Deep Learning for Cybersecurity: Recent Advances in Threat Detection and Attack Mitigation- A brief Review

Abstract: Cybersecurity has turn out to be one of the most critical challenges of the digital technology, as evolving assault vectors together with zero-day exploits, polymorphic malware, phishing, and IoT-based intrusions keep bypassing conventional protection systems. Deep getting to know (DL), a subset of artificial intelligence, has emerged as a effective paradigm able to learn complex styles, reading massive-scale information streams, and adapting to new chance environments with unprecedented accuracy. The motive of this evaluate is to systematically have a look at current advances in DL-primarily based cybersecurity, with a focal point on threat detection and assault mitigation across a couple of domains. This study is enormous because it consolidates fragmented studies, highlights current gaps, and identifies answers that contribute to the development of resilient cybersecurity infrastructures. A established literature evaluation of 25 peer-reviewed studies (2014–2025) became performed, synthesizing findings across application areas such as intrusion detection systems (IDS), malware analysis, phishing and fraud detection, IoT/cloud protection, opposed robustness, and go-domain employer applications. The findings show that DL models—mainly CNNs, RNNs, LSTMs, and hybrid variants—always outperform classical gadget gaining knowledge of techniques in detection accuracy and adaptableness. however, challenges which include dataset barriers, adverse vulnerability, scalability troubles, and absence of explainability persist. This assessment contributes by identifying interpretable DL, adversarially sturdy models, and federated learning as promising research guidelines. The practical implication is a shift from reactive defenses in the direction of proactive, scalable, and adaptive cybersecurity frameworks that ensure resilience in an increasing number of complicated digital ecosystems. Keywords: Deep Learning; Cybersecurity; Intrusion Detection; Malware Detection; IoT Security; Adversarial Robustness. Title: Deep Learning for Cybersecurity: Recent Advances in Threat Detection and Attack Mitigation- A brief Review Author: Ifteker Hossen Rakib, MD RAJU International Journal of Novel Research in Computer Science and Software Engineering ISSN 2394-7314 Vol. 12, Issue 2, May 2025 - August 2025 Page No: 72-81 Novelty Journals Website: www.noveltyjournals.com Published Date: 25-August-2025 DOI: https://doi.org/10.5281/zenodo.16939721 Paper Download Link (Source) https://www.noveltyjournals.com/upload/paper/Deep%20Learning%20for%20Cybersecurity-25082025-3.pdf