Design of Intrusion Detection and Prevention Model Using COOT Optimization and Hybrid LSTM-KNN Classifier for MANET




Intrusion detection & prevention, Ad hoc network security, MANETs, COOT optimization, hybrid KSTM-KNN, FIS, two-factor authentication, DNA cryptography


INTRODUCTION: MANET is an emerging technology that has gained traction in a variety of applications due to its ability to analyze large amounts of data in a short period of time. Thus, these systems are facing a variety of security vulnerabilities and malware assaults. Therefore, it is essential to design an effective, proactive and accurate Intrusion Detection System (IDS) to mitigate these attacks present in the network. Most previous IDS faced challenges such as low detection accuracy, decreased efficiency in sensing novel forms of attacks, and a high false alarm rate.

OBJECTIVES: To mitigate these concerns, the proposed model designed an efficient intrusion detection and prevention model using COOT optimization and a hybrid LSTM-KNN classifier for MANET to improve network security.

METHODS: The proposed intrusion detection and prevention approach consist of four phases such as classifying normal node from attack node, predicting different types of attacks, finding the frequency of attack, and intrusion prevention mechanism. The initial phases are done through COOT optimization to find the optimal trust value for identifying attack nodes from normal nodes. In the second stage, a hybrid LSTM-KNN model is introduced for the detection of different kinds of attacks in the network. The third stage performs to classify the occurrence of attacks.

RESULTS: The final stage is intended to limit the number of attack nodes present in the system. The proposed method's effectiveness is validated by some metrics, which achieved 96 per cent accuracy, 98 per cent specificity, and 35 seconds of execution time.

CONCLUSION: This experimental analysis reveals that the proposed security approach effectively mitigates the malicious attack in MANET.


Abid I, Rasool, RM. Saleem M, Aleem M and Hanif R. Intrusion Detection Mechanism to Mitigate Intrusion In MANET.

Sivanesh S and Dhulipala VRS. Analytical Termination of Malicious Nodes (ATOM): An Intrusion Detection System for Detecting Black Hole attack in Mobile Ad Hoc Networks. Wireless Personal Communications, 2021, 1-14.

Sivanesh S and Dhulipala VR. Accurate and cognitive intrusion detection system (ACIDS): a novel black hole detection mechanism in mobile ad hoc networks. Mobile Networks and Applications, 2021, 26(4), 1696-1704.

Valiveti, Ramakrishna S, Manglani A and Desai T. Anomaly-Based Intrusion Detection Systems for Mobile Ad Hoc Networks: A Practical Comprehension. International Journal of Systems and Software Security and Protection (IJSSSP), 2021, 12(2), 11-32.

Makani, Ruchi, and Reddy BVR. Trust-based-tuning of Bayesian-watchdog intrusion detection for fast and improved detection of black hole attacks in mobile ad hoc networks. International Journal of Advanced Intelligence Paradigms, 2022, 21(1-2), 53-71.

Popli, Renu, Sethi M, Kansal I, Garg A and Goyal N. Machine Learning Based Security Solutions in MANETs: State of the art approaches. In Journal of Physics: Conference Series, IOP Publishing, 2021, 1950(1), 012070

Raj, Paul AA and Mozhi JKK. Real-Time Multi Level Behavioral Analysis Model for Efficient Intrusion Detection in Manet. Malaya Journal of Matematik, 2021, S1, 140-144.

Zardari, Z. Ali, He J, Pathan MS, Qureshi S, Hussain MI, Razaque F, He P and Zhu N. Detection and prevention of Jellyfish attacks using kNN algorithm and trusted routing scheme in MANET. International Journal of Network Security, 2021, 23(1), 77-87.

Farahani and Gholamreza. Black hole attack detection using K-nearest neighbor algorithm and reputation calculation in mobile ad hoc networks. Security and Communication Networks, 2021, 2021.

Singh, Saurabh, Sharma S, Sharma S, Alfarraj O, Yoon B and Tolba A. Intrusion Detection System based Security Mechanism for Vehicular ad-hoc Networks for Industrial IoT. IEEE Consumer Electronics Magazine, 2021.

Ahmed, Siraj NS and Acharjya DP. A framework for various attack identification in manet using multi-granular rough set. In Research Anthology on Securing Mobile Technologies and Applications, IGI Global, 2021, pp. 119-143.

Srilakshmi, Uppalapati, Alghamdi S, Ankalu V V, Veeraiah N and Alotaibi Y. A secure optimization routing algorithm for mobile ad hoc networks. IEEE Access, 2022.

