Automatic Identification and Classification of IoT Devices in Computer Networks - An Overview of Opportunities and Challenges

oai:ronpub.com:OJIOT_2025v10i1n01_PetrozzielloDiscovering IoT devices joining a network is essential for network management, security and optimization. Knowing what is happening on a computer network and finding those IoT devices is necessary to counter hacker attacks. To address the security challenges of IoT devices, we present identification (discovery) and classification. This gives the reader an overview of both areas, which need to be considered together; the very fact that there are many techniques and protocols for managing and communicating with IoT devices makes them both worth considering. Due to the differences in discovery and classification of IoT devices, we first present the provisioning part of the IoT device lifecycle and then discuss the different classification approaches. This thesis also describes the importance of feature extraction for classification and the difference between packet and flow features. In addition, this work discusses the difference between statistical, machine learning and artificial intelligence based classification methods, including large language models and quantum computing. In short, this thesis discusses relevant IoT device discovery and traffic classification techniques, applications, challenges and future directions

Towards Sustainable Smart Cities: Monitoring Air Quality and Comfort for Citizen Health

In smart city development, addressing air pollution and climate change through advanced environmental monitoring systems is crucial for enhancing urban quality of life and public health. This study focuses on the architecture of an intelligent system designed for real-time environmental monitoring in smart cities. The system aims to improve urban quality of life by addressing air pollution and climate change and assessing their impact on public health. The proposed system uses Arduino technology and integrated sensors to monitor PM10, PM2.5, toxic gases, and temperature. It incorporates a database in InfluxDB and Node-RED for efficient data management and visualization. The analysis employs the Knowledge Discovery in Data (KDD) methodology, Principal Component Analysis (PCA), and the DBSCAN algorithm for clustering high-pollution areas. The findings highlight the significant impact of air quality variables on environmental comfort. The system effectively identifies areas with high pollution levels, enabling informed urban planning and decision-making. In conclusion, this study emphasizes the need for effective air quality management and cross-sector collaboration to create healthier urban environments. The intelligent system demonstrates the potential for enhancing environmental comfort and addressing the environmental challenges of modern cities

Closing the Gap between Web Applications and Desktop Applications by Designing a Novel Desktop-as-a-Service (DaaS) with Seamless Support for Desktop Applications

An increasing transformation from locally deployed applications to remote web applications has occurred for about two decades. Nevertheless, abandoning established and essential Windows or Linux desktop applications is in many scenarios impossible. This paper describes and evaluates existing Desktop-as-a-Service solutions and the components required for developing a novel DaaS. Based on the conclusions and findings of this analysis, the paper describes a novel approach for a Desktop-as-a-Service solution that enables, as a unique characteristic, the deployment of non-modified Linux and Windows applications. The interaction with these applications is done entirely through a browser which is unusual for remote interaction with Windows or Linux desktop applications but brings many benefits from the user's point of view because installing any additional client software or local virtualization solution becomes unnecessary. A solution, as described in this paper, has many advantages and offers excellent potential for use in academia, research, industry, and administration

WoTHive: Enabling Syntactic and Semantic Discovery in the Web of Things

In the last decade the Internet of Things (IoT) has experienced a significant growth and its adoption has become ubiquitous in either business and private life. As a result, several initiatives have emerged for addressing specific challenges and provide a standard or a specification to address them; like CoRE, Web of Things (WoT), oneM2M, or OGC among others. One of these challenges revolves around the discovery procedures to find IoT devices within IoT infrastructures and whether the discovery performed is semantic or syntactic. This article focusses on the WoT initiative and reports the benefits that Semantic Web technologies bring to discovery in WoT. In particular, one of the implementations for the WoT discovery is presented, which is named WoTHive and provides syntactic and semantic discovery capabilities. WoTHive is the only candidate implementation that addresses at the same time the syntactic and semantic functionalities specified in the discovery described by WoT. Several experiments have been carried out to test WoTHive; these advocate that the implementation is technically sound for CRUD operations and that its semantic discovery outperforms the syntactic one implemented. Furthermore, an experiment has been carried out to compare whether syntactic discovery is faster than semantic discovery using the Link Smart implementation for syntactic discovery and WoTHive for semantic

Generating SPARQL-Constraints for Consistency Checking in Industry 4.0 Scenarios

A smart manufacturing line consists of multiple connected machines. These machines communicate with each other over a network, to solve a common task. Such a scenario can be located in the Internet of Things (IoT) area. An individual machine can be perceived as an IoT device. Due to machine to machine communication, a huge amount of data is generated during manufacturing. This emerging data flow is an essential part of today's industry, as analyzing data helps improving processes and thus, product quality. To adequately make use of the collected data, we require a high level of data quality. In our work, we address the issue of inconsistent data in smart manufacturing and present an approach to automatically generate SPARQL queries for validation

3D Histogram Based Anomaly Detection for Categorical Sensor Data in Internet of Things

The applications of Internet-of-things (IoT) deploy massive number of sensors to monitor the system and environment. Anomaly detection on streaming sensor data is an important task for IoT maintenance and operation. In real IoT applications, many sensors report categorical values rather than numerical readings. Unfortunately, most existing anomaly detection methods are designed only for numerical sensor data. They cannot be used to monitor the categorical sensor data. In this study, we design and develop a 3D Histogram based Categorical Anomaly Detection (HCAD) solution to monitor categorical sensor data in IoT. HCAD constructs the histogram model by three dimensions: categorical value, event duration, and frequency. The histogram models are used to profile normal working states of IoT devices. HCAD automatically determines the range of normal data and anomaly threshold. It only requires very limit parameter setting and can be applied to a wide variety of different IoT devices. We implement HCAD and integrate it into an online monitoring system. We test the proposed solution on real IoT datasets such as telemetry data from satellite sensors, air quality data from chemical sensors, and transportation data from traffic sensors. The results of extensive experiments show that HCAD achieves higher detecting accuracy and efficiency than state-of-the-art methods

