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Paper Title : DESIGN AND DEVELOP INTRUSION DETECTION SYSTEM FOR DETECTING AND CLASSIFYING CYBER ATTACKS AT NETWORK LEVEL USING DEEP LEARNING MODELS

ISSN : 2395-1303
Year of Publication : 2021
10.29126/23951303/IJET-V7I5P2

Authors: -Dr. D.J. Samatha Naidu , C. Shalini

         


Abstract
- Deep learning techniques are being widely used to develop an IDS (Intrusion Detection System) for detecting and classifying cyberattacks at the network level. In existing system many challenges raised since malicious attacks are continually changing and occurring in very large volumes requiring a scalable solution. In proposed work a hybrid intrusion detection alert system using a highly scalable framework on commodity hardware server which has the capability to analyze the network and host-level activities. The framework employed distributed deep learning model with CNN (convolutional neural network) and LSTM (Long short term memory) for handling and analyzing very large scale data in real-time. In addition, we collected host-based and network based features in real-time and employed the proposed CNN and LSTM models for detecting attacks and intrusions. In all the cases, we observed that CNN and LSTM exceeded in performance when compared to the classical machine learning classifiers . To the best of our knowledge this is the only framework which has the capability to collect network -level and host-level activities in a distributed manner using CNN and LSTM to detect attack more accurately.

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