Design and implementation of a semi-automated threat analysis system -Tartarus-

In recent years, there has been a notable increase in the number of cyberattacks caused by the change in the work model due to the pandemic. People were forced to work at home, away from the secure confines of a corporate network. Moreover, in the coming years the number, intensity and variety of these attacks are expected to keep increasing. Therefore, it is of vital importance to constantly track and adapt to the new techniques, vulnerabilities and malware used by the cybercriminals to understand your level of exposure against these new threats. This project aims to implement a semi-automated threat analysis system that can distribute and execute malware samples into sandboxed virtual machines. Once it has been executed, this system retrieves metrics of the results of each execution to observe the detection and blocking capacity of the machine against these threats

Amazon Alexa traffic traces

The number of devices that make up the Internet of Things (IoT) has been increasing every year, including smart speakers such as Amazon Echo devices. These devices have become very popular around the world where users with a smart speaker are estimated to be about 83 million in 2020. However, there has also been great concern about how they can affect the privacy and security of their users [1]. Responding to voice commands requires devices to continuously listen for the corresponding wake word, with the privacy implications that this entails. Additionally, the interactions that users may have with the virtual assistant can reveal private information about the user. In this document we publicly share two datasets that can help conduct privacy and security studies from the Amazon Echo Dot smart speaker. The included data contains 300.000 raw PCAP traces containing all the communications between the device and Amazon servers from 100 different voice commands on two different languages. The data can be used to train machine learning algorithms in order to find patterns that can characterize both, the voice commands and people using the device as well as Alexa as the device generating the traffic.Peer ReviewedPostprint (published version