A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks
Source abstract
Abstract
Internet of Things (IoT) is vulnerable to data-tampering (DT) attacks. Due to resource limitations, many anomaly detection systems (ADSs) for IoT have high false positive rates when detecting DT attacks. This leads to the misreporting of normal data, which will impact the normal operation of IoT. To mitigate the impact caused by the high false positive rate of ADS, this paper proposes an ADS management scheme for clustered IoT. First, we model the data transmission and anomaly detection in clustered IoT. Then, the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device. In the presence of a high false positive rate in ADSs, to deal with the trade-off between the security and availability of data, we develop a linear programming model referred to as a security trade-off (ST) model. Next, we develop an analysis framework for the ST model, and solve the ST model on an IoT simulation platform. Last, we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis. Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Advanced Malware Detection Techniques · IoT and Edge/Fog Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
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