MLBDACP 2021 - The Third International Symposium on Machine Learning and Big Data Analytics For Cybersecurity and Privacy

As cyber-attacks against critical infrastructure increase and evolve, automated systems to complement human analysis are needed. Moreover, chasing the breaches is like looking for a needle in a haystack. Such organizations are so large, with so much information and data to sort through to obtain actionable information that it seems impossible to know where to start. The analysis of the intelligence of an attack is traditionally an iterative, mainly manual process, which involves an unlimited amount of data to try to determine the sophisticated patterns and behaviours of intruders. In addition, most of the detected intrusions provide a limited set of attributes on a single phase of an attack. Accurate and timely knowledge of all stages of an intrusion would allow us to support our cyber-detection and prevention capabilities, enhance our information on cyber-threats and facilitate the immediate sharing of information on threats, as we share several elements. The workshop is expected to address the above issues and will aim to present new research in the field of cyber threat hunting, information on cyber threats and analysis of important data.
Therefore, cyber-attacks protection of computer systems is one of the most important cybersecurity tasks for single users and businesses, since even a single attack can result in compromised data and sufficient losses. Massive losses and frequent attacks dictate the need for accurate and timely detection methods. Current static and dynamic methods do not provide efficient detection, especially when dealing with zero-day attacks. For this reason, Big Data Analytics and machine learning-based techniques can be used.
This workshop aims to bring together researchers in the field of cybersecurity and key data to advance the missions of anticipating, prohibiting, preventing, preparing and responding to internal security. We are soliciting submissions in areas related to knowledge extraction from key cybersecurity intelligence datasets, rapid analysis of security datasets to obtain relevant information and Machine learning techniques for cyber-attacks detection and prevention.