Abstract
Internet of Things (IoT) refers to a system of interconnected heterogeneous smart devices communicating without human intervention. A significant portion of existing IoT networks is under the umbrella of ad-hoc and quasi ad-hoc networks. Ad-hoc based IoT networks suffer from the lack of resource-rich network infrastructures that are able to perform heavyweight network management tasks using, e.g. machine learning-based Network Traffic Monitoring and Analysis (NTMA) techniques. Designing light-weight NTMA techniques that do not need to be (re-) trained has received much attention due to the time complexity of the training phase. In this study, a novel pattern recognition method, called Trend-based Online Network Traffic Analysis (TONTA), is proposed for ad-hoc IoT networks to monitor network performance. The proposed method uses a statistical light-weight Trend Change Detection (TCD) method in an online manner. TONTA discovers predominant trends and recognizes abrupt or gradual time-series dataset changes to analyze the IoT network traffic. TONTA is then compared with RuLSIF as an offline benchmark TCD technique. The results show that TONTA detects approximately 60% less false positive alarms than RuLSIF.
Highlights
Internet of Things (IoT) is increasingly recognized as a networking paradigm connecting heterogeneous smart devices without human intervention [1,2]
To analyze the network traffic based on Trend-based Online Network Traffic Analysis (TONTA), we consider two steps: (i) determining boundaries of network traffic behavior changes by recognizing Change points (CPs) of the time-series dataset, and (ii) identifying the trends between every two continuous boundaries as the network traffic behavior
Different Network Traffic Monitoring and Analysis (NTMA) techniques have been introduced based on machine learning solutions, they suffer from high timecomplexity of training and the need for labeled data
Summary
IoT is increasingly recognized as a networking paradigm connecting heterogeneous smart devices without human intervention [1,2]. Considering data forwarding tasks in ad-hoc IoT, existing techniques have been almost successful in establishing an efficient network infrastructure, e.g. routing and clustering techniques [6,7,8]. Among various TCD sub-techniques, CPD techniques are the most common online solutions to identify changes in gradually-generated time-series datasets [23]. The proposed technique determines the behavior of IoT network traffic by analyzing network management characteristics gathered as time-series datasets. To analyze the network traffic based on TONTA, we consider two steps: (i) determining boundaries of network traffic behavior changes by recognizing CPs of the time-series dataset, and (ii) identifying the trends between every two continuous boundaries as the network traffic behavior. Formulating a new model for a dynamic-sliding window to process datasets gathered from ad-hoc based IoT networks to improve the time complexity and accuracy of the proposed algorithm.
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