Adaptive machine learning system for cybersecurity threats

Narooka, Preeti; Arora, Suchita; Srivastava, Satyajee; Tiwari, Mohit; Yadav, Sanjay

doi:10.47974/JDMSC-2161

Open Access Research Article

Adaptive machine learning system for cybersecurity threats

^*Preeti NarookaCorresponding authorpreeti.narooka@jaipur.manipal.edu preeti.narooka@gmail.comDepartment of Artificial Intelligence and Machine LearningManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Suchita Arorasuchita.arora1@poornima.edu.inDepartment of Computer Science and EngineeringPoornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , Satyajee Srivastavadrsatyajee@gmail.comSchool of Engineering and Technology (SET)Manav Rachna International Institute of Research and StudiesFaridabad, Haryana, 121007, IndiaView full profile → , Mohit Tiwarimohit.tiwari@bharatividyapeeth.eduDepartment of Computer Science and EngineeringA-4 Rohtak RoadBharati Vidyapeeth’s College of EngineeringPaschim Vihar, Delhi, 110063, IndiaView full profile → , Sanjay Yadavsanjay.yadav@alliance.edu.inDepartment of Pure and Applied MathematicsAlliance School of SciencesAlliance UniversityBangalore, Karnataka, 562106, IndiaView full profile →

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pp. 1611–1620Vol. 28Issue 5-AAugust 2025DOI: 10.47974/JDMSC-2161 Crossmark XML

Received:: 05 Nov 2024
Published Online:: 30 Aug 2025
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2161
Pages:: 1611–1620

Abstract

With the proliferation of digital technologies, cybercrime has become a pervasive system where end users, government users and business industries worldwide. Addressing this complex challenge requires innovative approaches that leverage different technologies like machine learning and data science. This abstract presents a machine learning-based computational system designed to combat cybercrime offenses effectively. The proposed system integrates machine learning algorithms with computational systems that find a lot of information and its similar patterns which are the ways of cybercrimes. By leveraging supervised, unsupervised, and reinforcement learning methods, the system can detect anomalies, classify malicious behavior, and predict potential cyber threats in real-time. Cybercrime has become a significant concern in the modern system where most of the users are using digital devices which causes the identity theft to financial fraud. Traditional methods of combating cybercrime are often reactive and fall short in finding the rapidly evolving system of cyber threats. In response, there is a growing interest in developing proactive and intelligent systems to detect, prevent, and mitigate cybercrime offenses. This paper presents a machine learning-based computational system for controlling cybercrime offenses. By seeing the information in the data set it starts learning itself and adapting to emerging threats, the system can effectively detect and reaction on the cyber threats active in the real environment.

Keywords

Machine learning Cybercrime offenses Cybersecurity Cyber threats Pattern recognition Cybercrime detection methods

Subject Classifications

Primary 93A30Secondary 49K15

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