Artificial Neural Network for the Internet of Things Security

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2020 by IJETT Journal
Volume-68 Issue-11
Year of Publication : 2020
Authors : Amit Sagu, Nasib Singh Gill, Preeti Gulia
DOI :  10.14445/22315381/IJETT-V68I11P218


MLA Style: Amit Sagu, Nasib Singh Gill, Preeti Gulia  "Artificial Neural Network for the Internet of Things Security" International Journal of Engineering Trends and Technology 68.11(2020):129-136. 

APA Style:Amit Sagu, Nasib Singh Gill, Preeti Gulia. Artificial Neural Network for the Internet of Things Security  International Journal of Engineering Trends and Technology, 68(11),129-136.

The Internet of Things (IoT) defines billions of devices that are tied to each other and sharing data via the internet or wireless network. IoT makes available the environment, including home, offices, and vehicles, smarter. With mounting the recognition of IoT, security challenges are growing too. IoT sensors assemble and share classified data, which means to be shielded from unlawful contact. For securing the IoT environment, numerous traditional approaches are being used. Some are lightweight encryption, create a separate connection for IoT devices, or defend against known spoofing identities. An innovative approach to secure IoT environment is employing Artificial Neural Network (ANN) [1], a machine learning model. ANN is a mimic of a biological neural network, which is an information processing model. It can be employed in the intrusion detection system between the IoT environment and outer network; it can also overpower traditional security methods.

[1] M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial, IEEE, (2019) 3039 - 3071.
[2] D. sukhija, A Review Paper on AES and DES Cryptographic, International Journal of Electronics and Computer Science Engineering, pp. 354-359.
[3] M. F. Shireen Nisha, RSA Public Key Cryptography Algorithm – A Review, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, 6 (07) 2017.
[4] M. Karthik, Data Encryption and Decryption by Using Triple-DES and Performance Analysis of Crypto System, International Journal of Scientific Engineering and Research (IJSER), 2014.
[5] A. Alabaichi, F. Ahmad and R. Mahmod, Security analysis of blowfish algorithm, 2013 Second International Conference on Informatics & Applications (ICIA), IEEE, 2013.
[6] K. A. Taher, B. M. Y. Jisan and M. M. Rahman, Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection, International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), IEEE, 2019.
[7] N. Chauhan, A. K. Bhatt, R. K. Dwivedi, and R. Belwal, Accuracy Testing of Data Classification using Tensor Flow a Python Framework in ANN Designing, International Conference on System Modeling & Advancement in Research Trends (SMART) 2018.
[8] X. Sui, Q. Wu, J. Liu, Q. Chen and G. Gu, A Review of Optical Neural Networks, IEEE Access, 8 (2020) 70773 – 70783.
[9] S. Lin, J. Zeng, and X. Zhang, Constructive Neural Network Learning, IEEE Transactions on Cybernetics, vol. 49(1) (2019) 221 – 232.
[10] A. Mukherjee, D. K. Jain, P. Goswami, Q. Xin, L. Yang and J. J. P. C. Rodrigues, Back Propagation Neural Network-Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems, IEEE Access, 8 (2020) 28524 - 28532.
[11] A. Jaiswal, A. S. Manjunatha, B. Madhu and M. P. Chidananda. Predicting unlabeled traffic for intrusion detection using semi-supervised machine learning : International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 2017.
[12] N. Shi, X. Yuan, J. Hernandez, K. Roy, and A. Esterline, Self-Learning Semi-Supervised Machine Learning for Network Intrusion Detection, International Conference on Computational Science and Computational Intelligence (CSCI), 2018.
[13] P. P. S. M. I. a. H. H. Chaoyun Zhang, Deep Learning in Mobile and Wireless Networking: A Survey, IEEE, 2019.

IoT, Machine Learning, Artificial Neural Network.