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The time required to open a breaker or a valve is determined by the physical properties of the device.If an acknowledgement arrives too soon after the command is issued — less time than it would take for a breaker or valve to open, for instance — the security system could suspect spoofing, Beyah explained.Beyond industrial controls, the principle could also apply to the Internet of Things (Io T), where the devices being controlled have specific signatures related to switching them on and off.
To develop the device fingerprints, the researchers have built computer models of utility grid devices to understand how they operate.
Information to build the models came from “black box” techniques — watching the information that goes into and out of the system — and “white box” techniques using schematics or physical access to the systems and unique signatures that indicates the identity of specific devices, or device type, or associated actions.
Industrial control system (ICS) networks used in critical infrastructures such as the power grid present a unique set of security challenges.
The distributed networks are difficult to physically secure, legacy equipment can make cryptography and regular patches virtually impossible, and compromises can result in catastrophic physical damage.The benefit to someone like a medical examiner is to better assess what happened to a person and if they were in a particular place, or in the vicinity when an event occurred.For example, your hands and palms are so unique that if you were to pick up a cup of coffee, if you left it at a crime scene, years later, those identifying factors could still place you at that particular location.Security devices listening to signals traversing the grid’s control systems can differentiate between these legitimate devices and signals produced by equipment that’s not part of the system.Devices such as circuit breakers and electrical protection systems can also be told to open or close remotely, and they then report on the actions they’ve taken.Additionally, the physical fingerprinting method is extended to develop a completely new class of fingerprint generation that requires neither prior access to the network nor an example target device.Fingerprint classification accuracy is evaluated using a combination of a real world five month dataset from a live power substation and controlled lab experiments.They may not be able to give a date or time, but this stamp on your hand, so to speak, can confirm that you were at a location, if indeed, you said you were not.It could also mean that someone took a coffee mug that you used and placed it there.The researchers used supervised learning techniques when a list of IP addresses and corresponding device types were available; and unsupervised learning when not available, with performance nearly as high as the supervised learning methods.The researchers have demonstrated the technique on two electrical substations, and plan to continue refining it until it becomes close to 100 percent accurate.