Machine learning based volatile block chain construction for secure routing in decentralized military sensor networks

Wireless Sensor Networks (WSNs) contain multiple wireless sensor nodes deployed around the geographical locations. The WSN used in military applications need more security and hence the deployment of trustworthy nodes and links in WSN provides more secure data transmission in Decentralized Military Sensor Networks (DMSNs). Moreover, the DMSNs work with different set of significance constraints including higher security requirements. The design of DMSNs targets surveillance tasks, intruder tracking tasks, army resource maintenance tasks and communication security requirements. Therefore, building a secure and dynamic DMSN against multiple threats is a challenging task. In addition, security principles developed for DMSN cause excessive energy consumption. Moreover, DMSN has completely open distributed architecture without having any base stations. Under this situation, the need for effective and secured data communication can be achieved with the help of a secure routing protocol. Block chains are generally used for making secure financial transactions. However, the routing protocols used in DMSN can support autonomous routing transactions from one node to other node. In this situation, block chain enabled routing procedures can ensure the trustworthiness of any data that is forwarded through different sensor nodes. Hence, a new Generative Adversarial Networks (GAN) based Block Chain enabled secured Routing Protocol (GBCRP) is proposed in this paper which authenticates and validates the ongoing routing procedures of DMSN. Moreover, a new intrusion detection system is also proposed in this work using GAN which is deployed in the nodes of the DMSN for enhancing the security of communication. Since block chain based routing protocols do not provide security, the GBCRP works for creating volatile block chains using decentralized authentication principles and effective intrusion detection. The proposed system uses a Fully Decentralized Generative Adversarial Network (FDGAN) for monitoring the secure routing transactions by the development of an intrusion detection system. The results obtained from this work show that the proposed GBCRP providing better secured routing compared to the existing systems with respect to security, energy consumption and routing metrics.

Automated summary

The paper addresses the security challenges in deploying WSN in military applications, proposing the GBCRP to enhance secure data transmission between sensor nodes in DMSNs, ensuring trustworthiness of data through block chain enabled routing procedures. Additionally, a new intrusion detection system using GAN is proposed for enhancing communication security in DMSNs.