Predictive Modelling for Network Threat Detection Using Artificial Intelligence Techniques

Abstract

The advent of artificial intelligence (AI) techniques has revolutionized network security by enabling predictive modelling for threat detection. This abstract proposes a novel approach to enhancing network security through predictive modelling, leveraging advanced AI techniques. By analyzing vast amounts of network traffic data, AI algorithms can identify patterns indicative of potential threats, including malware, intrusions, and anomalous activities. The predictive models developed through this approach can forecast potential network vulnerabilities and pre-emptively detect emerging threats before they manifest into security breaches. This proactive stance empowers organizations to fortify their network defenses, minimize the risk of cyberattacks, and safeguard sensitive information. Through the fusion of AI and predictive modelling, this research endeavors to pave the way for more robust and resilient network security frameworks in an increasingly interconnected digital landscape. Future improvements will focus on incorporating AI-driven adaptive security mechanisms that can evolve with emerging cyber threats. With the increasing reliance on digital platforms, this study highlights the urgent need for a comprehensive cybersecurity framework to safeguard business websites. The project not only presents a novel security solution but also provides insights into best practices for website protection, ensuring a safer digital environment.

Introduction

With the rapid growth of interconnected digital systems, cyber threats have become more advanced and dynamic, outpacing traditional signature-based detection methods. This gap has led to the adoption of AI-driven predictive modeling techniques that can proactively identify potential threats and anomalies in network traffic before serious breaches occur. AI and machine learning enable systems to learn from large volumes of data, detect subtle malicious patterns, and adapt to new and unknown attacks in real time, creating smarter, self-learning cybersecurity defenses.

The text also explains foundational concepts:

• Data Science: An interdisciplinary field combining domain knowledge, programming, and statistical modeling to extract insights from data, aiding strategic decision-making.

• Artificial Intelligence (AI): The simulation of human intelligence by machines, involving learning, problem-solving, and perception, with applications ranging from natural language processing to autonomous vehicles.

• Machine Learning (ML): A subset of AI focused on enabling computers to learn from data and improve over time without explicit programming, including supervised, unsupervised, and reinforcement learning.

The literature review highlights recent advances in AI and ML applied to cybersecurity, such as malware analysis, sentiment classification, PDF malware detection, optimization algorithms for financial predictions, and various machine learning models improving threat detection accuracy.

The proposed work aims to develop a predictive AI-based network security system that continuously monitors network traffic, detects anomalies, and adapts through deep learning to evolving threats. This proactive approach seeks to enhance response speed and strengthen cybersecurity defenses, helping organizations maintain secure and reliable network operations.

Conclusion

Our network threat detection predictive modelling strategy is based on the principles of sophisticated artificial intelligence methods. Utilizing machine learning algorithms that can scan large datasets, the system has the capability to detect patterns, anomalies, and unusual behaviors that could be potential signs of cyber threats. Through this data-driven approach, proactive cybersecurity is possible, going beyond the conventional reactive measures that heavily depend on known threat signatures and human intervention. The use of AI facilitates real-time processing of data and threat detection, which is essential in the current rapid-paced digital landscape. Since threats can arise and propagate within a matter of seconds, responding quickly while being able to detect them is essential. Our platform continuously scans network traffic, raising red flags on suspicious activities and initiating proper alerts or responses automatically. This essentially shortens response time, limiting possible losses and providing organizations with a vital advantage in counteracting attacks. Another key benefit of our AI-based system is its ongoing flexibility. In contrast to rigid rule-based systems, our model improves by learning from new information and previous events, becoming more accurate with time. This flexibility makes the system continue to be effective even as cyber threats become increasingly sophisticated and advanced. It is also able to adapt to various network environments and threat profiles, thus being a scalable and versatile solution for numerous use cases. Finally, this smart threat detection system provides a strong and resilient method for ensuring network integrity. By improving detection accuracy, speeding up response times, and learning from the constantly evolving cyber environment, the system offers a reliable definition against unauthorized access, data breaches, and other security threats. Not only does it protect sensitive data, but it also facilitates long-term cybersecurity strategies for organizations looking to remain ahead of cyber attackers.

