Deep Learning Based Network Intrusion Detection Framework for Secure Enterprise  Communication Systems

Kiran Desai; Kiran Desai

doi:10.15662/ijetemr.2016.0101001

Deep Learning Based Network Intrusion Detection Framework for Secure Enterprise Communication Systems

Kiran Desai

Dr. Ambedkar Institute of technology, Bangalore, Karnataka, India

Abstract

The rapid growth of enterprise communication systems and digital transformation initiatives has significantly increased the vulnerability of organizational networks to sophisticated cyberattacks. Traditional intrusion detection systems (IDS) often fail to detect complex and evolving threats due to their reliance on predefined signatures and static rules. This research proposes a deep learning based network intrusion detection framework designed to enhance the security of enterprise communication systems through intelligent and adaptive threat detection. The framework integrates advanced deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) architectures to analyze large-scale network traffic data and identify malicious activities in real time. The proposed model focuses on improving detection accuracy, minimizing false positives, and enabling rapid response to cyber threats. The framework also incorporates data preprocessing, feature extraction, anomaly detection, and continuous learning mechanisms to adapt to emerging attack patterns. Experimental evaluation using benchmark datasets demonstrates that the proposed system achieves superior performance compared to conventional machine learning and signature-based approaches. The study highlights the effectiveness of deep learning techniques in strengthening enterprise communication infrastructures and ensuring secure data transmission across modern digital networks

Article Information

Journal	International Journal of Emerging Trends in Engineering and Management Research
Volume (Issue)	Vol. 1 No. 1 (2016): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)
DOI	https://doi.org/10.15662/ijetemr.2016.0101001
Pages	1-13
Published	July 14, 2016
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Kiran Desai (%2016). Deep Learning Based Network Intrusion Detection Framework for Secure Enterprise Communication Systems. International Journal of Emerging Trends in Engineering and Management Research , Vol. 1 No. 1 (2016): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 1-13. https://doi.org/10.15662/ijetemr.2016.0101001

References

1. Aburomman, A. A., & Reaz, M. B. I. (2015). A novel SVM-kNN-PSO ensemble method for intrusion detection system. Applied Soft Computing, 38, 360–372.
2. Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
3. Cannady, J. (1998). Artificial neural networks for misuse detection. Proceedings of the National Information Systems Security Conference, 443–456.
4. Denning, D. E. (1987). An intrusion-detection model. IEEE Transactions on Software Engineering, SE-13(2), 222–232.
5. Ghosh, A. K., Schwartzbard, A., & Schatz, M. (1999). Learning program behavior profiles for intrusion detection. Workshop on Intrusion Detection and Network Monitoring, 1–13.
6. Kim, G., Lee, S., & Kim, S. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4), 1690–1700.
7. Kumar, S., & Spafford, E. H. (1994). A pattern matching model for misuse intrusion detection. Proceedings of the National Computer Security Conference, 11–21.
8. Lippmann, R., Haines, J. W., Fried, D. J., Korba, J., & Das, K. (2000). The 1999 DARPA off-line intrusion detection evaluation. Computer Networks, 34(4), 579–595.
9. Mukkamala, S., Sung, A. H., & Abraham, A. (2005). Intrusion detection using an ensemble of intelligent paradigms. Journal of Network and Computer Applications, 28(2), 167–182.
10. Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12), 3448–3470.
11. Peddabachigari, S., Abraham, A., Grosan, C., & Thomas, J. (2007). Modeling intrusion detection system using hybrid intelligent systems. Journal of Network and Computer Applications, 30(1), 114–132.
12. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy, 305–316.
13. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 dataset. Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, 1–6.
14. Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). Intrusion detection by machine learning: A review. Expert Systems with Applications, 36(10), 11994–12000.
15. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
16. Mathew, A., & Asari, V. K. (2012, August). Local histogram based descriptor for tracking in wide area imagery. In International Conference on Information Processing (pp. 119-128). Berlin, Heidelberg: Springer Berlin Heidelberg.
17. Murugeshwari, B., Sarukesi, K., & Jayakumar, C. (2010, March). An efficient method for knowledge hiding through database extension. In 2010 International Conference on Recent Trends in Information, Telecommunication and Computing (pp. 342-344). IEEE.
18. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
19. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
20. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
21. Kumar, J. (2013). Preservation of the Privacy for Multiple Custodian Systems with Rule Sharing. Journal of Computer Science.
22. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
23. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
24. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
25. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
26. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
27. Jagadeesh, S., & Soundappan, R. S. (2014). Survey on knowledge discovery in speech emotion detection. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4476-4481.
28. Murugeshwari, B., & Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
29. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
30. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
32. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta, 2(1), 21-27.
33. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
34. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
35. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
36. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160