• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 15, Pages: 677-688

Original Article

Enhanced Network Security for Improved Trustworthiness of VoIP Applications via Cuckoo Search and Machine Learning

Received Date:27 July 2021, Accepted Date:01 March 2022, Published Date:15 April 2022


Objectives: To offer network design for secure Voice over Internet Protocol (VoIP) services with improved Quality of Services (QoS) parameters. Methods: The network area is created with required number of nodes. INTRA-SR process is employed for tracing of the route from the source node and INTER-SR is involved to reach a destination node. Cuckoo Search (CS) based optimization followed for the broadcasted voice packets. The Machine learning classifiers as SVM and ANN applied to decrease the instances of loss of voice packets. Simulation work is performed by using MATLAB 2018 and results obtained plotted in graphs using MS-Excel. Findings: The proposed design evaluated by incorporating CS algorithm to minimize the packet drops. SVM and ANN hybrid used to locate secure routing path. The QoS for throughput, latency and jitter are observed. The results exhibited higher average throughput of 98.8% irrespective of the attack instances. Lower average latency and jitter of 1.9s and 2.51ms are also exhibited by the proposed work. Similarly, latency work employing Ant Colony Optimization (ACO) with multiplex and multicasting (MM) is 2.66s that get increased to 3.16s due to attack. Novelty/Applications: The proposed algorithm significantly enhanced by deployment of highly protected network design with QoS and security. Application of ANN and SVM shown the improvement in performance for VoIP services. In addition to the regression analysis validates the results by applying other optimization algorithms.

Keywords: Voice over Internet Protocol; Cuckoo Search; Support Vector Machine; Artificial Neural Network; Machine Learning


