Early Generation and Detection of Efficient IoT Device Fingerprints Using Machine Learning

Abstract

The proliferation of Internet of Things (IoT) markets in the last decade introduces new challenges for network traffic analysis, and processing packet flows to identify IoT devices. This type of device suffers from scarcity, making them vulnerable to spoofing operations. In such circumstances, the device can be recognized by identifying its fingerprint. In this paper, a novel idea to elicit Device FingerPrint (DFP) is presented by extracting 30 features from the collected traffic packets of 19 IoT devices during setup and startup operations. Raspberry Pi 3 Model B+ is configured as an access point to collect and analyze the traffic of seven networked IoT devices using Wireshark Network Protocol Analyzer. Moreover, the rest of IoT devices traffic is taken from the publicly available network traffic dataset. Each IoT device's feature extraction process starts from getting Extensible Authentication Protocol over LAN (EAPOL) protocol, continuing with the other flowed protocols until the first session of Transmission Control Protocol (TCP) related to that device is closed. Depending on some produced variation of device traffic features, 20 fingerprints for each device are created. The probability theorem of Gaussian Naive Bayes (GNB) supervised machine learning is utilized to identify fingerprints of individual known devices and isolate the unknown ones. The performance evaluation for the proposed technique was calculated based on two measures, F1-score and identification accuracy. The average F1 score was around 0.99, while the overall identification accuracy rate was 98.35%.

Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices

“…Genaro sees iot as an ally in alleviating everyday activities, from smart homes to industrial environments, some of which are critical IOP Publishing doi:10.1088/1742-6596/2711/1/012003 2 [3]. The system proposed by Ferman uses a hybrid approach to select features by applying feature selection methods to machine learning classifiers [4]. Francisco believed that due to the use of computers as the main tool for work or leisure, as well as the increase in jobs with high office workload, the proportion of time spent sitting has increased substantially [5].…”

Applications of Big Data Analytics in Traffic Management in Intelligent Transportation Systems

“…The notion of the intelligent transportation system (ITS) appeared to enhance the output of traffic systems, improve road traffic safety and protect the environment [48]- [50]. The advancement of sensing and broadcasting technologies, as well as the development of efficient incorporation of networked information systems, decision-making, and infrastructure of physics, all contributed significantly to the appearance of ITS [47], [51]- [53]. However, with increasing traffic density, especially in developing countries, an intelligent transportation system needs an auxiliary system to control and increase the safety and sustainability of the transport system, and Big Data can meet these requirements.…”