A Novel Approach for Developing Intrusion Detection Systems in Mobile Social Networks

Authors

DOI:

https://doi.org/10.31181/jscda31202576

Keywords:

Intrusion Detection Systems, Mobile Social Networks, Ad Hoc Networks, Packet Drop Attack, Black Hole Attack, Sleep Deprivation Attack, TCP SYN Attack

Abstract

Intrusion Detection Systems (IDS) have been developed to improve security levels in networks. These systems can observe user activities and, upon detecting any abnormal behavior suggesting a potential hacker, issue an alert to network security managers while simultaneously limiting the hacker's activities. In this study, we propose an IDS based on communication analysis in social networks for ad hoc networks. The proposed IDS monitors the various relationships between ad hoc nodes and triggers an alert when one or more rules governing normal communications are violated. The performance of the proposed IDS is assessed under different system factors (such as mobility and traffic load). In this study, we tested the system against common attacks (such as packet dropping, black hole, sleep deprivation, and TCP SYN attacks). The results show that the proposed IDS can detect attacks with high speed, and the rate of false attack alerts is lower compared to previous work. Moreover, the results show that the proposed IDS offers significant advantages over IDSs based on the Apriori algorithm (in terms of computational complexity).

Downloads

Download data is not yet available.

References

Kavitha, D. (2017). Survey of data mining techniques for social networking websites. International Journal of Computer Science and Mobile Computing, 6(4), 418-426.

Adedoyin-Olowe, M., Gaber, M. M., & Stahl, F. (2013). A Survey of Data Mining Techniques for Social Media Analysis. arXiv preprint arXiv:1312.4617. https://doi.org/10.48550/arXiv.1312.4617

Nasiri, S., Shahabi, S., Shafiesabet, A., Talebbeidokhti, M., & Behineh, E. A. (2026). Cybersecurity in Action: Unraveling the Effects of Individual, Social, and Organizational Determinants. Tehnički glasnik, 20(2), 1-10. https://doi.org/10.31803/tg-20240627004731

Provan, K. G., Fish, A., & Sydow, J. (2007). Interorganizational networks at the network level: A review of the empirical literature on whole networks. Journal of management, 33(3), 479-516. https://doi.org/10.1177/0149206307302554

Mirfathollahi, A., Ghodrati, M. T., Shalchyan, V., Zarrindast, M. R., & Daliri, M. R. (2023). Decoding hand kinetics and kinematics using somatosensory cortex activity in active and passive movement. iScience, 26(10), 107808. https://doi.org/10.1016/j.isci.2023.107808

Baheti, R., & Gill, H. (2011). Cyber-physical systems. The impact of control technology, 12(1), 161-166.

Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual review of clinical psychology, 14, 91-118. https://doi.org/10.1146/annurev-clinpsy-032816-045037

Roshdieh, N. (2024). The Effect of Monetary Policy Uncertainty on Stock Market Uncertainty with NARDL Approach. Research Journal of Finance and Accounting, 15(10), 1-9.

Hosseinidoust, S. E., Fotros, M. H., & Massahi, S. (2016). Application of Dynamic Parametric and Non Parametric Systems in Stock Market Return Forecasting: Case Study of Tehran Stock Market. Quarterly Journal of Fiscal and Economic Policies, 3(12), 125-148. http://qjfep.ir/article-1-289-en.html

Bagherabad, M. B., Rivandi, E., & Mehr, M. J. (2025). Machine Learning for Analyzing Effects of Various Factors on Business Economic. Authorea Preprints. https://doi.org/10.36227/techrxiv.174429010.09842200/v1

Rivandi, E. (2024). FinTech and the Level of Its Adoption in Different Countries Around the World. Available at SSRN 5049827. https://dx.doi.org/10.2139/ssrn.5049827

Heidari, S., Hashemi, S., Khorsand, M. S., Daneshfar, A., & Jazayerifar, S. (2024). Towards standardized regulations for block chain smart contracts: Insights from Delphi and SWARA analysis. Amity Journal of Management, XI(II), 1-15. https://doi.org/10.31620/AJM.1121

