However, it is essential that traffic classification solutions preserve the privacy of users.Įxisting proposals for traffic classification may inspect the packet payload, what creates privacy risks . In this way, the development of classification mechanisms is fundamental to the proper operation of a home network composed of IoT devices . The lack of visibility on the devices present in a smart home can result in security and performance issues . ![]() A smart home, which emerges as one of the main IoT domains , is typically equipped with devices such as smart bulbs, sleep monitors, personal assistants, motion sensors and security cameras, each one performing a different task.ĭue to their quantity and variety, identifying IoT devices in a home network poses significant challenges to the administrator. The Internet of Things is composed of a variety of networked devices with capabilities for sensing user presence on numerous domains, such as smart homes and cities, vehicles, industrial locations and hospitals. Also, the results show that the Decision Tree presented the lowest latency among the five classifiers evaluated in the identification of the devices, followed by k-NN, Random Forest, SVM and Majority Voting. Hypothesis testing is used to validate the obtained results. The results show that the Random Forest algorithm can achieve up to 96% of accuracy in the identification of devices, 99% of precision in distinguishing between IoT and non-IoT devices and 99% of accuracy in the identification of IoT device events. The evaluation included the algorithms k-Nearest Neighbors (k-NN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Majority Voting, some of the most popular algorithms for traffic classification. The solution to characterize IoT devices and events is evaluated with traffic from two real-world testbeds and five classifiers. The solution identifies IoT devices and events, such as voice commands to smart assistants, and also distinguishes between IoT and non-IoT devices. The solution uses only the statistical mean, the standard deviation and the number of bytes transmitted over a one-second window, which can be extracted from the encrypted traffic, making the use of TCP vectors unnecessary. This paper proposes a solution that uses packet length statistics from encrypted traffic to characterize the behavior of IoT devices and events in a smart home scenario. In addition, existing techniques may also use complex mechanisms for extracting traffic characteristics, including the creation of vectors containing data from the Transmission Control Protocol (TCP) sessions. However, existing proposals may inspect the packet payload, what creates risks to IoT users’ privacy, and may use several features, increasing the computational complexity for traffic classification. Simple Weather Indicator is available to install on Ubuntu 16.04 LTS (and above) using the following installer from the project’s GitHub page.Recently, machine learning algorithms have been used to identify Internet of Things (IoT) devices and events. Simple Weather Indicator is a no-frills weather applet that reports the current temperature and conditions for your location. To stop it launching on login you’ll need to manually remove it from the start-up items folder as a root user, a method that’s far from ideal! Download Simple Weather Indicator for Ubuntu 16.04 LTS + ![]() Sadly the biggest single issue with this app remains: there’s no easy way to remove it from your start-up preferences as it doesn’t appear in the Start-Up Applications app. Moved to robust API endpoint with higher uptimeĪs before you can (if you find the auto detection is a little ways off) enter manual coordinates (longitude, latitude), you can also select between Celsius and Fahrenheit, and adjust start-up preferences.The 0.6 release also addresses the lack of customisation options, and adds an option to hide the location name from the panel applet, and an option to round-off temperatures to a…let’s say less overly specific figure! Simple Weather Indicator v0.6 changelog: Indicator Weather v0.6 nixes a number of niggles found in the forecast-reporting applet’s earlier releases, and switches to a new location detection API to improve location accuracy. Another month, another update to the simple weather indicator we first featured back in July.
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