Alghamdi and Saleh A. Novel trust-aware intrusion detection and prevention system for 5G MANET–Cloud. International Journal of Information Security, 2021, 1-20.

Kowsigan M, Rajeshkumar J, Baranidharan B, Prasath N, Nalini S and Venkatachalam K. A novel intrusion detection system to alleviate the black hole attacks to improve the security and performance of the MANET. Wireless Personal Communications, 2021, 1-21.

Kondaiah, Ramireddy and Sathyanarayana B. Trust factor and fuzzy-firefly integrated particle swarm optimization based intrusion detection and prevention system for secure routing of manet. International Journal of Computer Sciences and Engineering, 2018, 10(1), 13-33.

Doss, Srinath, Nayyar A, Suseendran G, Tanwar S, Khanna A and Thong PH. APD-JFAD: Accurate prevention and detection of jelly fish attack in MANET. IEEE Access, 2018, 6, 56954-56965.

Verma, Vanita and Jha VK Detection and Prevention of Vampire Attack for MANET. In Nanoelectronics, Circuits and Communication Systems, pp. 81-90. Springer, Singapore, 2021.

Naruei, Iraj and Keynia F. A new optimization method based on COOT bird natural life model. Expert Systems with Applications, 2021, 183, 115352.

Henderi, Henderi, Wahyuningsih T and Rahwanto E. Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. International Journal of Informatics and Information Systems, 2021, 4(1), 13-20.

Horasan and Fahrettin. Latent Semantic Indexing-Based Hybrid Collaborative Filtering for Recommender Systems. Arabian Journal for Science and Engineering, 2022, 1-15.

Mohiyuddin, Aqsa, Javed AR, Chakraborty C, Rizwan M, Shabbir M and Nebhen J. Secure cloud storage for medical IoT data using adaptive neuro-fuzzy inference system. International Journal of Fuzzy Systems, 2021, 1-13.

Pawar, Mohandas V and Anuradha. Detection and prevention of black-hole and wormhole attacks in wireless sensor network using optimized LSTM. International Journal of Pervasive Computing and Communications, 2021.

Zardari, Ali Z, He J, Pathan MS, Qureshi S, Hussain MI, Razaque F, He P and Zhu N. Detection and prevention of Jellyfish attacks using kNN algorithm and trusted routing scheme in MANET. International Journal of Network Security, 2021, 23(1), 77-87.

Kolate, Varsha, and Joshi RB. An Information Security Using DNA Cryptography along with AES Algorithm. Turkish Journal of Computer and Mathematics Education, 2021, 12(1S), 183-192.

Zou, L., Wang, X., & Deng, L. (2021). Secure Data Fusion Analysis on Certificateless Short Signature Scheme Based on Integrated Neural Networks and Elliptic Curve Cryptography. EAI Endorsed Transactions on Scalable Information Systems, 9(34).

Nouman, M., Ullah, K., & Azam, M. (2021). Secure Digital Transactions in The Education Sector Using Blockchain. EAI Endorsed Transactions on Scalable Information Systems, 9(35).

Yin, J., Tang, M., Cao, J., You, M., Wang, H., & Alazab, M. (2022). Knowledge-driven cybersecurity intelligence: Software vulnerability co-exploitation behaviour discovery. IEEE Transactions on Industrial Informatics.

You, M., Yin, J., Wang, H., Cao, J., Wang, K., Miao, Y., & Bertino, E. (2022). A knowledge graph empowered online learning framework for access control decision-making. World Wide Web, 1-22.

Yin, J., Tang, M., Cao, J., Wang, H., You, M., & Lin, Y. (2022). Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web, 25(1), 401-423.

Jacobsson, A., & Gustavsson, C. (2003). Prediction of the number of residue contacts in proteins using LSTM neural networks. Halmstad University, 9.

Liao, Y., & Vemuri, V. R. (2002). Use of k-nearest neighbor classifier for intrusion detection. Computers & security, 21(5), 439-448.

Abbod, M. F., von Keyserlingk, D. G., Linkens, D. A., & Mahfouf, M. (2001). Survey of utilisation of fuzzy technology in medicine and healthcare. Fuzzy Sets and Systems, 120(2), 331-349.




How to Cite

G. M. Design of Intrusion Detection and Prevention Model Using COOT Optimization and Hybrid LSTM-KNN Classifier for MANET. EAI Endorsed Scal Inf Syst [Internet]. 2022 Dec. 27 [cited 2024 May 23];10(3):e2. Available from: