The same mechanics can be employed in security-related use cases, such as determining safe device behavior and general usage patterns, which can subsequently help to spot and block abnormal activity and potentially harmful behavior.
Already, several tech firms are drawing on this to offer solutions that enhance Io T security, especially in smart homes, where there are no defined security standards and practices.
“The way to address this in real time is to create a learning system that takes those outliers and solicits human feedback on them,” Veeramachaneni explains.
“The human alone can distinguish between malicious and benign, and that feedback returns to the system to create predictive models that can mimic human judgment — but at huge scale and in real time.” This is especially pertinent in Io T ecosystems, where large numbers of devices are involved, and the real-time analysis of the overwhelming amount of data generated are beyond human abilities.
The problem with this primitive method is that it produces too many false alarms and false positives.
The approach suggested by Pattern Ex is to develop a solution that incorporates machine learning and augments it with human analyst insight for greater attack detection.
The system learns from the experience and makes more accurate decisions next time.
“This model helps improve threat detection accuracy and decrease the number of false positives dramatically over time,” Veeramachaneni says.
“Neither machine learning nor humans can do it alone,” he says.
Goal and objectives of the thesis: The purpose of this Ph D thesis is to research in-depth and multidimensional content the special protection of children under International Humanitarian Law, which will contribute to the scientific rationale for the institution legal protection of children under international humanitarian law.