Security at the Edge for Resource-Limited IoT Devices.

The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy's modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems.

Data set and machine learning models for the classification of network traffic originators.

The widespread adoption of encryption in computer network traffic is increasing the difficulty of analyzing such traffic for security purposes. The data set presented in this data article is composed of network statistics computed on captures of TCP flows, originated by executing various network stress and web crawling tools, along with statistics of benign web browsing traffic. Furthermore, this data article describes a set of Machine Learning models, trained using the described data set, which can classify network traffic by the tool category (network stress tool, web crawler, web browser), the specific tool (e.g., Firefox), and also the tool version (e.g., Firefox 68) used to generate it. These models are compatible with the analysis of traffic with encrypted payload since statistics are evaluated only on the TCP headers of the packets. The data presented in this article can be useful to train and assess the performance of new Machine Learning models for tool classification.