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@article{179346,
        author = {Nandini C N and Monisha Ganapati Moger and Janani C G and Manuprasad A},
        title = {Intrusion detection system using hybrid approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6519-6527},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179346},
        abstract = {In today’s rapidly evolving digital landscape, 
cloud computing has emerged as a cornerstone of modern 
infrastructure. However, the increasing adoption of cloud 
environments has also given rise to complex and 
sophisticated cyber threats. Traditional intrusion 
detection systems (IDS), primarily based on signature or 
rule-based methods, often fail to detect unknown or zero
day attacks, especially in dynamic cloud settings. This 
paper proposes an advanced Intrusion Detection System 
using Hybrid Machine Learning techniques to effectively 
detect and classify network intrusions in cloud 
environments. The proposed system leverages a combination of machine 
learning algorithms to analyze and classify network 
traffic, aiming to enhance detection accuracy and reduce 
false positives. A hybrid approach is employed by 
integrating various models—such as Decision Trees, 
Random Forest, and Support Vector Machines—after 
performing feature selection and data preprocessing. The 
system is trained and evaluated using benchmark 
intrusion detection datasets, where metrics such as 
accuracy, precision, recall, and F1-score are used to 
assess performance. 
Experimental results indicate that the hybrid model 
outperforms 
traditional 
single-algorithm 
systems, 
achieving higher detection rates and improved robustness 
against diverse attack types. Furthermore, the model 
demonstrates adaptability and scalability, making it well
suited for real-time deployment in cloud-based 
infrastructure. This research contributes to the 
development of intelligent and automated IDS solutions 
that can proactively safeguard cloud environments from 
emerging cybersecurity threats.},
        keywords = {Machine learning, intrusion detection,  Feature selection (FS)},
        month = {May},
        }