Anomaly Based Intrusion Detection System Using Neural Network
DOI:
https://doi.org/10.61841/kc3crh30Keywords:
Intrusion Detection, Neural Networks, Cyber Security, Machine Learning, Deep Learning.Abstract
Intrusion detection system (or IDS) is an integral part of any Information and Communication Technology (or ICT) system. Building an efficient and reliable IDS that accurately detects an attempt to compromise the network using some known or unknown vulnerability in real time is still a huge challenge. We attempt to create a state-of-the-art Deep Neural Network that analyses the network traffic in real time, identifies an attempt to compromise a network, classifies the type of attack and then compare its accuracy and efficiency with that of conservative and existing models.
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References
1. Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey by Elike Hodo, Xavier Bellekens, Andrew Hamilton, Christos Tachtatzis and Robert Atkinson.
2. Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security by Rahul Vigneswaran K, Vinayakumar R, Soman KP and Prabaharan Poornachandran
3. Feature Extraction Methods for Intrusion Detection Systems by Hai Thanh Nguyen, Katrin Franke, Slobodan Petrović
4. Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS) by Bisyron Wahyudi Masduki, Kalamullah Ramli, Ferry Astika Saputra, Dedy Sugiarto
5. A Comparative Analysis of SVM and its Stacking with other Classification Algorithm for Intrusion Detection by Nanak Chand, Preeti Mishra, C. Rama Krishna, Emmanuel Shubhakar Pilli and Mahesh Chandra Govil
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