  1. Tole S, Lina H, Deris S, Agus RM. Riyadi Munawar Agus, Subroto Imam Much Ibnu. WhatsApp, viber and telegram: Which is the best for instant messaging? International Journal of Electrical & Computer Engineering. 2016;6(3):2088–8708. Available from: http://dx.doi.org/10.11591/ijece.v6i3.10271
  2. Kenton OH, Michael M, Richard H, Simon R, Jessica M. Everyday dwelling with WhatsApp. Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. 2014;15:1131–1143. Available from: http://dx.doi.org/10.1145/2531602.2531679
  3. Mohammed B, Ismail S, Tamer R, Bou NA. Maximizing embedding capacity for speech steganography: a segment-growing approach. Multimedia Tools and Applications. 2021;7:1–22. Available from: https://doi.org/10.1007/s11042-020-10228-6
  4. Elhalifa C, Lian L. Security of VoIP networks. 2nd International Conference on Computer Engineering and Technology IEEE. 2010;16:3–104. Available from: https://doi.org/10.1109/ICCET.2010.5485790
  5. Nikooghadam M, Amintoosi H. Perfect forward secrecy via an ECC-based authentication scheme for SIP in VoIP. The Journal of Supercomputing. 2020;76(4):3086–3104. Available from: https://dx.doi.org/10.1007/s11227-019-03086-z
  6. Alkhalil Z, Hewage C, Nawaf L, Khan I. Phishing Attacks: A Recent Comprehensive Study and a New Anatomy. Frontiers in Computer Science. 2021;3(6). Available from: https://dx.doi.org/10.3389/fcomp.2021.563060
  7. Qinyin C, Dong N, Jingbo X, Hsien-Wei T. Research on assessment method of network quality performance for a private data network. Microsystem Technologies. 2019;9:1–8. Available from: https://doi.org/10.1007/s00542-019-04383-6
  8. Faycal B, Najib EK, Ayoub B. Scalability evaluation of VOIP over various MPLS tunneling under OPNET modeler. Indian Journal of Science and Technology. 2009;10(29):1–8. Available from: https://doi.org/10.17485/ijst/2017/v10i29/117369
  9. Khundrakpam JS, Tanmay D. Efficient classification of DDoS attacks using an ensemble feature selection algorithm. Journal of Intelligent Systems. 2020;29(1):71–83. Available from: https://doi.org/10.1515/jisys-2017-0472
  10. Sadeghzadeh AM, Shiravi S, Jalili R. Adversarial Network Traffic: Towards Evaluating the Robustness of Deep-Learning-Based Network Traffic Classification. IEEE Transactions on Network and Service Management. 2021;18(2):1962–1976. Available from: https://dx.doi.org/10.1109/tnsm.2021.3052888
  11. Ehteram M, Singh VP, Ferdowsi A, Mousavi SF, Farzin S, Karami H, et al. An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration. PLOS ONE. 2019;14(5):e0217499. Available from: https://dx.doi.org/10.1371/journal.pone.0217499
  12. Dantu R, Fahmy S, Schulzrinne H, Cangussu J. Issues and challenges in securing VoIP. Computers & Security. 2009;28:743–753. Available from: https://dx.doi.org/10.1016/j.cose.2009.05.003
  13. Subashini P, Krishnaveni M, Dhivyaprabha TT, Shanmugavalli R. Review on Intelligent Algorithms for Cyber Security. Handbook of Research on Machine and Deep Learning Applications for Cyber Security. 2020;p. 1–22. Available from: https://doi.org/10.4018/978-1-5225-9611-0.ch001
  14. Chaddad L, Chehab A, Kayssi A. OPriv: Optimizing Privacy Protection for Network Traffic. Journal of Sensor and Actuator Networks. 2021;10(3):38. Available from: https://dx.doi.org/10.3390/jsan10030038
  15. Satapathy A, Livingston LMJ. A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud. Indian Journal of Science and Technology. 2016;9(6):1–3. Available from: https://dx.doi.org/10.17485/ijst/2016/v9i6/81980
  16. Addesso P, Cirillo M, Mauro MD, Matta V. ADVoIP: Adversarial Detection of Encrypted and Concealed VoIP. IEEE Transactions on Information Forensics and Security. 2020;15:943–958. Available from: https://dx.doi.org/10.1109/tifs.2019.2922398
  17. Ravanbakhsh N, Mohammadi M, Nikooghadam M. Perfect forward secrecy in VoIP networks through design a lightweight and secure authenticated communication scheme. Multimedia Tools and Applications. 2019;78:11129–11153. Available from: https://dx.doi.org/10.1007/s11042-018-6620-2
  18. Choudhury P, Kumar KRP, Nandi S, Athithan G. An empirical approach towards characterization of encrypted and unencrypted VoIP traffic. Multimedia Tools and Applications. 2020;79:603–631. Available from: https://dx.doi.org/10.1007/s11042-019-08088-w
  19. Alouneh S, Abed S, Ghinea G. Security of VoIP traffic over low or limited bandwidth networks. Security and Communication Networks. 2016;9:5591–5599. Available from: https://dx.doi.org/10.1002/sec.1719
  20. Wang R, Gao X, Gao J, Gao Z, Chen K, Peng C. An artificial immune and incremental learning inspired novel framework for performance pattern identification of complex electromechanical systems. Science China Technological Sciences. 2020;63(1):1–13. Available from: https://dx.doi.org/10.1007/s11431-019-9532-5
  21. Christabelle A, Dristi D, Syed A, Tannish G, Maheen H, Ali R. Dataset of attacks on a live enterprise VoIP network for machine learning based intrusion detection and prevention systems. Computer Networks. 2009;197:108283. Available from: https://doi.org/10.1016/j.comnet.2021.108283
  22. Mauro MD, Galatro G, Fortino G, Liotta A. Supervised feature selection techniques in network intrusion detection: A critical review. Engineering Applications of Artificial Intelligence. 2021;101:104216. Available from: https://dx.doi.org/10.1016/j.engappai.2021.104216
  23. Ramasamy S, , Eswaramoorthy K, . Ant Colony Optimization Based Handoff Scheme and Verifiable Secret Sharing Security with M-M Scheme for VoIP. International Journal of Intelligent Engineering and Systems. 2017;10(5):267–277. Available from: https://dx.doi.org/10.22266/ijies2017.1031.29
  24. Vijayalakshmi M, Rao DS. QoS aware multicasting using the enhanced differential evolution cuckoo search routing protocol in MANET. International Journal of Mobile Network Design and Innovation. 2018;8(4):215. Available from: https://doi.org/10.1504/IJMNDI.2018.095241
  25. Yassine M, Amar RC, Mohammed M, Dalila A. A hybrid quantum evolutionary algorithm with cuckoo search algorithm for QoS multicast routing problem. International Journal of Communication Networks and Distributed Systems. 2019;22(3):329–361. Available from: https://doi.org/10.1504/IJCNDS.2019.098873
  26. Akash S, Navneet S, Pawan A, Rohit B. Phishing Website Prediction by Using Cuckoo Search as a Feature Selection and Random Forest and BF-Tree Classifier as a Classification Method. Rising Threats in Expert Applications and Solutions . 2021;p. 765–776. Available from: https://doi.org/10.1007/978-981-15-6014-9_92
  27. Anandaraj APS, Indumathi G. Improved cuckoo search load distribution (ICS‐LD) and attack detection in cloud environment. Concurrency and Computation: Practice and Experience. 2021;33(3). Available from: https://doi.org/10.1002/cpe.5226
  28. Jain PK, Yekun EA, Pamula R, Srivastava G. Consumer recommendation prediction in online reviews using Cuckoo optimized machine learning models. Computers & Electrical Engineering. 2021;95:107397. Available from: https://dx.doi.org/10.1016/j.compeleceng.2021.107397
  29. Amjad K, Asfandyar K, Iqbal BJ, Fazli S, Abdullah K, Atif K, et al. Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks. Security and Communication Networks. 2021. Available from: https://doi.org/10.1155/2021/6654926
  30. Ji Yf, Song Lb, Sun J, Peng W, Li Hy, Ma Lf. Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling. Journal of Central South University. 2021;28(8):2333–2344. Available from: https://dx.doi.org/10.1007/s11771-021-4773-z
  31. Imran M, Khan S, Hlavacs H, Khan FA, Anwar S. Intrusion detection in networks using cuckoo search optimization. Soft Computing. 2022;3:1–3. Available from: https://dx.doi.org/10.1007/s00500-022-06798-2
  32. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon. 2018;4(11):e00938. Available from: https://dx.doi.org/10.1016/j.heliyon.2018.e00938
  33. Yang XS, Deb S. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation. 2010;1(4):330–343. Available from: https://dx.doi.org/10.1504/ijmmno.2010.035430
  34. Khuat TT, Le MH. A Novel Hybrid ABC-PSO Algorithm for Effort Estimation of Software Projects Using Agile Methodologies. Journal of Intelligent Systems. 2018;27(3):489–506. Available from: https://dx.doi.org/10.1515/jisys-2016-0294


© 2022 Kumar & Prakash Roy. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.