Abstract
IDS play significant role in the computer network and system. Now a days, research on the intrusion detection that has been use of machine learning applications. This paper proposes novel deep learning technique to empower IDS functioning within current system. The system shows a merging of deep learning and machine learning, capable of accurate analyzing an inclusive range of network traffic. The new approach proposes NDAE for un-supervised feature learning. Moreover, additionally proposes novel deep learning classification display built utilizing stacked autoencoder. Our proposed classifier has been executed in GPU and assessed utilizing the measure using ‘KDD’ Cup ‘99’ and ‘NSL-KDD’ datasets. The performance evaluated network intrusion detection analysis datasets, particularly KDD Cup 99 and NSL-KDD dataset.
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Gulghane, S., Shingate, V., Bondgulwar, S., Awari, G., Sagar, P. (2020). A Survey on Intrusion Detection System Using Machine Learning Algorithms. In: Raj, J., Bashar, A., Ramson, S. (eds) Innovative Data Communication Technologies and Application. ICIDCA 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-030-38040-3_76
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DOI: https://doi.org/10.1007/978-3-030-38040-3_76
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