The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar.

Distributed Denial-of-Service (DDoS) attacks remain challenging to mitigate in the existing systems, including in-home networks that comprise different Internet of Things (IoT) devices. In this article, we present a DDoS traffic detection model that uses a boosting method of logistic model trees for different IoT device classes. Specifically, a different version of the model will be generated and applied for each device class since the characteristics of the network traffic from each device class may have subtle variation(s). As a case study, we explain how devices in a typical smart home environment can be categorized into four different classes (and in our context, Class 1—very high level of traffic predictability, Class 2—high level of traffic predictability, Class 3—medium level of traffic predictability, and Class 4—low level of traffic predictability). Findings from our evaluations show that the accuracy of our proposed approach is between 99.92% and 99.99% for these four device classes. In other words, we demonstrate that we can use device classes to help us more effectively detect DDoS traffic.

For the premium calculation and billing process in the motor insurance, current billing models are very general and not optimally tailored for the end users. Important parameters such as mileage, driving behavior and types of roads driven are not considered for the premium calculation. Development of advanced information and communication systems, significant drop in the technology cost of ownership, as well as the necessity for market differentiation in the insurance industry, facilitated the appearance of new billing models in the motor insurance industry. The purpose of this paper is to demonstrate underlying principles of the technology facilitating new billing models in the motor insurance industry. This paper gives an overview of the system architecture of one of the telematics systems offered and used on the market, as well as the data model used in the billing process. The potential of such a system is demonstrated through the real case project implemented in Eastern Europe

In this digital era expectations for medical quality have increased. As the number of patients continues to increase, conventional health care methods are having to deal with new complications. In light of these observations, researchers suggested a hybrid combination of conventional health care methods with IoT technology and develop MIoT. The goal of IoMT is to ensure that patients can respond more effectively and efficiently to their treatment. But preserving user privacy is a critical issue when it comes to collecting and handling highly sensitive personal health data. However, IoMTs have limited processing power; hence, they can only implement minimal security techniques. Consequently, throughout the health data transfer through MIoT, patient’s data is at risk of data leakage. This manuscript per the authors emphasizes the need of implementing suitable security measures to increase the IoMT's resilience to cyberattacks. Additionally, this manuscript per the authors discusses the main security and privacy issues associated with IoMT and provide an overview of existing techniques.

In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.