• Utkoor Srikanth
• Sontireddy Chandana
• Thota Nithin
• D. Mounica

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber-attacks at the network-level and host-level in a timely and automatic manner. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. In this deep neural network (DNN), a type of deep learning model is explored to develop flexible and effective IDS to detect and classify unforeseen and unpredictable cyber-attacks. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyber-attacks.

DNN, IDS, SVM,

"Deep Learning Approach For Intelligent Intrusion detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b326-b329, June-2021, Available :http://www.jetir.org/papers/JETIR2106180.pdf

"Deep Learning Approach For Intelligent Intrusion detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb326-b329, June-2021, Available at : http://www.jetir.org/papers/JETIR2106180.pdf