Secure Data Aggregation and data Transmission using HMAC Protocol in Cluster base UAV Communication Network

Abstract

Listen

Translate article

In remote or hard-to-reach areas, deploying unmanned aerial vehicle (UAV’s) significantly increases the likelihood of various attacks, making security a critical concern. Given the limited computing power, memory, bandwidth, and energy resources of sensors, traditional security methods designed for resource-heavy systems like WLANs are unsuitable for UAV’s. Therefore, it is crucial to develop specialized security protocols tailored to UAV’s resource constraints. The use of public key cryptography in UAVs faces several challenges, such as high computational costs, lengthy key generation, susceptibility to brute-force attacks, and complexities in key distribution and management. In contrast, symmetric key cryptography, which requires minimal computation and storage, is a more suitable choice for securing data transmission in UAVs. This research focuses on existing and emerging security mechanisms for unmanned aerial vehicle (UAVs), specifically those enhancing the 802.11 standard with additional security layers. The research follows a four-phase methodology. In the first phase, a genetic algorithm is proposed for optimizing the selection and number of cluster heads (CHs) using a clustering approach. This method balances intra-cluster communication, CH distance from the base station (BS), and the remaining energy of nodes to maximize network efficiency. In phase two, a secure data aggregation technique employing the Hash Mandatory Access Control Protocol (HMAC) is introduced to reduce network data overhead and protect against multiple threats. Phase three presents a pairwise key management strategy for UAVs, utilizing a one-way hash function to minimize the impact of compromised nodes while maintaining secure links between neighbouring nodes. The fourth phase introduces a Broadcast Tree Construction method to reduce communication overhead by identifying the shortest paths, integrating data aggregation, and enhancing network connectivity. This approach emphasizes reducing power consumption to extend network lifespan while ensuring the timely detection and removal of rogue nodes before they can transmit harmful data..