AI-DRIVEN THREAT DETECTION USING DATA SCIENCE: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS ON CYBERSECURITY DATASETS

Author: Praveen Kumar Reddy Gouni

ABSTRACT

They originate from the rapid rise of cyber threats such as malware, phishing, ransomware, denial of service, and unauthorised network intrusion, which have proven to be so difficult to tackle that traditional security measures can hardly deal with the issue. Signature-based intrusion detection system techniques in particular, which are commonly adopted by traditional methods, usually lack the ability to detect novel and evolving attack vectors in addition to high false positive rate and response time. In this regard, this paper proposes an AI threat detection framework, employing data science methods to boost cybersecurity performances. The researchers of this paper have tested the effectiveness of several models using a benchmark dataset for cyber security, including CICIDS2017 or NSL-KDD and machine learning techniques such as Random Forest, Support Vector Machine, Logistic Regression and XGBoost for evaluating performance. Using measures of accuracy, precision, recall and F1-score, the experiments show that the performance of ensemble learning models is higher than shallow learning models in this research; XGBoost and Random Forest.

Keywords: Artificial Intelligence, Cybersecurity, Threat Detection, Machine Learning, Intrusion Detection System, Data Science, XGBoost, Random Forest

REFERENCES

  1. Heidari, Arash, and Mohammad Ali Jabraeil Jamali. “Internet of Things intrusion detection systems: a comprehensive review and future directions.” Cluster Computing 26, no. 6 (2023): 3753-3780.
  2. Reddy, B. (2021). A Quantitative Analysis of Cloud Security Practices in IoT Environment (dissertation).
  3. Khader, Shuaib Abdul Khader, and Praveen Kumar Reddy Gouni Gouni. “Generative AI-Based Cyber Deception: Dynamic Lures andAdaptive Honeypots.” Journal of Cognitive Computing and Cybernetic Innovations 1, no. 3 (2025): 44-52.
  4. Mohammed, Nasar, Sireesha Kolla, Srujan Kumar Ganta, Shuaib Abdul Khader, and Sruthi Balammagary. “Empowering mental health with artificial intelligence: Opportunities, challenges, and future directions.”
  5. Kashif, M., & Ansari, A. A. (2026). Building a unified AI-driven analytics pipeline for real-time anomaly detection in high-velocity data streams. IJIREEICE, 14(1), 66–75. https://doi.org/10.17148/ijireeice.2026.14111
  6. Ansari, Meraj Farheen, and Syed Sharik Ali. “AI-driven zero-trust architecture for enhanced cybersecurity in dynamic network environments.” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering 13 (2025): 12.
  7. Chittoju, Siva Sai Ram, Sireesha Kolla, Mubashir Ali Ahmed, and Abdul Raheman Mohammed. “Synergistic Integration of Blockchain and Artificial Intelligence for Robust IoT and Critical Infrastructure Security.”
  8. Khader, Shuaib Abdul, Amir Ahmed Ansari, and Syed Sharik Ali. “Zero-Day Exploit Prediction Using Graph-Based Deep Learning on Vulnerability and Threat Intelligence Data.”
  9. Ansari, Mohammed Azmath, Shaik Aqheel Pasha, and Narendar Kandula. “Reinforcement Learning for Adaptive Network Defense in Financial Institutions.”
  10. Chittoju, S. R., and Siraj Farheen Ansari. “Blockchain’s Evolution in Financial Services: Enhancing Security, Transparency, and Operational Efficiency.” International Journal of Advanced Research in Computer and Communication Engineering 13, no. 12 (2024): 1-5.
  11. Kashif, Mohammed, Mohammed Aasimuddin, Mubashir Ali Ahmed, Laxmi Bhavani Cheekatimalla, Eraj Farheen Ansari, and Ahwan Mishra. “AI-DRIVEN CTI FOR BUSINESS: EMERGING THREATS, ATTACK STRATEGIES, AND DEFENSIVE MEASURES.”
  12. RAHEEM, MOHD ABDUL, and MOHAMMED AZMATH ANSARI. “INTELLIGENT AND TRUSTWORTHY 6G: AI-DRIVEN ARCHITECTURES, APPLICATIONS, AND SECURITY FRAMEWORKS.
  13. Mohammed, Naveed Uddin, Zubair Ahmed Mohammed, Shravan Kumar Reddy Gunda, Akheel Mohammed, and Moin Uddin Khaja. “Networking with AI: Optimizing Network Planning, Management, and Security through the medium of Artificial Intelligence.
  14. Sultana, Ghousia, Siraj Farheen Ansari, Mohammed Imran Ahmed, Abdul Faiyaz Shaik, Moin Uddin Khaja, and Bibhu Dash. “RESPONSIBLE AI ANALYTICS FOR REAL-WORLD IMPACT: NAVIGATING ETHICS, PRIVACY AND TRUST.”
  15. Integrating Machine Learning and Engineering Management to Optimize Construction Scheduling and Resource Allocation
  16. Mohammed, Abdul Faisal, and Mohammed Akifuddin Ghori. “AI-Enhanced Safety for Heavy Load Construction Vehicles: An Integrated Embedded C++ Software Approach.”
  17. KASHIF, MOHAMMED, ABDUL RAHMAN JIBRAN SYED, and MUBASHIR ALI AHMED. “ADVANCING ANOMALY AND FRAUD DETECTION IN BIG DATA WITH ARTIFICIAL INTELLIGENCE.”
  18. Wang, Zheng-Xin, Dan-Dan Li, and Hong-Hao Zheng. “Model comparison of GM (1, 1) and DGM (1, 1) based on Monte-Carlo simulation.” Physica A: Statistical Mechanics and Its Applications 542 (2020): 123341.
  19. Mohammed, Akheel, Zubair Ahmed Mohammed, Naveed Uddin Mohammed, Shravan Kumar Gunda, Mohammed Azmath Ansari, and Mohd Abdul Raheem. “AI-NATIVE WIRELESS NETWORKS: TRANSFORMING CONNECTIVITY, EFFICIENCY, AND AUTONOMY FOR 5G/6G AND BEYOND