Leveraging Meaningful Intrusion Detection Mechanism for Voice Traffic Patterns
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Abstract
Voice over IP (VoIP) communication has emerged as an important way of communicating as a modern digital medium but it has been facing a lot of cyber threats because of the protocols involved and the real-time aspect associated with it. conventional Intrusion Detection Systems (IDS) do not cope well with VoIP traffic, as signature-based solutions fail to handle various types of intrusions, while anomaly-based approaches tend to produce a high false positive rate. In this research, a hybrid intrusion detection mechanism is integrated, a lightweight and application-based system built around signature matching and anomaly detection to offer advanced protection to VoIP traffic. The system permits traffic analysis offline, based on Packet Capture (PCAP) or Comma Separated Values (CSV) files, as well as active traffic observation, which is provided using two python-based user interfaces, a desktop Tkinter interface and a web interface based on Flask. The predefined Session Initiation Protocol (SIP) or Real-time Transport Protocol (RTP) signature rules namely SIP Flood, RTP Injection, and SIP Brute-force are used as part of hybrid detection engine together with anomaly discovery based on one-class SVM. Other options available are a SIP honeypot listener, email alert notifications and there is a complete logging of incidents. The historical inspection are facilitated by such visualizations as protocol distribution or traffic trend analysis. Through testing, the system is effective in the detection of the VoIP attacks without tuning of the thresholds, and at low false alarm rates. This hybrid IDS demonstrates the feasibility of the signature and anomaly-based detection integration as a way of ensuring VoIP traffic monitoring and as a baseline of any further enhancement with the help of more advanced machine learning or enterprise use.
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References
[1] F. Y. Lee, S. P. Kumaresan, E. Abdulwahab Anaam, and W. Chee Kong, “Social platforms in the deepfake age: Navigating media trust through media literacy,” International Journal on Informatics Visualization, vol. 9, no. 1, pp. 176, Jan. 2025, doi: 10.62527/joiv.9.1.3490.
[2] S. P. Kumaresan, L. F. Yee, N. Palanichamy, and E. Annan, “Improving human pose estimation with integrated dual self-attention mechanism in high-resolution network,” Journal of Telecommunications and the Digital Economy, vol. 12, no. 3, pp. 7–28, Sep. 2024, doi: 10.18080/jtde.v12n3.984.
[3] P. Sornsuwit and S. Jaiyen, “A new hybrid machine learning for cybersecurity threat detection based on adaptive boosting,” Applied Artificial Intelligence, vol. 33, no. 5, pp. 462–482, Mar. 2019, doi: 10.1080/08839514.2019.1582861.
[4] R. E. Yoro et al., “Adaptive DDoS detection mode in software-defined SIP-VoIP using transfer learning with boosted meta-learner,” PLoS One, vol. 20, no. 6, pp. e0326571, Jun. 2025, doi: 10.1371/journal.pone.0326571.
[5] E. M. Maseno, Z. Wang, and H. Xing, “A systematic review on hybrid intrusion detection system,” Security and Communication Networks, vol. 2022, pp. 1–23, May 2022, doi: 10.1155/2022/9663052.
[6] C. Choti, N. Hnoohom, S. Tritilanunt, and S. Yuenyong, “Prediction of intrusion detection in voice over internet protocol system using machine learning,” in Proceedings of the 9th International Conference on Computer and Communications Management, Singapore Singapore: ACM, pp. 149–155, Jul. 2021, doi: 10.1145/3479162.3479185.
[7] R. Mokhtarnameh, N. Muthuvelu, S. B. Ho, and I. Chai, “A Comparison study on key exchange-authentication protocol,” International Journal of Computer Applications, vol. 7, no. 5, pp. 5–11, Sep. 2010, doi: 10.5120/1161-1459.
[8] A. Wulan, R. Rahman, and Desvi, “Security analysis of the VoIP (Voice Over Internet Protocol) system,” International Journal of Smart Systems, vol. 1, no. 3, pp. 105–116, Aug. 2023, doi: 10.63876/ijss.v1i3.69.
[9] M. T.-T. Yong, S.-B. Ho, and C.-H. Tan, “Migraine generative artificial intelligence based on mobile personalized healthcare,” Journal of Informatics and Web Engineering, vol. 4, no. 1, pp. 275, Feb. 2025, doi: 10.33093/jiwe.2025.4.1.20.
[10] O. Youness, “Secure and efficient authentication scheme for VoIP communications,” IJCI. International Journal of Computers and Information, vol. 0, no. 0, pp. 0–0, Sep. 2023, doi: 10.21608/ijci.2023.236399.1142.
[11] R. Laurens, E. Christianto, B. Caulkins, and C. C. Zou, “Side-channel voip profiling attack against customer service automated phone system,” in GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil: IEEE, pp. 6091–6096, Dec. 2022, doi: 10.1109/GLOBECOM48099.2022.10001537.
[12] J. C. Pang, F. Y. Lee, L. H. Chin, P. L. Ng, and S. Y. Te, “Developing work aspiration in students with learning disabilities in malaysia,” Asia Pacific Journal of Educators and Education, vol. 40, no. 1, 299–316, 2025, doi: 10.21315/apjee2025.40.1.15.
[13] W. C. Kong, R. Parthasarathy, and L. F. Yee, “Issues surrounding private university student data privacy, security and policy management,” In: Bhateja, V., Abdul Hameed, V., Udgata, S.K., Azar, A.T. (eds) Innovations in Communication Networks: Sustainability for Societal and Industrial Impact. ICDECT 2024. Lecture Notes in Networks and Systems, vol 1365. Springer, Singapore, 2025, doi: 10.1007/978-981-96-5223-5_34.
