A Robust Malware Threat Hunting System and Method based on Deep Neural Networks in IoT environments

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Today's industrial companies and even enterprises use IoT devices for automating theirprocesses. These devices are exposed to malware threats and this is a real challenge in theindustrial 4.0 era. Due to evolving malware threats, the targeted companies need to pay forsecurity patches for their malware threat hunting procedures. These patches only work withpreviously seen threats and are not reliable against novel ones, resulting in high false-positiverates. The proposed system can hunt IoT malware threats and can also generate adversarialpayloads from every given executable dataset to create a unique database of adversarialpayloads. With this unique method, the proposed system is also capable of preventing fakesamples from passing through malware threat hunting methods. The proposed system alsoincludes an adversarial payload prevention method to identify fakes (generated sample) that aimto bypass the hunting method. Based on our initial market validation and literature research wefound there is no equivalent mechanism in real-world Industrial Internet of Things (IIoT)environments. Therefore, the proposed method could help the IIoT in terms of saving time andoperational cost by automating the whole malware prevention, generation, and hunting process.