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Method of anomaly detection for privacy protection in internet of things

An exception and message technology, applied in digital transmission systems, safety communication devices, electrical components, etc.

Active Publication Date: 2022-07-08
IBM CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, such conventional techniques still leave room for improvement in terms of detection accuracy as well as privacy protection.

Method used

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  • Method of anomaly detection for privacy protection in internet of things
  • Method of anomaly detection for privacy protection in internet of things
  • Method of anomaly detection for privacy protection in internet of things

Examples

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Embodiment Construction

[0014] Embodiments may provide techniques for detecting cybersecurity events in IoT data traffic that provide improved detection accuracy and privacy protection. Embodiments may use temporal hierarchies such as day of the week, time of day, and part of hour to model metadata information and cluster similarly-behaving devices. Embodiments may use limited, discrete message sizes to allow rigorous behavioral modeling. In contrast to a high variance Gaussian distribution of message sizes, embodiments may identify a relatively small number of different message sizes (each surrounded by a low variance message size distribution) to provide more accurate anomaly detection. The very mission-specific nature of these devices also allows defenders to identify distinct sequences from which any deviations can be counted as anomalies.

[0015] exist figure 1 An exemplary block diagram of an Internet of Things (IoT) system 100 is shown in . In this example, multiple IoT devices 104A-L are ...

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Abstract

Embodiments may provide techniques to detect network security events in IoT data traffic that provide improved detection accuracy and privacy protection. For example, in an embodiment, a method may be implemented in a computer including a processor, a memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may include collecting a plurality of messages destined and from at least one device; extracting metadata features from the plurality of collected messages; generating a time window; determining additional features based on the extracted metadata features present during the time window; detecting a behavioral pattern of the at least one device based on the collected plurality of messages, clustering the determined additional features and the detected behavioral pattern present during the time window; and detecting at least one anomaly or anomaly type using the clustered determined additional features and the detected behavior pattern.

Description

Background technique [0001] The present invention relates to techniques for detecting network security events in IoT data traffic that provide improved detection accuracy and privacy protection. [0002] As more Internet of Things (IoT) devices connect to the network, the need to detect cybersecurity incidents becomes more prominent. The network traffic of IoT devices has certain unique characteristics. Exploiting these properties allows defenders to detect abnormal deviations. For example, some conventional techniques can use the data and the context of the data to detect anomalies in data generated from sensors. Conventional techniques can filter the data, perform statistical analysis on the data, and analyze header fields from packets carrying the data. However, such conventional techniques still leave room for improvement in detection accuracy and privacy protection. [0003] Accordingly, a need has arisen for techniques for detecting cybersecurity incidents in IoT dat...

Claims

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Application Information

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IPC IPC(8): H04L9/40
CPCH04L63/123H04L63/20H04L63/1425H04L63/126H04L63/1416H04L63/1433
Inventor O·索瑟阿努L·格瑞恩博格E·阿哈罗尼A·阿迪
Owner IBM CORP
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