sensors, vol. 2017, 2017.

H. Sharma, A. Haque, and F. Blaabjerg, ‘Machine learning in wireless sensor networks for smart cities: a survey’, Electronics (Basel), vol. 10, no. 9, p. 1012, 2021.

W. Fang, W. Zhang, W. Chen, T. Pan, Y. Ni, and Y. Yang, ‘Trust-based attack and defense in wireless sensor networks: a survey’, Wireless Communications and Mobile Computing, vol. 2020, 2020.

C. R. Morales, F. Rangel de Sousa, V. Brusamarello, and N. C. Fernandes, ‘Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN’, Sensors, vol. 21, no. 21, p. 7375, 2021.

Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, ‘ Application of machine learning in wireless networks: Key techniques and open issues’, Proc. In IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp., 2019.

YG. Bhatti, ‘ Machine learning based localization in large-scale wireless sensor networks’, Sensors, vol. 18, no. 12, p. 4179, 2018.

F. Sanhaji, H. Satori, and K. Satori, ‘Clustering Based on Neural Networks in Wireless Sensor Networks’, in: Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems, pp. 1–6, 2017.

P. Hankare, S. Babar, P. Mahalle, ‘Trust management approach for detection of malicious devices in siot’, ISSN 1846-6168 (Print), ISSN 1848-5588 (Online) Original scientific paper https://doi.org/10.31803/tg-20210204180217, vol. 15, no. 1, pp. 43–50, 2021.

Vasily A. Desnitsky, Igor V. Kotenko, Igor B. Parashchuk,’Neural Network Based Classification of Attacks on Wireless Sensor Networks’, IEEE, 2020.

A. Beheshtiasl and A. Ghaffari, ‘Secure and trust-aware routing scheme in wireless sensor networks’, in Wireless Personal Communications, vol. 107, no. 4, pp. 1799–1814, 2019.

J. Wang, X. Jing, Z. Yan, Y. Fu, W. Pedrycz, and L. T. Yang, ‘A survey on trust evaluation based on machine learning’, ACM Computing Surveys (CSUR), vol. 53, no. 5, pp. 1–36, 2020.

H. Rathore, ‘Case study: A review of security challenges, attacks and trust and reputation models in wireless sensor networks’, in Mapping Biological Systems to Network Systems, pp. 117–175, 2016.

F. Ishmanov and Y. bin Zikria, ‘Trust mechanisms to secure routing in wireless sensor networks: Current state of the research and open research issues’, in Journal of Sensors, vol. 2017.

H. Deng, X. Sun, B. Wang, and Y. Cao, ‘Selective forwarding attack detection using watermark in WSNs’, ISECS International Colloquium on Computing, Communication, Control, and Management, vol. 3, pp. 109–113, 2009.

Y. Cho and G. Qu, ‘Detection and prevention of selective forwarding-based denial-of-service attacks in WSNs’, in: International Journal of Distributed Sensor Networks, vol. 9, no. 8, p. 205920, 2013.

H. Alzaid, M. Alfaraj, S. Ries, A. Jøsang, M. Albabtain, and A. Abuhaimed, ‘Reputation-based trust systems for wireless sensor networks: A comprehensive review’, in IFIP International Conference on Trust Management, pp. 66–82, 2013.

C.B. Vinutha, N. Nalini and B.S. Veeresh, ‘Energy Efficient Wireless Sensor Network Using Neural Network Based Smart Sampling and Reliable Routing Protocol’, IEEE WiSPNET conference, 2017.

A. Akbas, H. U. Yildiz, A. M. Ozbayoglu, and B. Tavli, ‘Neural network based instant parameter prediction for wireless sensor network optimization models’, in Wireless Networks, vol. 25, no. 6, pp. 3405–3418, 2019.

B. Jaint, V. Singh, L. K. Tanwar, S. Indu, and N. Pandey, ‘An efficient weighted trust method for malicious node detection in clustered wireless sensor networks’, in 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1183–1187, 2018.

