Abstract
Security of cyber-physical systems against cyber attacks is an important yet challenging problem. Cyber-physical systems are prone to information leakage from the physical domain. The analog emissions, such as magnetic and power, can turn into side channel revealing valuable data, even the crypto key of the system. Template attack is a popular type of side-channel analysis using machine learning technology. Malicious attackers can use template attack to profile the analog emission, then recover the secret key of the system. But conventional template attack requires that the adversary has access to an identical experiment device that he can program to his choice. This study proposes a novel side-channel analysis for physical-domain security in cyber-physical systems. Our contributions are the following three points: (1) Major peak region method for finding points of interests correctly is proposed. (2) A method for establishing templates on the basis of those points of interest still without requiring knowledge of the key is proposed. Several techniques are proposed to improve the quality of the templates as well. (3) A method for choosing attacking traces is proposed to significantly improve the attacking efficiency. Our experiments on three devices show that the proposed method is significantly more effective than conventional template attack. By doing so, we will highlight the importance of performing similar analysis during design time to secure the cyber-physical system.
Highlights
The term cyber-physical systems (CPSs) refers to a new generation of systems with integrated computational and physical capabilities that can interact with humans through many new modalities
Several techniques are proposed to improve the quality of the templates as well
points of interest (POIs) are the basis of building templates in Template attack (TA)
Summary
The term cyber-physical systems (CPSs) refers to a new generation of systems with integrated computational and physical capabilities that can interact with humans through many new modalities. By changing the secret key of the experimental device, the adversary can cross compare the power consumption measured in the encrypting process with different secret keys and find possible positions of information leakage on power traces. These positions are called points of interest (POIs).[17] The knowledge[18,19] of POIs allows the adversary to profile the power distribution of specific operations (usually by multivariate Gaussian model) and build the corresponding templates for extracting the secret key of a new device. How to find POIs correctly? How to build templates effectively? How to extract the correct key? These three questions are answered in section ‘‘Novel TA on SM4.’’ In order to verify the novel method, experiments are shown in section ‘‘Verification and comparison.’’ the conclusion is drawn
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More From: International Journal of Distributed Sensor Networks
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