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@article{156173,
        author = {Nayan kumar V and Punith kumar M and Tejas Kumar K and Vinod Kumar S and Dr. Aruna M.G},
        title = {Efficient Intrusion Detection of Imbalenced Network Traffic using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {1048-1054},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156173},
        abstract = {In imbalanced network traffic, malicious cyber-attacks can often hide in large amounts of normal data. At Cyberspace, using a high level of encryption and making it difficult for NIDS to ensure accuracy and timeliness. Despite decades of development, IDSs still face challenges in improving in detection accuracy. Deep Learning is a branch of Machine learning, whose performance is remarkable and as a hotspot in field of research.This paper involves both machine learning and Deep learning for intrusion detection in imbalanced network traffic.this process a DSSTE algorithm to avoid imbalance problems.initially the training set is preprocessed to modify imbalanced data and features are extracted. Then newly obtained training set is fed to a various classification models to evaluate that proposed DSSTE algorithm outperforms other methods.},
        keywords = {RandomForest, Alxenett, Lstm, DSSTE algorithm},
        month = {},
        }