Translation of Array-based Loop Programs to Optimized SQL-based Distributed Programs

Many data analysis programs are often expressed in terms of array operations in sequential loops. However, these programs do not scale very well to large amounts of data that cannot fit in the memory of a single computer and they have to be rewritten to work on Big Data analysis platforms, such as Map-Reduce and Spark. We present a novel framework, called SQLgen, that automatically translates sequential loops on arrays to distributed data-parallel programs, specifically Spark SQL programs. We further extend this framework by introducing OSQLgen, which automatically parallelizes array-based loop programs to distributed data-parallel programs on block arrays. At first, our framework translates the sequential loops on arrays to monoid comprehensions and then to Spark SQL. For SQLgen, the SQL is over coordinate arrays while for OSQLgen, it is over block arrays. As block arrays are more compact than coordinate arrays, computations on block matrices are significantly faster than on arrays in the coordinate format. Since not all array-based loops can be translated to SQL on block arrays, we focus on certain patterns of loops that match an algebraic structure known as a semiring. Many linear algebra operations, such as matrix multiplication required in many machine learning algorithms, as well as many graph programs that are equivalent to a semiring can be translated to distributed data-parallel programs on block arrays using OSQLgen, thus giving us a substantial performance gain. Finally, to evaluate our framework, we compare the performance of OSQLgen with GraphX, GraphFrames, MLlib, and hand-written Spark SQL programs on coordinate and block arrays on various real-world problems

A SIEM Architecture for Advanced Anomaly Detection

Dramatic increases in the number of cyber security attacks and breaches toward businesses and organizations have been experienced in recent years. The negative impacts of these breaches not only cause the stealing and compromising of sensitive information, malfunctioning of network devices, disruption of everyday operations, financial damage to the attacked business or organization itself, but also may navigate to peer businesses/organizations in the same industry. Therefore, prevention and early detection of these attacks play a significant role in the continuity of operations in IT-dependent organizations. At the same time detection of various types of attacks has become extremely difficult as attacks get more sophisticated, distributed and enabled by Artificial Intelligence (AI). Detection and handling of these attacks require sophisticated intrusion detection systems which run on powerful hardware and are administered by highly experienced security staff. Yet, these resources are costly to employ, especially for small and medium-sized enterprises (SMEs). To address these issues, we developed an architecture -within the GLACIER project- that can be realized as an in-house operated Security Information Event Management (SIEM) system for SMEs. It is affordable for SMEs as it is solely based on free and open-source components and thus does not require any licensing fees. Moreover, it is a Self-Contained System (SCS) and does not require too much management effort. It requires short configuration and learning phases after which it can be self-contained as long as the monitored infrastructure is stable (apart from a reaction to the generated alerts which may be outsourced to a service provider in SMEs, if necessary). Another main benefit of this system is to supply data to advanced detection algorithms, such as multidimensional analysis algorithms, in addition to traditional SIEMspecific tasks like data collection, normalization, enrichment, and storage. It supports the application of novel methods to detect security-related anomalies. The most distinct feature of this system that differentiates it from similar solutions in the market is its user feedback capability. Detected anomalies are displayed in a Graphical User Interface (GUI) to the security staff who are allowed to give feedback for anomalies. Subsequently, this feedback is utilized to fine-tune the anomaly detection algorithm. In addition, this GUI also provides access to network actors for quick incident responses. The system in general is suitable for both Information Technology (IT) and Operational Technology (OT) environments, while the detection algorithm must be specifically trained for each of these environments individually

IoT-PMA: Patient Health Monitoring in Medical IoT Ecosystems

The emergence of the Internet of Things (IoT) and the increasing number of cheap medical devices enable geographically distributed healthcare ecosystems of various stakeholders. Such ecosystems contain different application scenarios, e.g., (mobile) patient monitoring using various vital parameters such as heart rate signals. The increasing number of data producers and the transfer of data between medical stakeholders introduce several challenges to the data processing environment, e.g., heterogeneity and distribution of computing and data, lowlatency processing, as well as data security and privacy. Current approaches propose cloud-based solutions introducing latency bottlenecks and high risks for companies dealing with sensitive patient data. In this paper, we address the challenges of medical IoT applications by proposing an end-to-end patient monitoring application that includes NebulaStream as the data processing system, an easy-to-use UI that provides ad-hoc views on the available vital parameters, and the integration of ML models to enable predictions on the patients' health state. Using our end-to-end solution, we implement a real-world patient monitoring scenario for hemodynamic and pulmonary decompensations, which are dynamic and life-threatening deteriorations of lung and cardiovascular functions. Our application provides ad-hoc views of the vital parameters and derived decompensation severity scores with continuous updates on the latest data readings to support timely decision-making by physicians. Furthermore, we envision the infrastructure of an IoT ecosystem for a multi-hospital scenario that enables geo-distributed medical participants to contribute data to the application in a secure, private, and timely manner

Contributions to the 6th Workshop on Very Large Internet of Things (VLIoT 2022)

The concept of the Internet of Things, where small things become available in the Internet and get connected with each other for the purpose of advanced applications, raises many new open challenges to research. This even increases when considering large-scale Internet-of-Things (IoT) configurations, which is the focus of our Very Large Internet of Things (VLIoT) workshop. We recognize that the IoT research community is very active and the industry continuously develops novel IoT applications for daily live. Hence we received many high-quality submissions, from which we accepted 7 to be introduced in this editorial