Sinkhole and Black Hole Attack Detection Using Dynamic Reliability Based Anomaly Architecture for Sensor Networks

In this paper wireless sensor networks endure disruption of communication functions and attacks on network integrity stemming from sinkhole and black hole threats. Active attack scenarios allow malicious nodes to damage network performance and lose data packet information by directing packets incorrectly and discarding them. Traditional security technology fails to stop attacks because it cannot match the evolving speed of large network spaces. We use a Dynamic Reliability based Anomaly Architecture (DRA) which assesses network node trustworthiness through network behavior analysis and characteristic interaction evaluations. Through constant reliability scoring adjustments the system detects sinkhole effectors and blocks black hole network intrusions. Combination of real-time tracking of node activities with dynamic network situation evaluation forms the basis of this security-enhanced network protection strategy. Latest research proves networks implementing dynamic reliability mechanisms successfully protect against sinkhole and black hole attacks by keeping the proportion of false positives to a minimum thus improving security detection performance. This architecture creates a scalable solution to effectively extend protection throughout wireless sensor networks for multiple future applications. The upgraded system produces higher performance compared to conventional methods through enhanced precision and better adaptability while reducing power consumption.