[14] T. Bilot, N. E. Madhoun, K. A. Agha, and A. Zouaoui, “Graph neural networks for intrusion detection: A survey,” IEEE Access, vol. 11, pp. 49114–49139, 2023, doi: 10.1109/ACCESS.2023.3275789.
[15] Z. Azam, Md. M. Islam, and M. N. Huda, “Comparative analysis of intrusion detection systems and machine learning-based model analysis through decision tree,” IEEE Access, vol. 11, pp. 80348–80391, 2023, doi: 10.1109/ACCESS.2023.3296444.
[16] Z. Li, W. Fang, C. Zhu, G. Song, and W. Zhang, “Toward deep learning based intrusion detection system: A survey,” in Proceedings of the 2024 6th International Conference on Big Data Engineering, Xining China: ACM, pp. 25–32, Jul. 2024, doi: 10.1145/3688574.3688578.
[17] A. H. Ali et al., “Unveiling machine learning strategies and considerations in intrusion detection systems: A comprehensive survey,” Frontiers in Computer Science, vol. 6, pp. 1387354, Jun. 2024, doi: 10.3389/fcomp.2024.1387354.
[18] L. Diana, P. Dini, and D. Paolini, “Overview on intrusion detection systems for computers networking security,” Computers, vol. 14, no. 3, pp. 87, Mar. 2025, doi: 10.3390/computers14030087.
[19] M. M. Rahman, S. A. Shakil, and M. R. Mustakim, “A survey on intrusion detection system in IoT networks,” Cyber Security and Applications, vol. 3, pp. 100082, Dec. 2025, doi: 10.1016/j.csa.2024.100082.
[20] A. Pinto, L.-C. Herrera, Y. Donoso, and J. A. Gutierrez, “Survey on intrusion detection systems based on machine learning techniques for the protection of critical infrastructure,” Sensors, vol. 23, no. 5, pp. 2415, Feb. 2023, doi: 10.3390/s23052415.
[21] N. Khan, K. Ahmad, A. A. Tamimi, M. M. Alani, A. Bermak, and I. Khalil, “Explainable AI-based intrusion detection system for industry 5.0: An overview of the literature, associated challenges, the existing solutions, and potential research directions,” arXiv, 2024, doi: 10.48550/ARXIV.2408.03335.
[22] X. Zhao, K. W. Fok, and V. L. L. Thing, “Enhancing network intrusion detection performance using generative adversarial networks,” 2arXiv, 024, doi: 10.48550/ARXIV.2404.07464.
[23] V. S. R. Pavan Kumar Neeli and V. R. G. Nallagattla, “Leveraging machine learning techniques for intrusion detection system using UNSW-NB15 dataset,” in 2025 IEEE 2nd International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST), Jamshedpur, India: IEEE, pp. 1–6, Oct. 2025, doi: 10.1109/GIEST66547.2025.11387588.
[24] Y.-S. Wu, V. Apte, S. Bagchi, S. Garg, and N. Singh, “Intrusion detection in voice over IP environments,” International Journal of Information Security, vol. 8, no. 3, pp. 153–172, Jun. 2009, doi: 10.1007/s10207-008-0071-0.
[25] E. Anaam, S.-C. Haw, K.-W. Ng, N. Palanichamy, and J. Jayaram, “Applying fuzzy algorithm for new hybrid recommendation system in cybersecurity,” pp. 030002, Online, 2025, doi: 10.1063/5.0299249.
[26] O. H. Abdulganiyu, T. Ait Tchakoucht, and Y. K. Saheed, “A systematic literature review for network Intrusion Detection System (IDS),” International Journal of Information Security, vol. 22, no. 5, pp. 1125–1162, Oct. 2023, doi: 10.1007/s10207-023-00682-2.
[27] I. Syllaidopoulos, K. S. Ntalianis, and I. Salmon, “A comprehensive survey on AI in counter-terrorism and cybersecurity: Challenges and ethical dimensions,” IEEE Access, vol. 13, pp. 91740–91764, 2025, doi: 10.1109/ACCESS.2025.3572348.
[28] K. Achuthan, S. Ramanathan, S. Srinivas, and R. Raman, “Advancing cybersecurity and privacy with artificial intelligence: Current trends and future research directions,” Frontiers Big Data, vol. 7, pp. 1497535, Dec. 2024, doi: 10.3389/fdata.2024.1497535.
[29] O. Aslan, S. S. Aktug, M. Ozkan-Okay, A. A. Yilmaz, and E. Akin, “A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions,” Electronics, vol. 12, no. 6, pp. 1333, Mar. 2023, doi: 10.3390/electronics12061333.
[30] J. Zhang et al., “When LLMs meet cybersecurity: A systematic literature review,” arXiv, 2024, doi: 10.48550/ARXIV.2405.03644.
[31] H. Xu et al., “Large Language Models for cyber security: A systematic literature review,” arXiv, 2024, doi: 10.48550/ARXIV.2405.04760.
[32] F. Cremer et al., “Cyber risk and cybersecurity: A systematic review of data availability,” The Geneva Papers on Risk and Insurance - Issues and Practice, vol. 47, no. 3, pp. 698–736, Jul. 2022, doi: 10.1057/s41288-022-00266-6.
[33] M. I. B. Hanafiah et. al., “TSM-NIDS: A time-series mixer-based intrusion detection system for IoT networks,” MethodsX, vol. 16, pp. 103885, Jun. 2026, doi: 10.1016/j.mex.2026.103885.
[34] D. P. F. Moller, “Cybersecurity in digital transformation,” in Guide to Cybersecurity in Digital Transformation, vol. 103, Cham: Springer Nature Switzerland, pp. 1–70, 2023, doi: 10.1007/978-3-031-26845-8_1.