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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Improving Accuracy in Cyber Attack Classification: A Comprehensive Review for Network Security |
| Authors Name: | RINKI , Dr. Reena |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_207809
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| Published Paper Id: | IJRTI2512060 |
| Published In: | Volume 10 Issue 12, December-2025 |
| DOI: | |
| Abstract: | Abstract:As the digital communication systems and interconnected networks grow exponentially, the need to enforce a robust network security has become a worldwide issue of concern. The conventional intrusion detection systems (IDS) are not very effective in detecting complicated and dynamic cyber threats because of their lack of proper feature extraction and classification accuracy. In this paper, the author has provided a review and implementation framework that would improve the accuracy and efficiency of cyber attacks classification. The paper discusses the different methods of machine learning (ML) and Deep Learning (DL), including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Autoencoders and Ensemble Learning, and how each has its weaknesses and strengths in network intrusion detection. It is suggested to implement a hybrid deep learning-ensemble system, that is, the advantages of CNN to extract spatial features and LSTM to learn temporal sequences combined with ensemble decision fusion to reduce false positives. The conducted experimental analysis of benchmark datasets such as NSL-KDD, CIC-IDS2017, and IoT Network Dataset illustrates the impressive detection performance with the accuracy of up to 99.1 and a smaller false alarm rate. The findings support the fact that hybrid intelligent systems may substantially enhance the accuracy, scalability, and resilience of network intrusion detection systems to develop a more resilient and resilient cyber ecosystem. |
| Keywords: | Keyword: Cybersecurity; Intrusion Detection System (IDS); Machine Learning; Deep Learning; Hybrid Model; CNN-LSTM; Ensemble Learning; Network Security; Cyber Attack Classification; Artificial Intelligence (AI) |
| Cite Article: | "Improving Accuracy in Cyber Attack Classification: A Comprehensive Review for Network Security", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.a481-a494, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512060.pdf |
| Downloads: | 000172 |
| 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: IJRTI2512060
Registration ID:207809
Published In: Volume 10 Issue 12, December-2025
DOI (Digital Object Identifier):
Page No: a481-a494 Country: Jind, Haryana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2512060 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2512060 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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