APT-Sweeper Identification of malware based on analysis of the transmission context of data streams

01/01/2014 to 31/12/2017

Data streams always consist of information about the content of the message and context of the message (metadata, protocol data, time, etc.). Traditional approaches to identifying malware are based on an analysis of the content of incoming data streams. However, in many areas this procedure is only possible to a limited extent for reasons of data protection or fails because contents are protected against access in encrypted form. In the APT-Sweeper project, on the other hand, the transmission context is analyzed. Because the malware can be hidden in different components of the context, advanced machine learning techniques are combined with approaches to filtering complex data streams. Thus, both the context and the format-specific structure of content in the analysis can be taken into account. In addition, the recognition of alien content is possible without relying on extensive prior knowledge of past attacks.

Tuesday, 11 December, 2018


Pilots for the European Cybersecurity Competence Networks: how can your SME benefit? - Cyberwatching.eu 6th Webinar -

The four pilot projects involved in the development of the European Cybersecurity Competence Network will present their plans and upcoming tools and services for SMEs in the Cyberwatching.eu webinar on the 2nd of April, 10:00 AM CEST



Future Events

Cyber Insurance and its Contribution to Cyber Risk Mitigation - Leiden March 25-29
25/03/2019 to 29/03/2019

The rise in both the scale and severity of recent cyberattacks demands new thinking about cybersecurity risk and the mitigation and transfer of that risk. Cyber insurance is one potential way to manage risk by transferring damage liability, but the cyber insurance market is immature and the understanding and actuarial knowledge of cyber-risk is currently underdeveloped.

e-SIDES workshop 2019

e-SIDES workshop: Towards Value-Centric Big Data: Connect People, Processes and Technology


2 April 2019

10am to 4pm


e-SIDES is a research project funded by European Commission H2020 Programme that deals with the ethical, legal, social and economic implications of privacy-preserving technologies in different big data context.