Bassam Hasan, Sameer Alani, Mohammed Ayad Saad, ‘Secured node detection technique based on artificial neural network for wireless sensor network’, International Journal of Electrical and Computer Engineering (IJECE), February 2021.

F. Ishmanov, S. W. Kim, and S. Y. Nam, ‘A robust trust establishment scheme for wireless sensor networks’, in Sensors, vol. 15, no. 3, pp. 7040–7061, 2015.

F. Ghasemi, ‘Secure Geographic Routing in Wireless Sensor Networks’, Master of Science University of Gothenburg, Gothenberg, Sweden, 2013.

B. Karp and H.-T. Kung, ‘GPSR: Greedy perimeter stateless routing for wireless networks’, in Proceedings of the 6th annual international conference on Mobile computing and networking, pp. 243–254, 2000.

T. Zahariadis, H. C. Leligou, S. Voliotis, S. Maniatis, P. Trakadas, and P. Karkazis, ‘Energy-aware secure routing for large wireless sensor networks’, WSEAS Transactions on Communications, vol. 8, no. 9, pp. 981–991, 2009.

T. Zahariadis et al, ‘Design and implementation of a trust-aware routing protocol for large WSNs’, in: International Journal of Network Security & Its Applications (IJNSA), vol. 2, no. 3, pp. 52–68, 2010.

H.-C. Leligou, P. Trakadas, S. Maniatis, P. Karkazis, and T. Zahariadis, ‘Combining trust with location information for routing in wireless sensor networks’, Wireless Communications and Mobile Computing, vol. 12, no. 12, pp. 1091–1103, 2012.

T. Zahariadis, P. Trakadas, H. C. Leligou, S. Maniatis, and P. Karkazis, ‘A novel trust-aware geographical routing scheme for wireless sensor networks’, Wirel Pers Commun, vol. 69, no. 2, pp. 805–826, 2013.

Theodore Zahariadis, Helen C. Leligou, Panagiotis Trakadas, Stamatis Voliotis, ‘Mobile Networks Trust management in wireless sensor networks’, Wiley InterScience, Published online 8 April 2010.

T. Zahariadis, P. Trakadas, S. Maniatis, P. Karkazis, H. C. Leligou, and S. Voliotis, ‘Efficient detection of routing attacks in wireless sensor networks’, in 2009 16th International Conference on Systems, Signals and Image Processing, pp. 1–4, 2009.

S. Voliotis, T. Zahariadis, H. C. Leligou, D. Bargiotas, P. Trakadas, and P. Karkazis, ‘A Scalable Geographical Routing approach for Wireless Sensor Networks’, IWSSIP, 2010.

P. Trakadas, S. Maniatis, P. Karkazis, T. Zahariadis, H.-C. Leligou, and S. Voliotis, ‘A novel flexible trust management system for heterogeneous wireless sensor networks’, in 2009 International Symposium on Autonomous Decentralized Systems, pp.1–6, 2009.

Y. Stelios, N. Papayanoulas, P. Trakadas, S. Maniatis, H. C. Leligou, and T. Zahariadis, ‘A distributed energy-aware trust management system for secure routing in wireless sensor networks’, International Conference on Mobile Lightweight Wireless Systems, pp. 85–92, 2009.

T. Zahariadis, H. Leligou, P. Karkazis, and P. Trakadas, ‘Energy efficiency and implementation cost of trust-aware routing solutions in WSNs’, in2010 14th Panhellenic Conference on Informatics, pp. 194–198, 2010.

M. García-Otero et al., ‘Secure geographic routing in ad hoc and wireless sensor networks’, inEURASIP Journal on Wireless Communications and Networking, vol. pp. 1–12, 2010.

H. C. Leligou et al., ‘The impact of indirect trust information exchange on network performance and energy consumption in wireless sensor networks’, in Proceedings ELMAR-2011, pp. 153–156, 2011.

