Detecting FDI Attack on Dense IoT Network with Distributed Filtering Collaboration and Consensus
This addresses security threats in IoT networks, particularly for industrial services, but appears incremental as it builds on existing detection methods with collaborative enhancements.
The paper tackles the problem of false data injection (FDI) attacks in dense IoT networks by introducing CONFINIT, an intrusion detection system that combines watchdog surveillance and collaborative consensus, achieving detection rates of 99% with low false negative and false positive rates.
The rise of IoT has made possible the development of %increasingly personalized services, like industrial services that often deal with massive amounts of data. However, as IoT grows, its threats are even greater. The false data injection (FDI) attack stands out as being one of the most harmful to data networks like IoT. The majority of current systems to handle this attack do not take into account the data validation, especially on the data clustering service. This work introduces CONFINIT, an intrusion detection system against FDI attacks on the data dissemination service into dense IoT. It combines watchdog surveillance and collaborative consensus among IoT devices for getting the swift detection of attackers. CONFINIT was evaluated in the NS-3 simulator into a dense industrial IoT and it has gotten detection rates of 99%, 3.2% of false negative and 3.6% of false positive rates, adding up to 35% in clustering without FDI attackers.