Researchers create 'self-aware' algorithm to ward off hacking attempts
Oct 2021, phys.org
They found a way to hide signals in the unobservable "noise space" of the system:
Using the background noise within these systems' data streams, Abdel-Khalik and his students embed invisible, ever-changing, one-time-use signals that turn passive components into active watchers."We call it covert cognizance," said Abdel-Khalik, an associate professor of nuclear engineering and researcher with Purdue's Center for Education and Research in Information Assurance and Security (CERIAS)."Here, if someone sticks their finger in the data, the whole system will know that there was an intrusion, and it will be able to correct the modified data."
Sounds similar, although not exactly, to the inaudible sounds emitted by the commercials on your phone's music steaming service so that your smart tv can hear it and the two can figure out whether you're listening to their ads, because if the two devices are in the same room, there's a better chance you're with them.
Now that I think about it, maybe it's also like copyright traps, like when mapmakers put fake streets in their maps to catch the people copying their maps who also use the fake streets unknowingly, or when data harvesters put fake people on their marketing lists to catch marketers abusing their lists because they send advertisements to the fake people's addresses (who are usually real people who work for the data company).
Bottom line is -- we're talking about software being self-aware. Self-aware software. The future is now old man.
via Purdue University's Center for Education and Research in Information Assurance and Security:
Arvind Sundaram et al, Covert Cognizance: A Novel Predictive Modeling Paradigm, Nuclear Technology (2021). DOI: 10.1080/00295450.2020.1812349
Matthias Eckhart et al, Digital Twins for Cyber-Physical Systems Security: State of the Art and Outlook, Security and Quality in Cyber-Physical Systems Engineering (2019). DOI: 10.1007/978-3-030-25312-7_14
Yeni Li et al, Data trustworthiness signatures for nuclear reactor dynamics simulation, Progress in Nuclear Energy (2021). DOI: 10.1016/j.pnucene.2020.103612
Arvind Sundaram et al, Validation of Covert Cognizance Active Defenses, Nuclear Science and Engineering (2021). DOI: 10.1080/00295639.2021.1897731
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