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MENTORED: from modeling to experimentation - predicting and detecting DDoS and zero-day attacks

Abstract

The popularization of the Internet and its advances towards the Internet of Things (IoT) and the Internet of Everything (IoE) have raised expectations for new applications and services in different areas. Despite the great opportunities and benefits, these advances in the Internet also open space for great threats and dangers, some which did not exist before. In particular, there is concern in improving protections against Distributed Denial of Service (DDoS) attacks and against new attacks that may arise as a result of IoT and IoE scaling. Such concerns are relevant when considering recent attacks involving infected IoT devices that were responsible for some of the largest DDoS attacks in history. Considering this current scenario, this project aims to identify, model, and evaluate malicious behaviors associated with IoT to help in the construction of advanced and coordinated solutions to enable: prevention; prediction; detection, and mitigation of DDoS attacks. The team, made up of national and international partners from academia, industry, and government, will produce these solutions as well as build a testbed, to be offered to the entire security community, enabling experimentation of solutions for system and network security in a realistic way. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
VERGUTZ, ANDRESSA; PRATES, JR., NELSON G.; SCHWENGBER, BRUNO HENRIQUE; SANTOS, ALDRI; NOGUEIRA, MICHELE. An Architecture for the Performance Management of Smart Healthcare Applications. SENSORS, v. 20, n. 19 OCT 2020. Web of Science Citations: 0.

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