Open AccessDetection of Anomalies in the Traffic of IoT Devices
Author(s) -
Ivan Murenin
Publication year - 2021
Publication title -
trudy učebnyh zavedenij svâzi
Language(s) - English
Resource type - Journals
eISSN - 2712-8830
pISSN - 1813-324X
DOI - 10.31854/1813-324x-2021-7-4-128-137
Subject(s) - anomaly detection , computer science , data mining , anomaly (physics) , key (lock) , event (particle physics) , internet of things , series (stratigraphy) , real time computing , pattern recognition (psychology) , artificial intelligence , embedded system , geology , physics , computer security , quantum mechanics , condensed matter physics , paleontology
The article proposes an approach to finding anomalies in the traffic of IoT devices based on time series analysis and assessing normal and abnormal behavior using statistical methods. The main goal of the proposed approach is to combine statistical methods for detecting anomalies using unlabeled data and plotting key characteristics of device profiles. Within this approach the following techniques for traffic analysis has been developed and implemented: a technique for a feature extraction, a normal behavior boundary building technique and an anomaly detection technique. To evaluate the proposed approach, we used a technique for generating event logs from devices with the generation of anomalous markup. The experiments shown that the GESD-test gives the best results for anomaly detection in IoT traffic.