Expanding Cybersecurity with Advanced Machine Learning

D Sirisha, Anjani Dedepya S, A Uthpala Devi, K Ram Tejesh, M Rohith Naidu and K M R Yaswanth Kumar

International Journal of Scientific Research in Artificial Intelligence and Machine Learning, 2026, vol. 2, issue 2, 26-32

Abstract: The increasing complexity of the cybersecurity landscape, driven by the unprecedented growth of digital connectivity and the proliferation of IoT devices, has exposed significant vulnerabilities in traditional security architectures. In the current work on “Cybersecurity Data Science: An Overview from Machine Learning Perspective”. The current work is a review work that focuses on providing a critical analysis of the contributions, exploring both the strengths and limitations of the approaches. Furthermore, advanced methodologies such as deep learning, federated learning, quantum cryptography, and blockchain offer superior efficacy in addressing the multifaceted challenges of modern cybersecurity. This work serves as a vital expansion of the original work, underscoring the necessity of evolving cybersecurity models to align with cyber threats' dynamic and increasingly sophisticated nature.

Keywords: Cybersecurity; Machine Learning in Cybersecurity; Cybersecurity Data Science (CDS); Advanced Machine Learning Algorithms; Deep Learning in Cybersecurity; Federated Learning; Quantum Cryptography; Blockchain Technology; Digital Threat Detection; Cyber Threats and IoT Security; AI-enhanced Malware Detection; Intrusion Detection Systems (IDS); Data-Driven Cybersecurity Solutions; Convolutional Neural Networks (CNNs); Recurrent Neural Networks (RNNs); Anomaly Detection; Privacy-Preserving Cybersecurity; Distributed Threat Detection; Real-Time Cybersecurity Systems; Quantum Key Distribution (QKD); Post-Quantum Cryptography; Zero Trust Architecture (ZTA); Blockchain for Identity Management; Smart Contracts in Cybersecurity; Decentralized Cybersecurity Solutions; Artificial Intelligence in Cybersecurity; Data-Driven Decision Making; Lattice-Based Cryptography; Autoencoders for Anomaly Detection; Collective Defense Model in Cybersecurity (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML26224
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