A Deep Learning Framework for Intrusion Detection and Multimodal Biometric Image Authentication
DOI:
https://doi.org/10.13052/jmm1550-4646.18212Keywords:
Authentication, Deep learning, Recurrent neural network, Multimodal, biometric, intrusion detectionAbstract
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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Sree, S.R. Soruba and Dr. Radha, ‘A Survey on Fusion Techniques for Multimodal Biometric Identification’. International Journal of Innovative Research in Computer and Communication Engineering. Vol. 02. pp. 7493–7497, 2015.
Abozaid, A., Haggag, A., Kasban, H. and Eltokhy, M., ‘Multimodal biometric scheme for human authentication technique based on voice and face recognition fusion’. Multimedia Tools and Applications, 78, 78, 16345–16361 (2019). https://doi.org/10.1007/s11042-018-7012-3.
Shankar, K., Elhoseny, M., Chelvi, E.D. and Lakshmanaprabu, S.K. et al., ‘An Efficient Optimal Key Based Chaos Function for Medical Image Security’. IEEE Access, 6, pp. 77145–77154, 2018. https://doi.org/10.1109/ACCESS.2018.2874026
SreeVidya, B. and Chandra, E., ‘Multimodal biometric hash key cryptography-based authentication and encryption for advanced security in the cloud’. Biomedical Research, Medical Diagnosis and Study of Biomedical Imaging Systems and Applications, 29, 506–516, 2018. https://doi.org/10.4066/biomedicalresearch.29-17-1766.
Jahnavi, S. and Nandini, C, ‘Novel Multifold Secured System by Combining Multimodal Mask Steganography and Naive Based Random Visual Cryptography System for Digital Communication’. Journal of Computational and Theoretical Nanoscience, vol. 17, no. 12, pp. 5279–5295, 2020. https://doi.org/10.1166/jctn.2020.9420.
Brown, R., Bendiab, G., Shiaeles, S and Ghita, B, ‘A Novel Multimodal Biometric Authentication System Using Machine Learning and Blockchain’. International Networking Conference. Springer, pp. 31–46, 2020. https://doi.org/10.1007/978-3-030-64758-2_3.
Evangelin, L.N. and Fred, A.L. ‘Securing recognized multimodal biometric images using the cryptographic model’. Multimedia Tools and Applications, 18735–18752, 2021. https://doi.org/10.1007/s11042-021-10541-8.
Hafemann, L.G., Oliveira, L.S., Cavalin, P.R. and Sabourin, R., ‘Transfer learning between texture classifications tasks using convolutional neural networks’. International joint conference neural networks. pp. 1–7, 2015. https://doi.org/10.1109/IJCNN.2015.7280558.
El Khiyari, H. and Wechsler H, ‘Face recognition across time lapse using convolutional neural networks’. Journal of Information Security, vol. 7, no. 3, pp. 141–151, 2016. https://doi.org/10.4236/jis.2016.73010.
Ali, Z., Hossain, M.S., Muhammad, G. and Ullah, et al., ‘A. Edge-centric multimodal authentication system using encrypted biometric templates’. Future Generation Computer Systems, Vol. 85, pp. 76–87, 2018. https://doi.org/10.1016/j.future.2018.02.0404.
Walia, G.S., Singh, T., Singh, K. and Verma, N, ‘Robust multimodal biometric system based on optimal score level fusion model’. Expert Systems with Applications, vol. 116, pp. 364–376, 2019. https://doi.org/10.1016/j.eswa.2018.08.036
Sandeep Singh Sengar, U. Hariharan and K. Rajkumar, ‘Multimodal Biometric Authentication System using Deep Learning Method’, International Conference on Emerging Smart Computing and Informatics (ESCI), AISSMS Institute of Information Technology, Pune, India. March 12–14, 2020. https://doi.org/10.1109/ESCI48226.2020.9167512.
Zhu, Q., Xu, X., Yuan, N., and Zhang, Z. et al., ‘Latent correlation embedded discriminative multi-modal data fusion’. Signal Processing, vol. 171, 107466, 2020. https://doi.org/10.1016/j.sigpro.2020.107466.
Acharya, U.R., Oh, S.L., Hagiwara, Y. and Tan, J.H. et al., Gertych, A., San Tan, R., ‘A deep convolutional neural network model to classify heartbeats’. Computers in Biology and Medicine, vol. 89, pp. 389–396. 2017. https://doi.org/10.1016/j.compbiomed.2017.08.022.
Al Rahhal, M.M., Bazi, Y., Almubarak, H. and Alajlan, N. et al., ‘Dense convolutional networks with focal loss and image generation for electrocardiogram classification’. IEEE Access, vol. 7, pp. 182225–182237, 2019. https://doi.org/10.1109/ACCESS.2019.2960116.
Alcaraz, R., Abásolo, D., Hornero, R. and Rieta, J.J., ‘Optimal parameters study for sample entropy-based atrial fibrillation organization analysis’ Computer methods and programs in biomedicine, vol. 99, pp. 124–132, 2010. https://doi.org/0.1016/j.cmpb.2010.02.009.
Andersen, R.S., Peimankar, A. and Puthusserypady, S., ‘A deep learning approach for real-time detection of atrial fibrillation’. Expert Systems with Applications, vol. 115, pp. 465–473, 2019. https://doi.org/10.1016/j.eswa.2018.08.011.
Andreotti, F., Carr, O., Pimentel, M.A., and Mahdi, A. et al., ‘Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ecg’. Computing in Cardiology (CinC), IEEE. pp. 1–4. 2017. https://doi:org/10.22489/CinC.2017.360-239.