A. Yasin and K. Sabaneh, ‘Enhancing Wireless Sensor Network Security using Artificial Neural Network based Trust Model’, Int. J. Adv. Comput. Sci. Appl, vol. 7, no. 9, pp. 222–228, 2016.

Fuad A. Ghaleb, Anazida, Zainal, Murad, A. Rassam, and Fathey Mohammed, ‘An Effective Misbehavior Detection Model using Artificial Neural Network for Vehicular Ad hoc Network Applications’, IEEE Conference on Application, Information and Network Security (AINS), 2017.

Farah Sanhaji, Hassan Satori, Khalis Satori, ‘Cluster Head Selection based on Neural Networks in Wireless Sensor Networks’ 978-1-5386-7850-3/19/$31.00, IEEE, 2019.

Y. Trofimova, A. M. Moucha, and P. Tvrdik, ‘Application of neural networks for decision making and evaluation of trust in ad-hoc networks’, in 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), ‘pp. 371–377, 2017.

R. Azmi, M. Hakimi, and Z. Bahmani, ‘Dynamic reputation based trust management using neural network approach’, in International Journal of Computer Science Issues (IJCSI), vol. 8, no. 5, p. 161, 2011.

J. Krenek, K. Kuca, O. Krejcar, P. Maresova, V. Sobeslav, and P. Blazek, ‘Artificial neural network tools for computerised data modeling and processing’, in 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 255–260, 2014.

G. Mahalakshmi, E. Uma, M. Vinitha, and M. Aroosiya, “VANET: Trust Evaluation Using Artificial Neural Network”, in Advances in Parallel Computing Technologies and Applications, IOS Press, pp. 9–17, 2021.

D. P. Kumar, T. Amgoth, and C. S. R. Annavarapu, ‘Machine learning algorithms for wireless sensor networks: A survey’, Information Fusion, vol. 49, pp.1–25, 2019.

H. Kaur and S. Sahore, ‘A survey on wireless sensor network (wsn) security using AI methods’, Int. J. Latest Trends Eng. Technol, vol. 7, no. 4, pp. 234–239, 2016.

M. A. Alsheikh, S. Lin, D. Niyato, and H.-P. Tan, ‘Machine learning in wireless sensor networks: Algorithms, strategies, and applications’, in IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1996–2018, 2015.

G. M. Jinarajadasa and S. R. Liyange, ‘A survey on applying machine learning to enhance trust in mobile adhoc networks’, in 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), pp. 195–201, 2020.

Yi Zhao, ‘Combination of Wireless sensor network and artificial neural network: A new approach of Modeling’, in 2013.

A. F. Gad, A. F. Gad, and S. John, ‘Practical computer vision applications using deep learning with CNNs’, Springer, 2018.

A. F. Gad and F. E. Jarmouni, ‘Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide’, Academic Press, 2020.

R. v Kulkarni and G. K. Venayagamoorthy, ‘Neural network based secure media access control protocol for wireless sensor networks’, in 2009 international joint conference on neural networks, pp. 1680–1687, 2009.

B. Rajasekaran and C. Arun, ‘Detection of malicious nodes in wireless sensor networks based on features using neural network computing approach’, International Journal of Recent Technology and Engineering, vol. 7, no.4, pp. 188–192, 2018.

A. Sobeih et al., ‘J-sim: A simulation environment for wireless sensor networks’, in 38th Annual Simulation Symposium, pp. 175–187, 2005.

Y. G. G. H. C. H. H. K. L. K. N. L. H. L. A. S. H. T. H. Z. R. Zheng. Wei-peng Chen, ‘https://sites.google.com/site/jsimofficial/’, 2013.

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Published

2023-06-30

How to Cite

1.
Hassan KMA, Madkour MA, Nouh SAEH. A Realtime Adaptive Trust Model Based on Artificial Neural Networks for Wireless Sensor Networks. JCSANDM [Internet]. 2023 Jun. 30 [cited 2024 Jul. 11];12(04):519-46. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/19103

Issue

2023: Vol 12 Iss 4

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Articles

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