Securing Smart Device Using Smarter Binary Analysis

Hackers are increasingly targeting IoT devices as a place to implant malware. One way to reduce these threats is to mass analyze the firmware inside these systems looking for vulnerabilities. The recipient of an AXA Post-doctoral fellowship, Dr. Giuseppe Antonio Di Luna, of the Sapienza University of Rome (Italy), is building new machine learning techniques capable of analyzing the code contained in the firmware in it raw binary form. The aim is to facilitate automated analyses of a very large number of firmware, to mitigate hazards to their users, or the threat of coordinated attacks on public networks. The result of his research will be released under an open-source license.