A Modified Deep Neural Network Based Hybrid Intrusion Detection System in Cyber Security
Thupakula Bhaskar1, Tryambak Hiwarkar2, K. Ramanjaneyulu3

1Thupakula Bhaskar, Research-Scholar, Department of Computer Science and Engineering, Sri Satya Sai University of Technology & Medical Sciences, Bhopal (M. P.), India.
2Dr. Tryambak Hiwarkar, Professor, Department of Computer Science and Engineering, Sri Satya Sai University of Technology & Medical Sciences, Bhopal (M. P.), India.
3Dr. K. Ramanjaneyulu, Professor, Department of ECE, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada (A. P.), India.

Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 3366-3370 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7242068819/19©BEIESP
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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: In recent trends organizations are very much curious to protect data and prevent malware attack by using well flourished and excellent tools. Many algorithms are used for the intrusion detection system (IDS) and it has pros and cons. Here we introduced a new method of intrusion detection using Adaptive Jaya optimization (AJO) with modified deep neural network (MDNN) by hybrid optimization techniques such as Gravity search algorithm with gray wolf optimization (GSGW). In the proposed method modified deep neural network uses 4 hidden layers and has a low false alarm rate and a high detection rate. The performance evaluation is done by the feature selection in NSL-KDD dataset. In the proposed method the experimental result reveals less false alarm rate, better accuracy and high Detection when compared to previous analysis. This kind of IDS systems are used to develop extremely accurate in detecting and respond to malicious traffic/activities.
Keyword: Intrusion Detection System, Modified Deep Neural Network, Adaptive Jaya Optimization, and Gray Wolf Optimization.
Scope of the Article: Network Security Trust, & Privacy.