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A Smart Approach for Intrusion Detection and Prevention System in Mobile Ad Hoc Networks Against Security Attacks

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Abstract

Design of intrusion detection and prevention scheme for improving MANET security, with considered energy efficiency, detection rate, delay, and false positive rate are major research issues. Most of the existing solutions have suffered to obtain accurate detection rate in minimal time execution and energy consumption. In this work we proposed a Smart approach for intrusion detection and prevention system (SA-IDPS) to mitigate attacks in MANET by machine learning methods. Initially, mobile users are registered in Trusted Authority using One Way Hash Chain Function. Each mobile user submits their following information to verify authentication: finger vein biometric, user id, and latitude and longitude. Intrusion detection is executed using four entities: Packet Analyzer, Preprocessing Unit, Feature Extraction Unit and Classification Unit. In packet analyzer, we verify whether any attack pattern is found or not. It is implemented using Type 2 Fuzzy Controller which considers information from packet header. In preprocessing unit, logarithmic normalization and encoding schemes are considered, which is time series and suitable for any application. In feature extraction unit, Mutual Information is used where we extracts optimum set of features for packets classification. In classification unit, Bootstrapped Optimistic Algorithm for Tree Construction with Artificial Neural Network is used for packets classification, which classifies packets five classes: DoS, Probe, U2R, R2L, and Anomaly, and then Association Rule Tree are used to classify whether the attack is Frequent or Rare. In this case, historical table is used for packets classification. Finally, experiments are conducted and tested for evaluating the performance of proposed SA-IDPS scheme in terms of Detection Rate (%), False Positive Rate (%), Detection Delay (s), and Energy Consumption (J).

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References

  1. Islabudeen, M., & Kavitha Devi, M. K. (2015). An efficient intrusion detection system with BOAT classifier to detect rare and frequent misuse attacks in MANET. International Journal of Applied Engineering Research, Research India Publications,10(55), 2633–2639.

    Google Scholar 

  2. Chaudhary, A., Tiwari, V. N., & Kumar, A. (2016). Design an anomaly-based intrusion detection system using soft computing for mobile ad hoc networks. International Journal of Soft Computing and Networking,1(1), 17.

    Article  Google Scholar 

  3. Meddeb, R., Triki, B., Jmili, F., & Korbaa, O. (2018). An effective IDS against routing attacks on mobile ad hoc networks. Frontiers in Artificial Intelligence and Applications, 303, 201–204.

    Google Scholar 

  4. Vegda, H., & Modi, N. (2018). Secure and efficient approach to prevent ad hoc network attacks using intrusion detection system. In 2018 Second international conference on intelligent computing and control systems (ICICCS).

  5. Sankaranarayanan, S., & Murugaboopathi, G. (2017). Secure intrusion detection system in mobile ad hoc networks using RSA algorithm. In 2017 Second international conference on recent trends and challenges in computational models (ICRTCCM).

  6. Abbas, S., Faisal, M., Rahman, H. U., Khan, M. Z., Merabti, M., & Khan, A. R. (2018). Masquerading attacks detection in mobile ad hoc networks. IEEE Access,6, 55013–55025.

    Article  Google Scholar 

  7. Borkar, A., Donode, A., & Kumari, A. (2017). A survey on intrusion detection system (IDS) and internal intrusion detection and protection system (SA-IDPS). In 2017 International conference on inventive computing and informatics (ICICI).

  8. Salo, F., Injadat, M., Nassif, A. B., Shami, A., & Essex, A. (2018). Data mining techniques in intrusion detection systems: A systematic literature review. IEEE Access,6, 56046–56058.

    Article  Google Scholar 

  9. Nishani, L., & Biba, M. (2015). Machine learning for intrusion detection in MANET: A state-of-the-art survey. Journal of Intelligent Information Systems,46(2), 391–407.

