Guest Editorial: Deep Learning Driven Secure Communication for Cyber Physical Systems

Abstract

The proliferation of industrial cyber physical systems (CPSs) is changing our lives. CPS applications are often associated with sensitive data, core infrastructures, and assets, making them attractive in terms of vulnerability, data breach, and denial of services. Moreover, the heterogeneity in terms of protocols, operating systems, and devices combined with poor adoption of standard solutions create insecure design, architectures, and deployments. In addition, due to the use of wireless technologies, secure communication is strongly needed to protect valuable information. Therefore, secure communication management has become a crucial aspect of developing trustworthy systems with the preservation of security and privacy for CPSs. Deep learning (DL) has strong potential to overcome this challenge via data-driven solutions and improve the performance of CPSs while utilizing limited spectrum resources. DL is a more powerful method of data exploration to learn about 'normal' and 'abnormal' behavior according to how CPSs' components and devices interact with one another. The input data of each part of a CPS can be collected and investigated to determine normal patterns of interaction, thereby identifying malicious behavior at early stages. Moreover, DL can be important in predicting new attacks, which are often mutations of previous attacks, because they can intelligently predict future unknown attacks by learning from existing examples. Consequently, CPSs must have a transition from merely facilitating secure communication among devices to security-based intelligence enabled by DL methods for effective and secure systems.