Network Intrusion Detection using a Deep Learning Approach
V. V. Mandhare1, D. R Pede2, P. S. Vikhe3
1V. V. Mandhare*, Associate Professor, Department of Computer Engineering, Pravara Rural Engineering College, Loni, Rahata, Ahmednagar, India.
2D. R. Pede, Department of Computer Engineering, Pravara Rural Engineering College, Loni, Rahata, Ahmednagar, India.
3P. S. Vikhe, Associate Professor, Department of Instrumentation and Control Engineering, Pravara Rural Engineering College, Loni, Rahata, Ahmednagar, India.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 59-64 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.B4086079220 | DOI: 10.35940/ijrte.B4086.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: At present situation network communication is at high risk for external and internal attacks due to large number of applications in various fields. The network traffic can be monitored to determine abnormality for software or hardware security mechanism in the network using Intrusion Detection System (IDS). As attackers always change their techniques of attack and find alternative attack methods, IDS must also evolve in response by adopting more sophisticated methods of detection .The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including ID. Deep Learning (DL) is a subgroup of Machine Learning (ML) which is hinged on data description. The new model based on deep learning is presented in this research work to activate operation of IDS from modern networks. Model depicts combination of deep learning and machine learning, having capacity of wide range accurate analysis of traffic network. The new approach proposes non-symmetric deep auto encoder (NDAE) for learning the features in unsupervised manner. Furthermore, classification model is constructed using stacked NDAEs for classification. The performance is evaluated using a network intrusion detection analysis dataset, particularly the WSN Trace dataset. The contribution work is to implement advanced deep learning algorithm consists IDS use, which are efficient in taking instant measures in order to stop or minimize the malicious actions.
Keywords: Intrusion Detection System (IDS), Non- Symmetric Deep Auto-Encoder (NDAE), Deep Learning (DL), WSN Trace, Machine Learning (ML).