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@article{148476,
        author = {Mehnaz Anjum and Dr. Shreedhara K S},
        title = {Performance Analysis of Semi-Supervised Machine Learning Approach for DDoS Detection },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {6},
        number = {2},
        pages = {144-147},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=148476},
        abstract = {DDoS – Distributed daniel of service is one of the cyber-attack, which remains as a major attack on internet for past many years. DDoS detection based on Machine Learning techniques such as, Supervised and Unsupervised techniques has been already implemented which has some drawbacks like low detection accuracy and high false positive rates. In this paper, DDoS detection based on Semi-Supervised Machine learning technique is presented which is the combination of both supervised and unsupervised techniques that provides better results compared to the existing approaches. Unsupervised part consists of some estimation steps including clustering which reduces the false positive rates and increases the accuracy by reducing irrelevant data. In supervised part Random forest algorithm is used to accurately classify the DDoS attack data and it also reduces the false positive rate of unsupervised part.},
        keywords = {Semi-Supervised, Clustering, Random forest.},
        month = {},
        }