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).
















Similar content being viewed by others
References
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.
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.
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.
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).
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).
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.
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).
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
Singh, D., & Bedi, S. S. (2016). Multiclass ELM based smart trustworthy IDS for MANETs. Arabian Journal for Science and Engineering,41(8), 3127–3137.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zhang. (2009). Ad hoc Thesis.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-019-07022-5