A secure and intelligent framework for cyber threat detection using machine learning

Shukla, Ravi Shankar; Ather, Danish; Raj, Dharm; Jain, Abhishek; Sarkar, Tiyas; Rakhra, Manik

doi:10.47974/JDMSC-2752

Open Access Research Article

A secure and intelligent framework for cyber threat detection using machine learning

Ravi Shankar Shuklaravipraful@gmail.comDepartment of Computer ScienceCollege of Computing and InformaticsSaudi Electronic UniversityRiyadh, 11673, Saudi ArabiaView full profile → , Danish Atherdanishather@gmail.comDepartment of Artificial IntelligenceNamed After Muhammad Al-KhwarizmiTashkent University of Information TechnologiesTashkent, 100084, UzbekistanView full profile → , ^*Dharm RajCorresponding authordharmraj4u@gmail.comDepartment of Computer Science & EngineeringSharda School of Computing Science & EngineeringSharda UniversityNoida, Uttar Pradesh, 201310, IndiaView full profile → , Abhishek Jainajain.nit@gmail.comDepartment of Information TechnologyGuru Ghasidas VishwavidyalayaA Central UniversityBilaspur, Chhattisgarh, 495009, IndiaView full profile → , Tiyas Sarkarinfo.tiyasofficial11901657@gmail.comDepartment of Computer Science and EngineeringSchool of Computer Science and EngineeringLovely Professional UniversityPunjab, 144411, IndiaView full profile → , Manik Rakhrarakhramanik786@gmail.comDepartment of Computer Science and EngineeringSchool of Computer Science and EngineeringLovely Professional UniversityPunjab, 144411, IndiaView full profile →

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pp. 2441–2457Vol. 29Issue 6June 2026DOI: 10.47974/JDMSC-2752 Crossmark XML

Received:: 01 Nov 2025
Published Online:: 20 Jun 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2752
Pages:: 2441–2457

Abstract

The expanding interrelated digital systems across the board have significantly increased the number and advanced nature of cyber threats, thus making traditional rule-based security approaches inadequate. This paper proposes a secure and intelligent framework that is used for detecting cyber threats using machine learning techniques. The proposed framework supplements the above-mentioned techniques through a well-planned combination of data preprocessing, discriminative feature selection, ensemble-based supervised learning, and adaptive threat classification, which accurately identifies the malicious activities in the complex network environments. By taking advantage of machine learning models that were trained using benchmark datasets of intrusion detection tests, the framework was able to differentiate between normal and malicious traffic highly effectively, while minimizing false alarms. Experimental results show that the proposed method outperforms the conventional machine learning classifiers based on the results of accuracy, recall, F1-score, and false alarm rate. The framework further includes adaptability (periodic retraining) that facilitates resilience to changing patterns of threats. Owing to its modular design, scalability, and powerful detection capabilities, the proposed solution is suitable to be implemented in enterprise networks, cloud infrastructures, and other real world cybersecurity networks.

Keywords

Cyber security Machine learning Intrusion detection Threat analysis Network security

Subject Classifications

68M2568T07

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