Abstract: In an approach to secure firmware updating, a system comprises: one or more user devices, each of the one or more user devices having one or more firmware modules; one or more computing devices; a digital signature repository; and a z-engine circuitry. The z-engine circuitry is configured to: receive firmware for a particular user device of the one or more user devices; receive a non-repeating input data; retrieve a hardware digital profile of the particular user device from the digital signature repository; process the non-repeating input data with the hardware digital profile to generate a unique result; perform a hash operation on the unique result to generate an encryption key; encrypt the firmware for the particular user device with the encryption key; and send the encrypted firmware to the particular user device.

Abstract: The various technologies presented herein relate to upgrading industrial control software in real-time. During utilization of a first software to control an industrial process, a determination can be made that the first software needs to be replaced (e.g., partially or entirely) owing to a newer version of software is ready to be deployed, a vulnerability in the first software has been determined, etc. Rather than closing the industrial process down while the first software is replaced, a second software can be deployed such that the first software and the second software are operating in parallel (e.g., receiving the same operational data, process states). When a critical point (upgrade location) in the first software has been reached, control of the industrial process can be switched to the second software, thereby enabling control of the industrial process to occur without having to temporarily cease operation and/or monitoring of the industrial process.

Abstract: The various technologies presented herein relate to determining a network attack is taking place, and further to adjust one or more network parameters such that the network becomes dynamically configured. A plurality of machine learning algorithms are configured to recognize an active attack pattern. Notification of the attack can be generated, and knowledge gained from the detected attack pattern can be utilized to improve the knowledge of the algorithms to detect a subsequent attack vector(s). Further, network settings and application communications can be dynamically randomized, wherein artificial diversity converts control systems into moving targets that help mitigate the early reconnaissance stages of an attack. An attack(s) based upon a known static address(es) of a critical infrastructure network device(s) can be mitigated by the dynamic randomization.