Work

AI-Driven Cybersecurity: Building Adaptive Threat Detection Systems Using Deep Learning

Year: 2025

Type: article

Abstract: This paper explores the development of adaptive threat detection systems in cybersecurity by leveraging deep learning techniques. It investigates the integration of AI-driven models capable of dynamically identifying and responding to evolving cyber threats with enhanced accuracy and speed. Emphasizing the challenges posed by complex, rapidly changing attack patterns, this study evaluates advanced neural architectures and model interpretability to build robust, real-time detection frameworks. The findings demonstrate significant potential for deep learning to transform cybersecurity defenses through continuous adaptation and intelligent threat assessment.

Source: International Journal of Science and Research Archive

DOI: 10.5281/zenodo.17614609

Authors: KABIR, EFAZ, Bhuiyan, Md Nyem Hasan, Datta, Samya, Shubhankar, Mandal, Bin Habib, Mohammad Quayes

Institutions: East West University, Dhaka International University, Leading University, Xi'an Jiaotong University, Daffodil International University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7105813596

FWCI:

Cites:

Cited by:

Related to: