NDLI: KCAD: kinetic cyber-attack detection method for cyber-physical additive manufacturing systems

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KCAD: kinetic cyber-attack detection method for cyber-physical additive manufacturing systems

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KCAD: kinetic cyber-attack detection method for cyber-physical additive manufacturing systems

Content Provider	ACM Digital Library
Author	Faruque, Mohammad Abdullah Al Canedo, Arquimedes Chhetri, Sujit Rokka
Abstract	Additive Manufacturing (AM) uses Cyber-Physical Systems (CPS) (e.g., 3D Printers) that are vulnerable to kinetic cyber-attacks. Kinetic cyber-attacks cause physical damage to the system from the cyber domain. In AM, kinetic cyber-attacks are realized by introducing flaws in the design of the 3D objects. These flaws may eventually compromise the structural integrity of the printed objects. In CPS, researchers have designed various attack detection method to detect the attacks on the integrity of the system. However, in AM, attack detection method is in its infancy. Moreover, analog emissions (such as acoustics, electromagnetic emissions, etc.) from the side-channels of AM have not been fully considered as a parameter for attack detection. To aid the security research in AM, this paper presents a novel attack detection method that is able to detect zero-day kinetic cyber-attacks on AM by identifying anomalous analog emissions which arise as an outcome of the attack. This is achieved by statistically estimating functions that map the relation between the analog emissions and the corresponding cyber domain data (such as G-code) to model the behavior of the system. Our method has been tested to detect potential zero-day kinetic cyber-attacks in fused deposition modeling based AM. These attacks can physically manifest to change various parameters of the 3D object, such as speed, dimension, and movement axis. Accuracy, defined as the capability of our method to detect the range of variations introduced to these parameters as a result of kinetic cyber-attacks, is 77.45%.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450344661
DOI	10.1145/2966986.2967050
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Intrusion detection Cyber-physical systems Kinetic cyber-attacks Additive manufacturing Security
Content Type	Text
Resource Type	Article

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