Publications by authors named "Konstantin Izrailov"
Article Synopsis
- Modern IoT systems require advanced security measures, and static analysis has proven effective in analyzing their software, but increasing complexity necessitates the automation of this process.
- A hypothesis is proposed suggesting that machine learning can enhance static analysis for IoT systems, leading to a structured research approach that confirms this applicability and establishes a framework.
- Key contributions include systemizing static analysis stages, formalizing machine-learning models, reviewing related publications, proving machine learning's utility at various analysis stages, and presenting an intelligent framework for IoT static analysis.
Ensuring the security of modern cyberphysical devices is the most important task of the modern world. The reason for this is that such devices can cause not only informational, but also physical damage. One of the approaches to solving the problem is the static analysis of the machine code of the firmware of such devices.
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