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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: February 2026
Volume 11 | Issue 2
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21318
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| Paper Title: | Recent Advances in Deep Learning for Intrusion Detection: A Review of Anomaly Detection Techniques |
| Authors Name: | Irshad Ali , Nitesh Gupta |
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| Author Reg. ID: |
IJRTI_208985
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| Published Paper Id: | IJRTI2601010 |
| Published In: | Volume 11 Issue 2, February-2026 |
| DOI: | |
| Abstract: | Anomaly detection is paramount to contemporary Intrusion Detection Systems (IDSs), which focus on determining atypical patterns in network traffic or system behavior that may indicate actual or potential threat situations. Traditional approaches often fail to work properly because cybersecurity tasks involve complicated and high-dimensional data. Deep learning (DL) algorithms have emerged as capable approaches to detect deep patterns within huge datasets with minimal human intervention. This review presents a comprehensive review of deep learning methods in IDS-based anomaly detection, with emphasis on methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). We analyze their performance in detecting intrusion under both known and unknown scenarios, highlighting main issues like data imbalance, model interpretability, and scalability. Additionally, we overview IDS benchmark datasets throughout recent years, contrasting the performance of DL techniques under various experimental settings. The paper also provides a brief overview of hybrid approaches that combine deep learning with other machine learning approaches to achieve better accuracy in detection. Finally, we explore future research domains and ongoing challenges, emphasizing the design of robust models capable of adapting to the dynamic nature of attack strategies in network environments. |
| Keywords: | Anomaly detection, Intrusion detection systems, Deep learning, Convolutional neural networks, Recurrent neural networks, Autoencoders, Generative adversarial networks, Cybersecurity. |
| Cite Article: | "Recent Advances in Deep Learning for Intrusion Detection: A Review of Anomaly Detection Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 2, page no.a62-a69, February-2026, Available :http://www.ijrti.org/papers/IJRTI2601010.pdf |
| Downloads: | 00051 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2601010
Registration ID:208985
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier):
Page No: a62-a69 Country: Bhopal, Madhya Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2601010 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2601010 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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