From the course: Machine Learning and AI in Cybersecurity by Pearson

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Weaponized malware

Weaponized malware

From the course: Machine Learning and AI in Cybersecurity by Pearson

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Weaponized malware

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Now, let's turn our attention to actual weaponized malware. First of all, doing malware, presumably for an ethical reason, for a nation state, as perhaps a defensive weapon even, requires engineering, basic standard systems engineering. Well too often, these weapons, these software tools, are simply thrown together without a good engineering process, and that leads to unintended consequences, such as what we would normally call collateral damage. If we're going to use any weapon, cyber or kinetic, we'd like to limit its effect as much as possible to intended targets and not hitting innocent bystanders. Specifically, related to what we would like to talk about here, machine learning, AI, cybersecurity, integrating machine learning into the system's development lifecycle of weaponized malware will improve the operational efficacy. You're more likely to hit the target, but it will also improve target discrimination and thereby reduce collateral damage. This chart is also from a paper I…

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