Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic

This paper proposes a new duty-cycle-based protocol for transmitting emergent data with high priority and low latency in a sensor network environment. To reduce power consumption, the duty cycle protocol is divided into a listen section and a sleep section, and data can only be received when the receiving node is in the listen section. In this paper, high-priority transmission preempts low-priority transmission by distinguishing between high-priority preamble and low-priority preamble. However, even when a high priority transmission preempts a low priority transmission such that the high priority transmission is received first, if the sleep period is very long, the delay may be large. To solve this problem, the high priority short preamble and high priority data reduce receiver sensitivity and increase coverage through repeated transmission. If there are several receiving nodes within a wide coverage, the receiving node that wakes up first can receive the transmission, thus reducing the delay. The delay can also be further reduced by alternately reducing the sleep cycle of one node among the receiving nodes that can receive it. This paper shows that emergent data can be transmitted effectively and reliably by reducing the delay of high-priority data to a minimum through the use of preemption, coverage extension, and an asymmetric sleep cycle.

Section: Discussionmentioning

confidence: 99%

Duty-Cycle-Based Pre-Emption Protocol for Emergency Networks

Kim

2021

“…Recently, device identification by learning from traffic features has become a hotspot of research. The related supervised ML algorithms [14] include Random Forest (RF) [15][16][17][18], K-Nearest Neighbors (KNN) [19], Support Vector Machine (SVM) [20][21][22], Gradient boosting [23], Naive Bayes [24,25], Decision Trees (DT) [26], NLP [27], and so on. The related unsupervised ML methods include the Gibbs Sampling Dirichlet Multinomial Mixture Model (GSDMM) [28], Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [29], Kmeans [30], and so on.…”

Section: Introductionmentioning

confidence: 99%

WYSIWYG: IoT Device Identification Based on WebUI Login Pages

Wang¹,

Li²,

Jing³

et al. 2022

With the improvement of intelligence and interconnection, Internet of Things (IoT) devices tend to become more vulnerable and exposed to many threats. Device identification is the foundation of many cybersecurity operations, such as asset management, vulnerability reaction, and situational awareness, which are important for enhancing the security of IoT devices. The more information sources and the more angles of view we have, the more precise identification results we obtain. This study proposes a novel and alternative method for IoT device identification, which introduces commonly available WebUI login pages with distinctive characteristics specific to vendors as the data source and uses an ensemble learning model based on a combination of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) for device vendor identification and develops an Optical Character Recognition (OCR) based method for device type and model identification. The experimental results show that the ensemble learning model can achieve 99.1% accuracy and 99.5% F1-Score in the determination of whether a device is from a vendor that appeared in the training dataset, and if the answer is positive, 98% accuracy and 98.3% F1-Score in identifying which vendor it is from. The OCR-based method can identify fine-grained attributes of the device and achieve an accuracy of 99.46% in device model identification, which is higher than the results of the Shodan cyber search engine by a considerable margin of 11.39%.

“…A considerably explored area of research for securing IoT devices is the use of machine learning to detect security issues [ 2 , 3 ]. Most machine learning approaches learn device and traffic features from existing data for detecting security attacks using classification methods [ 4 , 5 ]. Moreover, IoT sensors can provide data that can be effectively used for making real time decisions.…”

Section: Introductionmentioning

confidence: 99%

A Blockchain Based Secure IoT System Using Device Identity Management

Sabrina

Sohail²

2022

Sharing data securely and efficiently has been identified as an issue in IoT-based smart systems such as smart cities, smart agriculture, smart health, etc. A large number of IoT devices are used in these smart systems and they produce a large amount of data. IoT devices generally have limited storage and processing capabilities, and configuring any security techniques on these devices is a challenge. In this paper, we propose a novel device identity management approach for blockchain-based IoT systems that provides data security in two ways. Firstly, a lightweight time-based identification protocol that uses hub identification for validating data. Secondly, data storage is augmented with an effective blockchain application for providing easy access and immutability for data sharing among multiple parties. Our initial prototype implementation shows that: our identity management approach can be implemented in large scale settings, our system can be effectively implemented in blockchain platforms, and our performance evaluation result shows that the prototype fulfills system requirements adequately.