Data Mining for the Security of Cyber Physical Systems Using Deep-Learning Methods

Authors

  • Bhagawan Nath University of Jyväskylä, Finland
  • Timo Hamaleinen University of Jyväskylä, Finland
  • Soundararajan Ezekiel Indiana University of Pennsylvannia, PA, USA

DOI:

https://doi.org/10.34190/iccws.17.1.74

Keywords:

cyber physical system, autoencoder, support vector machine, fault tolerance, sensor data, cyber attack

Abstract

Cyber Physical Systems (CPSs) have become widely popular in recent years, and their applicability have been growing exponentially. A CPS is an advanced system that incorporates a computation unit along with a hardware unit, allowing for computing processes to interact with the physical world. However, this increased usage has also led to the security concerns in them, as they allow potential attack vendors to exploit the possibilities of committing misconduct for their own benefit. It is of paramount importance that these systems have comprehensive security mechanisms to mitigate these security threats. A typical attack vector for a CPS is malicious data supplied by compromised sensors that are part of the CPSs. To combat this attack vector, many systems are secured through fault tolerance, including methods such as checkpointing to recover the system. Looking at the diverse nature of attacks and their ever growing complexities, traditional security approaches may not counter them efficiently, which creates a vacuum to be filled with sophisticated state-of-the-art techniques. In this paper, Deep Learning methods such as autoencoders, and Support Vector Machines are proposed to secure CPSs against these attacks. The networks in these applied methods are trained with a normal data profile devoid of any malicious data. Data collected from the system’s sensors at specified intervals is used to form a data series and input to the neural networks. The networks compare and analyze new data to the normal profile to detect anomalies, if there is any. In the presence of anomalous data, the networks generate corrective action(s) for these sensors and the physical states they are recording. Through detection of anomalies, effective security of CPSs may be improved in addition to providing protection for the sensors. Moreover, the proposed method of securing CPSs opens up the possibility of further research by showcasing the applicability of neural networks in securing CPSs.

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Published

2022-03-02