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@article{175446,
        author = {Ms. Vatsalya and Dr Asim Kumar and Dr Anurag Jain},
        title = {Anomaly Intrusion Detection using MCA: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3306-3313},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175446},
        abstract = {As the complexity and size of computer networks continues to grow, securing these systems becomes increasingly difficult. Intrusion detection plays an important role in identifying and mitigating threats. This article provides a brief review of vulnerability detection techniques, focusing on their benefits, advantages, and limitations. This review covers a variety of methods for detecting anomalies, including a combination of statistical and machine learning-based approaches. Each technology is evaluated on its ability to detect previously unseen patterns and deviations from normal behavior; This makes them essential for identifying new and complex cyber threats. This article discusses the importance of feature selection and extraction in improving the performance of visual search algorithms. It explores various models of alternative attack strategies and challenges associated with mitigating vulnerability. Additionally, the review highlights the importance of real-time performance and large-scale capacity building for wide area networks. This article also discusses the role of context-sensitive information in improving the accuracy of search engine retrieval. He is exploring the integration of communication data, such as user behavior and network topology, to improve the detection capabilities of these systems. The purpose of this brief review is to provide an overview of the current state of fashion. Access to research technology, progress, trends and future research areas are discussed. By understanding the strengths and limitations of current approaches, researchers and practitioners can make informed decisions when designing efficient and effective intrusion prevention devices against changing cyber threats.},
        keywords = {Terms—Intrusion detection system (IDS), Signature based intrusion detection system (S-IDS), Anomaly based intrusion detection system (A-IDS),Machine Learning based detection (ML-IDS), Knowledge based detection (K-IDS), Data Mining based detection (DM-IDS),Statistical Anomaly based detection (SA-IDS), Multi classifier approaches (MCA),Adaptive and Scalable intrusion detection scheme (ASIDS).},
        month = {April},
        }