DOI : https://doi.org/10.62527/ijasce.6.3.37

A Cutting-Edge Deep Learning Method For Enhancing IoT Security

Nadia Ansar (1), Mohammad Sadique Ansari (2), Mohammad Sharique (3), Aamina Khatoon (4), Md Abdul Malik (5), Md Munir Siddiqui (6)
(1) Jahangirabd Institute of Technology, U.P, India
(2) Jahangirabd Institute of Technology, U.P, India
(3) Jahangirabd Institute of Technology, U.P, India
(4) Jahangirabd Institute of Technology, U.P, India
(5) Jahangirabd Institute of Technology, U.P, India
(6) Jahangirabd Institute of Technology, U.P, India
Fulltext View | Download
How to cite (IJASEIT) :
Ansar, N., Ansari, M. S., Sharique, M., Khatoon, A., Malik, M. A., & Siddiqui, M. M. (2024). A Cutting-Edge Deep Learning Method For Enhancing IoT Security. International Journal of Advanced Science Computing and Engineering, 6(3), 98–104. https://doi.org/10.62527/ijasce.6.3.37
Citation Format :
There have been significant issues given the IoT, with heterogeneity of billions of devices and with a large amount of data. This paper proposed an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious. The real-time processing capability, scalability, and low false alarm rate in our model surpass some traditional IDS approaches and, therefore, prove successful for application in today's IoT networks. The development and the performance of the model, with possible applications that may extend to other related fields of adaptive learning techniques and cross-domain applicability, are discussed. The research involving deep learning for IoT cybersecurity offers a potent solution for significantly improving network security.

T. F. Lunt, “A survey of intrusion detection techniques,” Computers & Security, vol. 12, no. 4, pp. 405–418, Jun. 1993, doi: 10.1016/0167-4048(93)90029-5.

S. Mukkamala, A. H. Sung, and A. Abraham, “Intrusion detection using an ensemble of intelligent paradigms,” Journal of Network and Computer Applications, vol. 28, no. 2, pp. 167–182, Apr. 2005, doi: 10.1016/j.jnca.2004.01.003.

LeCun, Yann & Bengio, Y. & Hinton, Geoffrey. (2015). Deep Learning. Nature. 521. 436-44. 10.1038/nature14539.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

C. Yin, Y. Zhu, J. Fei, and X. He, “A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks,” IEEE Access, vol. 5, pp. 21954–21961, 2017, doi: 10.1109/access.2017.2762418.

Kim, Gisung & Lee, Seungmin & Kim, Sehun. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications. 41. 1690–1700. 10.1016/j.eswa.2013.08.066.

N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A Deep Learning Approach to Network Intrusion Detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41–50, Feb. 2018, doi: 10.1109/tetci.2017.2772792.

Tang, Tuan & Mhamdi, Lotfi & McLernon, Des & Zaidi, Syed Ali Raza & Ghogho, Mounir. (2016). Deep Learning Approach for Network Intrusion Detection in Software Defined Networking. 10.1109/WINCOM.2016.7777224.

C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel, and M. Rajarajan, “A survey of intrusion detection techniques in Cloud,” Journal of Network and Computer Applications, vol. 36, no. 1, pp. 42–57, Jan. 2013, doi: 10.1016/j.jnca.2012.05.003.

Ansar, Nadia & Parveen, Suraiya & Khan, Ehtiram & Alankar, Bhavya. (2024). DeepSecIoT: An Advanced Deep Learning- Based Algorithm for Enhancing Security in Wireless IoT Devices. Tuijin Jishu/Journal of Propulsion Technology. Vol.45No.1(2024). 1001-4055.2.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, doi: 10.1109/cvpr.2016.90.

Hochreiter, Sepp & Schmidhuber, Jürgen. (1997). Long Short-term Memory. Neural computation. 9. 1735-80. 10.1162/neco.1997.9.8.1735.

Kingma, D.P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. CoRR, abs/1412.6980.

Keras Documentation: Convolutional Layers. Retrieved from https://keras.io/layers/convolutional

Chollet, F. (2021). Deep learning with Python. Simon and Schuster.