Analysis on detection and prevention techniques for data tampering and replay attacks in IoT systems

A qualitative analysis of this work takes an overview of current approaches used in identifying and addressing data tampering and replay attacks in IoT systems with focus on the trade-off involving the use of extra resources, time and energy. Recommendations like Timestamp Based Detection and Edge Computing can be highlighted as the most suitable methods for large scale IoT scenarios because they provide acceptable levels of performance and are resource-friendly. On the other hand, methods such as Blockchain-Based Detection and Deep Learning have superior security and at the same time are computationally demanding for use in high risk, high returns systems. The analysis points out that one should consider using the detection techniques which would successfully exist in IoT systems based on various constricts like devices, the size of the network, and security issues. This guarantees optimum resource optimization for portability, size, power consumption while ailing to create safer and sustainable IoT environments and ecosystems.The paper assesses the advantages, disadvantages, and genera generality of these methodologies across various IoT domains including smart cities, healthcare, and industrial IoT. The findings may be useful to researchers and practitioners, who wish to construct a robust IoT system capable of dealing with changing forms of cyber risk.