References

[1] Abusitta, A., Li, M. Q., & Fung, B. C. M. (2021). Malware classification and composition analysis: A survey of recent developments. Journal of Information Security.and.Applications,59,102828. https://doi.org/10.1016/j.jisa.2021.102828 [2] R. Bharathi, R. Bhavani, and R. Priya, Twitter text sentiment analysis of Amazon unlocked mobile reviews using supervised learning techniques, Indian J. Compute. Sci. Eng., vol. 13, no. 4, pp. 1242-1251, 2022. [Online]. Available: https://www.ijcse.com/docs/INDJCS E22-13-04-100.pdf [3] Habibi,O.,Chemmakha,M.–& Lazaar, M. Performance Evaluation of CNN and Pre-trained Models for Malware Classification. Arab J Sci Eng 48,10355–10369,(2023). https://doi.org/10.1007/s13369-023-07608-z [4] R. Bharathi, R. Bhavani, and R. Priya, Leveraging deep learning with sentiment analysis for Online Book reviews polarity classification model, Multimed. Tools Appl., 2024. Available: https://doi.org/10.1007/s11042-024-20369-7. [5] Pachpute, S. S. (2019). Malware Analysis on PDF. Master\'s Projects. San Jose State University. https://doi.org/10.31979/etd.pf8d-htjh. [6] R. Bharathi, \"Study of Comparison between Bat Algorithm, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) for user\'s bank loan and their related due history,\" International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), vol. 3, issue,5,pp.1168-1176,May-June,2018. https://ijsrcseit.com/home/issue/view/article.ph p?id=CSEIT1835264 [7] Atadoga, A., Sodiya, E. O., Umoga, U. J., & Amoo, O. O. (2024). A comprehensive review of machine learning\'s role in enhancing network security and threat detection. World Journal of Advanced Research and Reviews, 21(02), 877–886. https://doi.org/10.30574/wjarr.2024.21.2.0501 [8] Nguyen, T. T., & Reddi, V. J. (2021). Deep Reinforcement Learning for Cyber Security. IEEE Transactions on Neural Networks and Learning Systems,32(11),5239–5253. https://doi.org/10.1109/TNNLS.2021.3121870 [9] Okoli, U. I., Obi, O. C., Adewusi, A. O., & Abrahams, T. O. (2024). Machine learning in cybersecurity: A review of threat detection and defense mechanisms. World Journal of Advanced Research and Reviews, 21(01), 2286–2295. https://doi.org/10.30574/wjarr.2024.21.1.0315 [10] R. Bharathi, R. Bhavani, & R. Priya. “Leveraging Deep Learning with Sentiment Analysis for Online Book Reviews Polarity Classification Model”, Multimedia Tools and Applications, 17 October 2024, pp,1-20.DOI:https://doi.org/10.1007/s11042-024-20369-7 [11] Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41-50. https://doi.org/10.1109/TETCI.2017.2772792 [12] Bharadiya J. Machine learning in cybersecurity: Techniques and challenges. Eur J Technol. 2023;7(2):1–14. [13] Dasgupta D, Akhtar Z, Sen S. Machine learning in cybersecurity: A comprehensive survey. J Def Model Simul. 2022;19(1):57–106. [14] National Institute of Standards and Technology (NIST). Framework for improving critical infrastructure cybersecurity. [Internet]. Available from: https://www.nist.gov/publications/frameworkimproving-critical-infrastructurecybersecurity-version-11 [15] IBM Security. AI cybersecurity. IBM Study; 2024.Availablefrom: https://www.ibm.com/security/artificialintelligence [16] Microsoft. Microsoft cloud security for enterprise architects. [Internet]. Available from: https://download.microsoft.com/download/6/d/f/6dfd7614-bbcf-4572-a871-e446b8cf5d79/msft_cloud_architecture_security.pdf [17] Cisco Security. Annual cybersecurity report. [Internet]. Available from: https://engage2demand.cisco.com/LP=9810 [18] Google Cloud. Security & identity. Google Cloud; 2024. [Internet]. Available from: https://cloud.google.com/blog/products/identity-security [19] Amazon Web Services (AWS). AWS cloud security. [Internet]. Available from: https://aws.amazon.com/security [20] CloudFlare. Trends report: State of application security in 2024. [Internet]. Available from: https://www.cloudflare.com/en-in/2024-application-security-trends [21] Symantec. Enterprise security. Broadcom Software; 2024. [Internet]. Available from: https://www.broadcom.com/solutions/enterprise-security [22] Altulaihan EA, Alismail A, Frikha M. A survey on web application penetration testing. Electronics. 2023;12:1229. [23] Sadqi Y, Maleh Y. A systematic review and taxonomy of web applications threats. Inf Secur J Glob Perspect. 2022;31:1–27. [24] Otuu, Obinna Ogbonnia, and Felix ChukwumaAguboshim. \"Aguide to the methodology and system analysis section of a computer science project.\" World Journal ofAdvanced Research and Reviews 19.2 (2023): 322-339 [25] Spring, Jonathan M., et al. \"Machine learning in cybersecurity: A Guide.\" SEI- CMU Technical Report 5 (2019). [26] Alhamed M, Rahman MMH. A systematic literature review on penetration testing in networks: Future study directions. Appl Sci. 2023;13:6986. [27] Sarker, Iqbal H. \"Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects.\" Annals of Data Science 10.6 (2023): 1473-1498. [28] Makino Y, Klyuev V. Evaluation of web vulnerability scanners. In: 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems; 2015 Sep 24–26; Warsaw, Poland. Vol. 1. p. 399–402. [29] Liu, Jing, Yang Xiao, Shuhui Li, Wei Liang, and C. L. Philip Chen. “Cyber Security and Privacy Issues in Smart Grids.” IEEE Communications Surveys and Tutorials/IEEE Communications Surveys and Tutorials 14, no. 4 (January 1, 2012):981–97. https://doi.org/10.1109/surv.2011.122111.00145. [30] F Kagorora, Li J, D Hanyurwimfura, L Camara Effectiveness of web application security scanners at detecting vulnerabilities behind AJAX/JSON. Int J Innov Res Sci Eng Technol. 2015;4:4179–88. [31] Singh N, Meherhomji V, Chandavarkar BR. Automated versus manual approach of web application penetration testing. In: 2020 11th Int Conf on Computing, Communication and Networking Technologies (ICCCNT); 2020 Jul 1–3; Kharagpur, India. p. 1–6. [32] Hu Z, Beuran R, Tan Y. Automated penetration testing using deep reinforcement learning. In: 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW); 2020 Sep 7–11; Genoa, Italy. p. 2–10. [33] Hance J, Milbrath J, Ross N, Straub J. Distributed attack deployment capability for modern automated penetration testing. Computers. 2022;11:33. [34] Elmrabit N, Zhou F, Li F, Zhou H. Evaluation of machine learning algorithms for anomaly detection. In: 2020 Int Conf on Cyber Security and Protection of Digital Services; 2020 Jun 15–19; Dublin, Ireland. p. 1–8.

Copyright

Copyright © 2025 Dr. R. Bharathi, Asha P V, M S Arjun Dev, M A Navin Raj. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.