A Deep Learning Framework for Intrusion Detection and Multimodal Biometric Image Authentication

Authors

  • M. Gayathri Department of Computer Science and Engineering, S.R.M. Institute of Science and Technology, Kattankulathur campus, Chennai, India https://orcid.org/0000-0001-7405-953X
  • C. Malathy Department of Computer Science and Engineering, S.R.M. Institute of Science and Technology, Kattankulathur campus, Chennai, India https://orcid.org/0000-0003-1974-8927

DOI:

https://doi.org/10.13052/jmm1550-4646.18212

Keywords:

Authentication, Deep learning, Recurrent neural network, Multimodal, biometric, intrusion detection

Abstract

Nowadays, a demand is increased all over the world in the field of information security and security regulations. Intrusion detection (ID) plays a significant role in providing security to the information, and it is an important technology to identify various threats in network during transmission of information. The proposed system is to develop a two-layer security model: (1) Intrusion Detection, (2) Biometric Multimodal Authentication. In this research, an Improved Recurrent Neural Network with Bi directional Long Short-Term Memory (I-RNN-BiLSTM) is proposed, where the performance of the network is improved by introducing hybrid sigmoid-tanh activation function. The intrusion detection is performed using I-RNN-BiLSTM to classify the NSL-KDD dataset. To develop the biometric multimodal authentication system, three biometric images of face, iris, and fingerprint are considered and combined using Shuffling algorithm. The features are extracted by Gabor, Canny Edge, and Minutiae for face, iris, and fingerprint, respectively. The biometric multimodal authentication is performed by the proposed I-RNN-BiLSTM. The performance of the proposed I-RNN-BiLSTM has been analysed through different metrics like accuracy, f-score, and confusion matrix. The simulation results showed that the proposed system gives better results for intrusion detection. Proposed model attains an accuracy of 98% for the authentication process and accuracy of 98.94% for the intrusion detection process.

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Author Biographies

M. Gayathri, Department of Computer Science and Engineering, S.R.M. Institute of Science and Technology, Kattankulathur campus, Chennai, India

M. Gayathri is an Assistant Professor in Department of Computer Science and Engineering S.R.M. Institute of Science and Technology, Kattankulathur campus, Chennai, India. Currently she is pursing Ph.D. (CSE) in S.R.M. Institute of Science and Technology, Chennai. She has over ten years of experience in Teaching. Her research interest is Security and Privacy in Biometrics, Network Security, Internet of Things and Cryptography.

C. Malathy, Department of Computer Science and Engineering, S.R.M. Institute of Science and Technology, Kattankulathur campus, Chennai, India

C. Malathy is a Professor in Department of Computer Science and Engineering, S.R.M. Institute of Science and Technology, Kattankulathur campus, Chennai, India. She earned Ph.D. in Computer Science & Engineering from S.R.M. Institute of Science and Technology, Chennai. She has over Twenty-eight years of experience in Teaching and Research. Her areas of interest are Image processing, Data Mining and Computer architecture. She has published research papers in many international conferences and refereed journals.

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Published

2021-11-16

Issue

Section

Enabling AI Technologies Towards Multimedia Data Analytics for Smart Healthcare