Cipher chaining key re-synchronization in LPWAN IoT network using a deep learning approach
Abstract Low Power Wide Area Network (LPWAN) technology enables low power consumption, long range wireless connectivity, and low cost for strengthening the business cases and achieved eminence as the preferred choice for IoT applications as well. Despite these advantages, they have critical limitations such as very restricted payload size and computation power which creates new challenges in their encryption methods including cipher chaining. A resynchronization mechanism is required in the cipher chaining methods, to sync the keys in case of receiving messages out of order due to events such as packet loss (which is very likely in LPWANs). Since current key resynchronization methods consume a part of the transmitting message, they are not applicable in LPWANs considering their very limited payload size. In this paper, a novel mechanism is proposed for key resynchronization using deep learning. The deep learning is used to learn the pattern of messages and recognize whether or not a message is decrypted corruptly. Then, this model is employed as a tool to identify correct key to decrypt messages. The proposed method enables two parties to keep track of their key sequence without having to add any overload to the payload. Various case studies are provided and simulated to exhibit the efficiency of the proposed method. The case studies proved the robustness of the method in different situations such as single and multiple events loss and different lossy networks. Case studies show up to 99.9% accuracy in key resynchronization with the proposed method after message loss.