A Security Method for Intrusion Detection in Mobile Ad Hoc Networks Based on DSR Protocol

Document Type : Original Article

Authors

1 Department of Computer Engineering, Islamic Azad University,Ardabil , Iran

2 Department of Computer Engineering, Islamic Azad University,Germi branch , Germi

Abstract

A mobile ad hoc network consists of mobile nodes communicating with each other without centralized control or infrastructure. The inherent wireless nature of these networks introduces significant security challenges. However, recognizing that routing plays a pivotal role in most mobile ad hoc network operations, enhancing the security of routes can contribute to overall network performance. This paper introduces a novel technique aimed at improving intrusion detection in mobile ad hoc networks by identifying and detecting black hole nodes. The proposed solution involves the introduction of the S-DSR protocol, a variant of the DSR protocol. The primary objective is to enhance intrusion detection by identifying black hole nodes during the route detection phase and subsequently excluding routes containing them. This ensures secure data transmission and reception within the network. The protocol, named S-DSR, is designed to address these security concerns. The results obtained from simulations conducted in the NS-2 environment indicate that the S-DSR protocol outperforms the traditional DSR protocol in terms of network performance.

Keywords


  • Krishnan, R. S., Julie, E. G., Robinson, Y. H., Kumar, R., Son, L. H., Tuan, T. A., & Long, H. V. (2020). Modified zone based intrusion detection system for security enhancement in mobile ad hoc networks. Wireless Networks, 26(2), 1275–1289. doi:10.1007/s11276-019-02151-y
  • Khezri, E., & Zeinali, E. (2021). A review on highway routing protocols in vehicular ad hoc networks. SN Computer Science, 2(2). doi:10.1007/s42979-021-00451-9
  • Ferreira, M. A., Lukkarinen, J., Nota, A., & Vel'azquez, J. J. L. (2022). Non-power law constant flux solutions for the Smoluchowski coagulation equation.
  • Uddin, L. (2021). Cognitive and behavioural flexibility: neural mechanisms and clinical considerations.
  • Rao, P. C. S., Lalwani, P., Banka, H., & Rao, G. S. N. (2021). Competitive swarm optimization based unequal clustering and routing algorithms (CSO-UCRA) for wireless sensor networks. Multimedia Tools and Applications, 80(17), 26093–26119. doi:10.1007/s11042-021-10901-4
  • Srilakshmi, U., Alghamdi, S. A., Vuyyuru, V. A., Veeraiah, N., & Alotaibi, Y. (2022). A secure optimization routing algorithm for mobile ad hoc networks. IEEE Access: Practical Innovations, Open Solutions, 10, 14260–14269. doi:10.1109/access.2022.3144679
  • Saboksayr, S. S., & Mateos, G. (2023, June 4). Dual-based online learning of dynamic network topologies. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Presented at the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece. doi:10.1109/icassp49357.2023.10096392
  • Subhrahmanyam, G., & Ginimav, I. (2019). Intelligent Routing Mechanisms in IoT. Intelligent System and Computing.
  • Zipperle, M., Gottwalt, F., Chang, E., & Dillon, T. (2023). Provenance-based Intrusion Detection Systems: A survey. ACM Computing Surveys, 55(7), 1–36. doi:10.1145/3539605
  • Kanakogi, K., Washizaki, H., Fukazawa, Y., Ogata, S., Okubo, T., Kato, T., Yoshioka, N. (2022). Comparative evaluation of NLP-based approaches for linking CAPEC attack patterns from CVE vulnerability information. Applied Sciences (Basel, Switzerland), 12(7), 3400. doi:10.3390/app12073400
  • Tamilarasan, S. (2014). Securing AODV Routing Protocol from Black Hole Attack. International Journal of Computer Science and Telecommunications, 52–56.
  • Rutvij, H., & Sankita, J. (2016). DoS Attacks in Mobile Ad-hoc Networks: A Survey.Second International Conference on Advanced Computing & Communication Technologies. 535–540.
  • Gad, A. R., Nashat, A. A., & Barkat, T. M. (2021). Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset. IEEE Access: Practical Innovations, Open Solutions, 9, 142206–142217. doi:10.1109/access.2021.3120626
  • Alkadi, O., Moustafa, N., Turnbull, B., & Choo, K.-K. R. (2021). A deep blockchain framework-enabled collaborative intrusion detection for protecting IoT and cloud networks. IEEE Internet of Things Journal, 8(12), 9463–9472. doi:10.1109/jiot.2020.2996590
  • Srilakshmi, U., Alghamdi, S. A., Vuyyuru, V. A., Veeraiah, N., & Alotaibi, Y. (2022). A secure optimization routing algorithm for mobile ad hoc networks. IEEE Access: Practical Innovations, Open Solutions, 10, 14260–14269. doi:10.1109/access.2022.3144679
  • Mourad, A., Tout, H., Wahab, O. A., Otrok, H., & Dbouk, T. (2021). ad hoc vehicular fog enabling cooperative low-latency intrusion detection. IEEE Internet of Things Journal, 8(2), 829–843. doi:10.1109/jiot.2020.3008488
  • Jaisankar, N., Saravanan, R., & Swamy, K. D. (2010). A novel security approach for detecting black hole attack in MANET. In Communications in Computer and Information Science. Communications in Computer and Information Science (pp. 217–223). doi:10.1007/978-3-642-12214-9_36
  • Tamilselvan, L., & Sankaranarayanan, V. (2008). Prevention of co-operative black hole attack in MANET. Journal of Networks, 3(5). doi:10.4304/jnw.3.5.13-20
  • Deng, H., Li, W., & Agrawal, D. P. (2002). Routing security in wireless ad hoc networks. IEEE Communications Magazine, 40(10), 70–75. doi:10.1109/mcom.2002.1039859
  • Zarzoor, A. R. (2021, September 18). Enhancing dynamic source routing (DSR) protocol performance based on link quality metrics. 2021 International Seminar on Application for Technology of Information and Communication (iSemantic). Presented at the 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarangin, Indonesia. doi:10.1109/isemantic52711.2021.9573233
  • Samantaray, S., Sahoo, P., Sahoo, A., & Satapathy, D. P. (2023). Flood discharge prediction using improved ANFIS model combined with hybrid particle swarm optimisation and slime mould algorithm. Environmental Science and Pollution Research International, 30(35), 83845–83872. doi:10.1007/s11356-023-27844-y
  • Al-Shareeda, M. A., Anbar, M., Hasbullah, I. H., & Manickam, S. (2021). Survey of Authentication and Privacy Schemes in Vehicular ad hoc Networks. IEEE Sensors Journal, 21(2), 2422–2433. doi:10.1109/jsen.2020.3021731