A Stacked CNN-BiLSTM Model with Majority Technique for Detecting the Intrusions in Network

Authors

  • Swati Mirlekar Assistant Professor, Department of Electronics & Communication Engineering, St. Vincent Pallotti College of Engineering & Technology,Nagpur, Maharashtra
  • Komal Prasad Kanojia Associate Professor, Department of Electronics & Communication Engineering, RKDF Institute of Science and Technology Bhopal, M.P
  • Bharti Chourasia HOD, Department of Electronics & Communication Engineering, RKDF Institute of Science and Technology Bhopal, M.P

Keywords:

intrusion detection, stacked CNN-biLSTM, SMOTE, NSL-KDD, UNSW-NB15

Abstract

The Internet of today is composed of almost 500,000 distinct networks. It is a challenging process to identify the attacks in every network connection according to the sorts of attacks they use since various attacks may have different connections, & the no. of attacks may range anywhere from a few to hundreds of network connections. Using a DNN (Deep Neural Network) technique to identify unknown attack packages is the primary objective of this research study. This will be accomplished by using an advanced Intrusion Detection System (IDS) that has excellent network performance. To conduct the assessment of metrics, UNSW-NB15 & NSL-KDD datasets are employed. This model makes use of LSTM & CNN to provide more accurate forecasts by concentrating more intently on the features of a successful earthquake. Initially, to reduce the quantity of noise in the majority group, we employ the One-Side Selection (OSS) technique. Then, to broaden the diversity of our samples, we employ the Synthetic Minority Oversampling Technique (SMOTE). This method of creating a balanced dataset dramatically reduces the amount of time required for training the model while allowing it to fully understand the characteristics of minority samples. Next, we use stacked CNN-biLSTM to extract spatial and temporal features, and then we use this information to build a deep stacked network model, which we stacked on top of one another. This proposed model can achieve remarkable accuracy in both datasets leaving a gap that is discussed at the end of the paper.

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Published

24.11.2023

How to Cite

Mirlekar, S. ., Kanojia, K. P. ., & Chourasia , B. . (2023). A Stacked CNN-BiLSTM Model with Majority Technique for Detecting the Intrusions in Network . International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 152–162. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3874

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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