3 research outputs found
IoT Crawler with Behavior Analyzer at Fog layer for Detecting Malicious Nodes
The limitations in terms of power and processing in IoT (Internet of Things) nodes make nodes an easy prey for malicious attacks, thus threatening business and industry. Detecting malicious nodes before they trigger an attack is highly recommended. The paper introduces a special purpose IoT crawler that works as an inspector to catch malicious nodes. This crawler is deployed in the Fog layer to inherit its capabilities, and to be an intermediate connection between the things and the cloud computing nodes. The crawler collects data streams from IoT nodes, upon a priority criterion. A behavior analyzer, with a machine learning core, detects malicious nodes according to the extracted node behavior from the crawler collected data streams. The performance of the behavior analyzer was investigated using three machine learning algorithms: Adaboost, Random forest and Extra tree. The behavior analyzer produces better testing accuracy, for the tested data, when using Extra tree compared to Adaboost and Random forest; it achieved 98.3% testing accuracy with Extra tree
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A Review of Techniques on Gait-Based Person Re-Identification
Copyright (c) 2023 Babak Rahi, Maozhen Li and Man Qi. Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.This research received no external funding