Real-time cyber threats and unauthorized access detection using embedded AI

Praveen, K. N. Raja; Yadav, Durga Prasad; Yenkikar, Anuradha V.; Kumar, Jambi Ratna Raja; Srinivasulu, Senduru; Desai, Smita

doi:10.47974/JDMSC-2647

Open Access Research Article

Real-time cyber threats and unauthorized access detection using embedded AI

^*K. N. Raja PraveenCorresponding authorp.raja@Jainuniversity.ac.inDepartment of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University)Ramanagara District, Karnataka, 562112, IndiaView full profile → , Durga Prasad Yadavdurga.prasad@niu.edu.inSchool of Engineering & Technology Noida International UniversityNoida, Uttar Pradesh, 203201, IndiaView full profile → , Anuradha V. Yenkikaranuradha.yenkikar@viit.ac.inDepartment of Artificial Intelligence Vishwakarma Institute of Information TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Jambi Ratna Raja Kumarratnaraj.jambi@gmail.comDepartment of Computer Engineering Genba Sopanrao Moze College of EngineeringPune, Maharashtra, 411045, IndiaView full profile → , Senduru Srinivasulusendurusrinivasulu.it@sathyabama.ac.inDepartment of Information Technology Sathyabama Institute of Science and Technology Chennai, Tamil Nadu, 600119, IndiaView full profile → , Smita Desaismita.desai@dypvp.edu.inDepartment of Electronics and telecommunication Dr. D. Y. Patil Institute of Technology PimpriPune, Maharashtra, 411018, IndiaView full profile →

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pp. 1077–1085Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2647 Crossmark XML

Received:: 07 May 2025
Published Online:: 11 Feb 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2647
Pages:: 1077–1085

Abstract

Because online dangers are getting smarter, we need more advanced ways to find and stop them in real time. The idea in this study is to use integrated AI to find harmful behaviour and unauthorized entry in computer environments with limited resources. The method combines preparation and normalization methods with the best training methods for lightweight AI models. This makes it possible to find anomalies quickly without slowing down the device. A statistical approach is given that turns risk detection into a classification problem and gives numbers to “anomaly scores” to make them more accurate. The model’s ability to provide low-latency, high-precision recognition makes it a good choice for IoT, edge, and integrated defence apps.

Keywords

Cybersecurity Embedded AI Real-time threat detection Unauthorized access Anomaly scoring

Subject Classifications

68M2568M15

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