Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Abstract: This research focus Traditional intrusion detection systems (IDS) frequently struggle to stay accurate and flexible as cyber threats grow more complex. In order to improve intrusion detection capabilities, this study presents a hybrid deep learning technique that combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. The model is trained and evaluated on popular datasets such as CICIDS2017 and NSL-KDD. Metrics like accuracy, precision, recall, and F1-score are used to assess its performance. The findings demonstrate that, in comparison to standalone models, this method produces higher detection rates and fewer false positives. This hybrid architecture offers a robust and scalable response to the current network security issues by utilizing both spatial and temporal data features.
Keywords:
Intrusion Detection System (IDS),Hybrid Deep Learning, CNN-LSTM, Network Security, Cyber Threat Detection
Cite Article:
"Enhancing Intrusion Detection Systems Using Hybrid Deep Learning Algorithms", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b814-b819, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505192.pdf
Downloads:
000356
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