    Article  Google Scholar 

  10. Singh, D., Devendra, K., & Bedi, S. (2015). A survey: Feature based intrusion detection system in mobile ad-hoc network. International Journal of Computer Science and Technology,6, 135–140.

    Google Scholar 

  11. Shams, E. A., & Rizaner, A. (2017). A novel support vector machine based intrusion detection system for mobile ad hoc networks. Wireless Networks,24(5), 1821–1829.

    Article  Google Scholar 

  12. Veeraiah, N., & Krishna, T. B. (2019). Trust-aware FuzzyClus-Fuzzy NB: Intrusion detection scheme based on fuzzy clustering and Bayesian rule. Wireless Networks,25, 1–11.

    Article  Google Scholar 

  13. Resende, P. A. A., & Drummond, A. C. (2018). A survey of random forest based methods for intrusion detection systems. ACM Computing Surveys,51(3), 1–36.

    Article  Google Scholar 

  14. Ding, Y., & Zhai, Y. (2018). Intrusion detection system for NSL-KDD dataset using convolutional neural networks. In Proceedings of the 2018 2nd international conference on computer science and artificial intelligence—CSAI’18.

  15. Jinarajadasa, G., Rupasinghe, L., & Murray, I. (2018). A reinforcement learning approach to enhance the trust level of MANETs. In 2018 National information technology conference (NITC).

  16. Singh, O., Singh, J., & Singh, R. (2017). An intelligent intrusion detection and prevention system for safeguard mobile adhoc networks against malicious nodes. Indian Journal of Science and Technology,10(14), 1–12.

    Google Scholar 

  17. Yerur, S. V., Natarajan, P., & Rangaswamy, T. R. (2017). Proactive hybrid intrusion prevention system for mobile adhoc networks. International Journal of Intelligent Engineering and Systems,10, 273–283. https://doi.org/10.22266/ijies2017.1231.29.

    Article  Google Scholar 

  18. Wahab, O. A., Mourad, A., Otrok, H., & Bentahar, J. (2016). CEAP: SVM-based intelligent detection model for clustered vehicular ad hoc networks. Expert Systems with Applications,50, 40–54.

    Article  Google Scholar 

  19. Singh, D., & Bedi, S. S. (2016). Multiclass ELM based smart trustworthy IDS for MANETs. Arabian Journal for Science and Engineering,41(8), 3127–3137.

    Article  Google Scholar 

  20. Kolias, C., Kambourakis, G., Stavrou, A., & Gritzalis, S. (2016). Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset. IEEE Communications Surveys and Tutorials,18(1), 184–208.

    Article  Google Scholar 

  21. Subba, B., Biswas, S., & Karmakar, S. (2016). Intrusion detection in mobile ad-hoc networks: Bayesian game formulation. Engineering Science and Technology, an International Journal,19(2), 782–799.

    Article  Google Scholar 

  22. Ahmed, M. N., Abdullah, A. H., & Kaiwartya, O. (2016). FSM-F: Finite state machine based framework for denial of service and intrusion detection in MANET. PLoS ONE,11(6), e0156885.

    Article  Google Scholar 

  23. Shanthi, K., Murugan, D., & Ganesh Kumar, T. (2018). Trust-based intrusion detection with secure key management integrated into MANET. Information Security Journal: A Global Perspective,27, 1–9.

    Google Scholar 

  24. Khan, F. A., Imran, M., Abbas, H., & Durad, M. H. (2017). A detection and prevention system against collaborative attacks in mobile ad hoc networks. Future Generation Computer Systems,68, 416–427.

    Article  Google Scholar 

  25. Raja, R., & Ganesh Kumar, P. (2018). QoSTRP: A trusted clustering based routing protocol for mobile ad hoc networks. Programming and Computer Software,44(6), 407–416.

    Article  Google Scholar 

  26. Anusha, K., & Sathiyamoorthy, E. (2017). A new trust-based mechanism for detecting intrusions in MANET. Information Security Journal: A Global Perspective,26(4), 153–165.