Attia, Z.I., Friedman, P.A., Noseworthy, P.A and Lopez-Jimenez et al., ‘Age and sex estimation using artificial intelligence from standard 12-lead ecgs’. Circulation: Arrhythmia and Electrophysiology, vol. 12, e007284, 2019. https://doi.org/10.1161/CIRCEP.119.007284.
Syed Aqeel Haider, Yawar Rehman and S.M. Usman Ali, ‘Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics’, Electronics, vol. 9, pp. 1916, 2020. https://doi.org/10.3390/electronics9111916.
Xiang zang, Linoyao, Chaoran Hung and Tao Gu et al., ‘DeepKey: A Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves’, ACM Transactions on Intelligent Systems and Technology, Article No.: 49 https://doi.org/10.1145/3393619, May 2020. https://doi.org/10.1145/3393619.
Saadatnejad, Saeed, Oveisi, Mohammadhosein and Hashemi et al., ‘LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices’. IEEE Journal of Biomedical and Health Informatics. pp. 1–1. 10.1109/JBHI.2019.2911367,2019. https://doi.org/10.1109/JBHI.2019.2911367.
R. Vinayakumar, K. Soman, and P. Poornachandran, ‘Evaluating effectiveness of shallow and deep networks to intrusion detection system’, International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Udupi, India, pp. 1282–1289, September 2017. https://doi.org/10.1109/ICACCI.2017.8126018.
Yirui Wu, Dabao Wei, and Jun Feng, ‘Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey’, Hindawi Security and Communication Networks, Volume Article ID 8872923, 17 pages https://doi.org/10.1155/2020/8872923, 2020. https://doi.org/10.1155/2020/8872923.
R. Vinayakumar, M. Alazab, K.P. Soman and P. Poornachandran, et al., ‘Deep learning approach for intelligent intrusion detection system’, IEEE Access, vol. 7, pp. 41525–41550, 2019. https://doi.org/10.1109/ACCESS.2019.2895334.
Shideh Saraeian, and Mahya Mohammadi Golchi, ‘Application of Deep Learning Technique in an Intrusion Detection System’, International Journal of Computational Intelligence and Applications, vol. 19, No. 02, 2050016, 2020. https://doi.org/10.1142/S1469026820500169.
T.A. Tang, L. Mhamdi, D. McLernon and S.A.R. Zaidi et al., ‘Deep learning approach for network intrusion detection in software defined networking’, International Conference on Wireless Network Mobile Communication. (WINCOM), pp. 258–263. Oct. 2016. https://doi.org/10.1109/WINCOM.2016.7777224
R.B. Krishnan and N. Raajan, ‘An intellectual intrusion detection system model for attacks classification using RNN’, International Journal of Pharmaceutical Technology and Biotechnology, vol. 8, no. 4, pp. 23157–23164, 2016.
Chuanlong Yin, Yuefei Zhu, Jinlong Fei, and Xinzheng He, ‘A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks‘, IEEE Access, vol. 5, 2017. https://doi.org/10.1109/ACCESS.2017.2762418.
M. Hassan, A. Gumaei, A. Alsanad, and M. Alrubaian et al., ‘A Hybrid Deep Learning Model for Efficient Intrusion Detection in Big Data Environment’, Information Sciences, vol. 513, 2019. https://doi.org/10.1016/j.ins.2019.10.069.
Pramita Sree Muhuri, Prosenjit Chatterjee, Xiaohong Yuan and Kaushik Roy et al., ‘Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks’. Information, vol. 11, pp. 243; doi:10.3390/info11050243. 2020. https://doi.org/10.3390/info11050243.
Tavallaee, M., Bagheri, E., Lu, Wand Ghorbani, ‘A Detailed Analysis of the KDD CUP 99 Data Set’. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada, pp. 1–6, 8–10 July 2009. https://doi.org/10.1109/CISDA.2009.5356528.
Dhanabal, L. and Shantharajah, S.P, ‘A Study on NSL_KDD Dataset for Intrusion Detection System Based on Classification Algorithms.’ Computer Science. vol. 4, pp. 446–452, 2015. https://doi.org/10.17148/IJARCCE.2015.4696.
Hamid, Y. Balasaraswathi, V.R., Journaux, L. and Sugumaran, M. ‘Benchmark Datasets for Network Intrusion Detection: A Review.’ International Journal of Network. Security. vol. 20, pp. 645–654. 2018. https://doi.org/10.6633/IJNS.2018XX.20%28X%29.XX.
M. Hammad, Y. Liu and K. Wang, ‘Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint’, IEEE Access, vol. 7, pp. 26527–26542, doi: 10.1109/ACCESS.2018.2886573.2019. https://doi.org/10.1109/ACCESS.2018.2886573.
Muthukumar, A., and Kavipriya, A, ‘A biometric system based on Gabor feature extraction with SVM classifier for Finger-Knuckle-Print’. Pattern Recognition Letters, vol. 125, pp. 150–156, 2019. https://doi.org/10.1016/j.patrec.2019.04.007.
Chanukya, P.S. and Thivakaran, T.K., ‘Multimodal biometric cryptosystem for human authentication using fingerprint and ear’, Multimedia. Tools and Applications, vol. 79, pp. 659–673, 2020. https://doi.org/10.1007/s11042-019-08123-w.
Devan, P., Khare, N. ‘An efficient XGBoost–DNN-based classification model for network intrusion detection system’. Neural Computing & Applications, 32, 12499–12514, 2020. https://doi.org/10.1007/s00521-020-04708-x
Zheng, D., Hong, Z., Wang, N., and Chen, P. An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application. Sensors (Basel, Switzerland), 20(6), 1706, 2020. https://doi.org/10.3390/s20061706
