Visible to the public Hierarchical Abnormal-Node Detection Using Fuzzy Logic for ECA Rule-Based Wireless Sensor Networks

TitleHierarchical Abnormal-Node Detection Using Fuzzy Logic for ECA Rule-Based Wireless Sensor Networks
Publication TypeConference Paper
Year of Publication2018
AuthorsBerjab, N., Le, H. H., Yu, C., Kuo, S., Yokota, H.
Conference Name2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing (PRDC)
Date PublishedDec. 2018
ISBN Number978-1-5386-5700-3
Keywordsabnormal nodes, abnormal-node detection, abnormality thresholds, Correlation, data integrity, data integrity attack, data-integrity detection, Dependability, dependable monitoring system, distributed system, ECA rule-based wireless sensor networks, ECA rules, embedded sensor devices, event-condition-action rule, failure handling, Fuzzy logic, heterogeneous sensor readings, hierarchical abnormal-node detection, hierarchical framework, Internet of Things, IoT security, lightweight monitoring system, malicious data-integrity attacks, malicious nodes, Metrics, Monitoring, network attacks, networked system, pubcrawl, resilience, Resiliency, security, security of data, sensor correlations, Silicon, telecommunication security, Temperature measurement, Temperature sensors, Wireless Sensor Network, Wireless sensor networks, WSNs

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

Citation Keyberjab_hierarchical_2018