DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS

Main Article Content

J Prashanthi
M. Pravalika
B. Savitha
Ch. Manikanta
M. Balraj
M. Haritha

Abstract

Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyberattacks. This paper proposes the first energy auditing and analytics based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregation-aggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber-attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Prashanthi , J. ., Pravalika, M., Savitha, B. ., Manikanta, C. ., Balraj, M. ., & Haritha, M. . (2023). DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 14(03), 1352–1361. https://doi.org/10.61841/turcomat.v14i03.14524
Section
Research Articles

References

M. A. Al-Garadi, A. Mohamed, A. Al-Ali, X. Du, and M. Guizani, “A survey of machine and deep learning

methods for internet of things (IoT) security,” arXiv preprint arXiv:1807.11023, 2018.

H. Ning, H. Liu, and L. Yang, “Cyber-entity security in the internet of things,” Computer, p. 1, 2013.

H. A. Abdul-Ghani, D. Konstantas, and M. Mahyoub, “A comprehensive IoT attacks survey based on a

building-blocked reference model,” International Journal of Advanced Computer Science and

Applications(IJACSA), vol. 9(3), 2018.

H. Guo, S. Li, B. Li, Y. Ma, and X. Ren, “A new learning Automata-Based pruning method to train deep

neural networks,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3263–3269, Oct. 2018.

F. Li, A. Shinde, Y. Shi, J. Ye, X. Li, and W. Z. Song, “System statistics learning-based iot security:

Feasibility and suitability,” IEEE Internet of Things Journal, pp. 1–8, 2019.

.M. Zou, C. Wang, F. Li, and W. Song, “Network phenotyping for network traffic classification and

anomaly detection,” in IEEE International Symposium on Technologies for Homeland Security (HST),

J. Pacheco and S. Hariri, “IoT security framework for smart cyber infrastructures,” in Foundations and

Applications of Self* Systems, IEEE International Workshops on. IEEE, 2016, pp. 242–247.

R. Roman, J. Zhou, and J. Lopez, “On the features and challenges of security and privacy in distributed

internet of things,” Computer Networks, vol. 57, no. 10, pp. 2266–2279, 2013.

.A. Banerjee, K. K. Venkatasubramanian, T. Mukherjee, and S. K. S. Gupta, “Ensuring safety, security, and

sustainability of mission-critical cyber-physical systems,” Proceedings of the IEEE, vol. 100, no. 1, pp.

–299, 2012.

D. E. Phillips, R. Tan, M.-M. Moazzami, G. Xing, J. Chen, and D. K. Y. Yau, “Supero: A sensor system for

unsupervised residential power usage monitoring,” in 2013 IEEE International Conference on Pervasive

Computing and Communications (PerCom). IEEE, 2013, pp. 66–75.