Abstract
Internet of Things (IoT) enables a myriad of applications by interconnecting software to physical objects. The objects range from wireless sensors to robots and include surveillance cameras. The applications are often critical (e.g. physical intrusion detection, fire fighting) and latency-sensitive. On the one hand, such applications rely on specific protocols (e.g. MQTT, COAP) and the network to communicate with the objects under very tight timeframe. On the other hand, anomalies (e.g. communication noise, sensors' failures, security attacks) are likely to occur in open IoT systems and can result by sending false alerts or the failure to properly detect critical events. To address that, IoT systems have to be equipped with anomaly detection processing in addition to the required event detection capability. This is a key feature that enables reliability and efficiency in IoT. However, anomaly detection systems can be themselves object of failures and attacks, and then can easily fall short to accomplish their mission. This paper introduces a Reliable Event and Anomaly Detection Framework for the Internet of Things (READ-IoT for short). The designed framework integrates events and anomalies detection into a single and common system that centralizes the management of both concepts. To enforce its reliability, the system relies on a reputation-aware provisioning of detection capabilities that takes into account the vulnerability of the deployment hosts. As for validation, READ-IoT was implemented and evaluated using two real life applications, i.e. a fire detection and an unauthorized person detection applications. Several scenarios of anomalies and events were conducted using NSL-KDD public dataset, as well as, generated data to simulate routing attacks. The obtained results and performance measurements show the efficiency of READ-IoT in terms of event detection accuracy and real-time processing.
Highlights
Internet of Things (IoT) refers to the ubiquitous network of heterogeneous objects such as cameras, sensors and drones [1]
CONTRIBUTIONS AND RESULTS This paper introduces a Reliable Event and Anomaly Detection Framework for the Internet of Things (READ-IoT for short)
We propose the integration of Anomaly Detection Systems (ADS) and EDS in a common system to profit from the subsystem integration advantages in terms of resource optimization, management optimization and improved reliability of the overall system
Summary
Internet of Things (IoT) refers to the ubiquitous network of heterogeneous objects such as cameras, sensors and drones [1]. B. CONTRIBUTIONS AND RESULTS This paper introduces a Reliable Event and Anomaly Detection Framework for the Internet of Things (READ-IoT for short). CONTRIBUTIONS AND RESULTS This paper introduces a Reliable Event and Anomaly Detection Framework for the Internet of Things (READ-IoT for short) This framework enables accurate detection of outliers thanks to the adoption of a common management system and an optimal processing workflow. Improving the system reliability: data integration fed from both EDS and ADS allows for building a common risk management. The performed experiments show that: (1) combining both event and anomaly detection in IoT systems improves the system reliability, (2) the cascaded activation of rule-based and machine learning processing qualifies better the detected outliers, (3) the reputation-aware deployment enforces ADS and EDS reliability and the detection accuracy and (4) the resource-aware provisioning.
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