Karizaki, M. S., Gnesdilow, D., Puntambekar, S., & Passonneau, R. J. (2024, July). How Well Can You Articulate that Idea? Insights from Automated Formative Assessment. In International Conference on Artificial Intelligence in Education (pp. 225-233). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64299-9_16

Sadeghi, S., & Niu, C. (2024). Augmenting Human Decision-Making in K-12 Education: The Role of Artificial Intelligence in Assisting the Recruitment and Retention of Teachers of Color for Enhanced Diversity and Inclusivity. Leadership and Policy in Schools, 1-21. https://doi.org/10.1080/15700763.2024.2358303

Nikzat, P., & Noorymotlagh, M. (2025). Artificial Intelligence in Business: Driving Innovation and Competitive Advantage. International journal of industrial engineering and operational research, 7(3), 50-62. https://doi.org/10.22034/ijieor.v7i3.180

Fire, M., Katz, G., & Elovici, Y. (2012). Strangers intrusion detection-detecting spammers and fake profiles in social networks based on topology anomalies. Human journal, 1(1), 26-39.

Sayar, A. A., Pawar, S. N., & Mane, V. (2014). A review of intrusion detection system in computer network. International Journal of Computer Science and Mobile Computing, 3(2), 700-703.

Retnaswamy, B., & Ponniah, K. K. (2016). A new ontology‐based multi agent framework for intrusion detection. International Journal of Communication Systems, 29(17), 2490-2502. https://doi.org/10.1002/dac.3189

Al-Badawy, A. M., Abbas, H. M., & Belal, M. (2018). A TTP-Free Location Privacy Framework for Mobile Social Networks with Key Agreement Protocol. International Journal of Applied Engineering Research, 13(14), 11540-11547.

Aleid, H., Alkhalaf, A. A., Taemees, A. H., Almurished, H. S., Alharbi, K. A., & Alanazi, N. H. (2018). Framework to classify and analyze social media content. Social Networking, 7(02), 79.

Huang, Y. A., & Lee, W. (2003, October). A cooperative intrusion detection system for ad hoc networks. In Proceedings of the 1st ACM workshop on Security of ad hoc and sensor networks (pp. 135-147). https://doi.org/10.1145/986858.986877

Abapour, N., Shafiesabet, A., & Mahboub, R. (2021). A novel security based routing method using ant colony optimization algorithms and RPL protocol in the IoT networks. Mapta Journal of Electrical and Computer Engineering (MJECE), 3(1), 1-9.

Al-Khanjari, Z., Alanee, A., Kraiem, N., & Jamoussi, Y. (2013). Proposing a real time internal intrusion detection system towards a secured development of e-government web site. European Scientific Journal, 27-37.

Latifi, T., Li, J., Blum, S. C., & Fowler, D. (2024). Determinants of Users’ Intention to Visit a Destination: A Virtual Reality Quality Framework. Journal of Quality Assurance in Hospitality & Tourism, 1-25. https://doi.org/10.1080/1528008X.2024.2440010

Azimi Asmaroud, S. (2022). Preservice Elementary Teachers’ Categorical Reasoning and Knowledge Transfer on Definition Tasks With Two Dimensional Figures (Publication No. 1588) [Doctoral dissertation, Illinois State University]. Theses and Dissertations. https://ir.library.illinoisstate.edu/etd/1588

Zhang, Q., Deng, R., Ding, K., & Li, M. (2024). Structural analysis and the sum of nodes’ betweenness centrality in complex networks. Chaos, Solitons & Fractals, 185, 115158. https://doi.org/10.1016/j.chaos.2024.115158

Downloads

Published

2025-10-20

Data Availability Statement

Data is available from the corresponding author upon reasonable request.

Issue

Vol. 3 No. 1 (2025): Journal of Soft Computing and Decision Analytics

Section

Articles

License

Copyright (c) 2025 Elaheh Rivandi, Rozita Jamili Oskouei (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Rivandi, E., & Oskouei, R. J. (2025). A Novel Approach for Developing Intrusion Detection Systems in Mobile Social Networks. Journal of Soft Computing and Decision Analytics, 3(1), 158-170. https://doi.org/10.31181/jscda31202576