    Google Scholar 

  27. Luong, N. T., Vo, T. T., & Hoang, D. (2019). FAPRP: A machine learning approach to flooding attacks prevention routing protocol in mobile ad hoc networks. Wireless Communications and Mobile Computing,2019, 1–17.

    Article  Google Scholar 

  28. Sasirekha, D., & Radha, N. (2017). Secure and attack aware routing in mobile ad hoc networks against wormhole and sinkhole attacks. In 2017 2nd international conference on communication and electronics systems (ICCES).

  29. Ahmad, I., Basheri, M., Iqbal, M. J., & Rahim, A. (2018). Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access,6, 33789–33795.

    Article  Google Scholar 

  30. Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access,5, 21954–21961.

    Article  Google Scholar 

  31. Khan, M. A., Karim, M. R., & Kim, Y. (2019). A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry,11(4), 583. https://doi.org/10.3390/sym11040583

    Article  Google Scholar 

  32. Xu, C., Shen, J., Du, X., & Zhang, F. (2018). An intrusion detection system using a deep neural network with gated recurrent units. IEEE Access,6, 1.

    Article  Google Scholar 

  33. Elwahsh, H., Gamal, M., Salama, A. A., & El-Henawy, I. M. (2018). A novel approach for classifying MANETs attacks with a neutrosophic intelligent system based on genetic algorithm. Security and Communication Networks,2018, 1–10.

    Article  Google Scholar 

  34. Vimala, S., Khanaa, V., & Nalini, C. (2018). A study on supervised machine learning algorithm to improvise intrusion detection systems for mobile ad hoc networks. Cluster Computing,22, 1–10.

    Google Scholar 

  35. Kavitha, T., Geetha, K., & Muthaiah, R. (2019). India: Intruder node detection and isolation action in mobile ad hoc networks using feature optimization and classification approach. Journal of Medical Systems,43(6), 1–7.

    Article  Google Scholar 

  36. Feng, F., Liu, X., Yong, B., Zhou, R., & Zhou, Q. (2018). Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device. Ad Hoc Networks,84, 82–89.

    Article  Google Scholar 

  37. Zhang, B., Liu, Z., Jia, Y., Ren, J., & Zhao, X. (2018). Network intrusion detection method based on PCA and Bayes algorithm. Security and Communication Networks,2018, 1–11.

    Google Scholar 

  38. Shafi, Q., Basit, A., Qaisar, S., Koay, A., & Welch, I. (2018). Fog-assisted SDN controlled framework for enduring anomaly detection in an IoT network. IEEE Access,6, 1.

    Article  Google Scholar 

  39. Bouhaddi, M., Radjef, M. S., & Adi, K. (2018). An efficient intrusion detection in resource-constrained mobile ad-hoc networks. Computers and Security,76, 156–177.

    Article  Google Scholar 

  40. Marchang, N., Datta, R., & Das, S. K. (2017). A novel approach for efficient usage of intrusion detection system in mobile ad hoc networks. IEEE Transactions on Vehicular Technology,66(2), 1684–1695.

    Article  Google Scholar 

  41. Zhang. (2009). Ad hoc Thesis.

Download references

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Authors and Affiliations

  1. Department of CSE, Syed Ammal Engineering College, Ramanathapuram, India

    M. Islabudeen

  2. Department of CSE, Thiagarajar College of Engineering, Madurai, India

    M. K. Kavitha Devi

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  1. M. Islabudeen
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  2. M. K. Kavitha Devi
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Correspondence to M. Islabudeen.

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Islabudeen, M., Kavitha Devi, M.K. A Smart Approach for Intrusion Detection and Prevention System in Mobile Ad Hoc Networks Against Security Attacks. Wireless Pers Commun 112, 193–224 (2020). https://doi.org/10.1007/s11277-019-07022-5

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  • DOI: https://doi.org/10.1007/s11277-019-07022-5

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