A Neural Network Model for Attacker Detection using GRU and Modified Kernel of SVM
Sharfuddin Waseem Mohammed1, C Madan Kumar2, Narasimha Reddy Soora3
1Sharfuddin Waseem Mohammed, Department of Computer Science Engineering, Kakatiya Institute of Technology & Sciences, Warangal, India.
2C Madan Kumar, Department of Computer Science Engineering, Kakatiya Institute of Technology & Sciences, Warangal, India.
3Narasimha Reddy Soora, Department of Computer Science Engineering, Kakatiya Institute of Technology & Sciences, Warangal, India.
Manuscript received on 10 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 4441-4444 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3337078219/19©BEIESP | DOI: 10.35940/ijrte.B3337.078219
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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: Over past few decades neural network changed the way of traditional computing many different models has proposed depending upon data intensity, predictions, and recognition and so on. Among which Gated Recurrent Unit (GRU) is created for variety of long short-term memory (LSTM) unit, which is part of recurrent neural network (RNN). These models proved to be dominant for range of machine learning job such as predictions, speech recognition, sentiment analysis and natural language processing. In this proposed model, a support vector machine (SVM) with modified kernel as final output layer for prediction is used instead of traditional approach of SoftMax and log loss function is used to calculate the loss. Proposed technique is applied for binary classification for intrusion detection using honeypot dataset (2013) network traffic sequence of Kyoto University. Results shows a prominent change in training efficiency of ≈89.45% and testing efficiency of ≈88.15% when compared with softmax output layer. We can conclude that linear SVM with modified kernel as output layer outperform compared with SoftMax in prediction time.
Keywords: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Recurrent Neural Network (RNN)
Scope of the Article: Machine Learning