Cyber Security Webinar by Dr. Giovanni Apruzzese – Some pragmatic relationships between Machine Learning and Cybersecurity

The first involves adversarial attacks targeting both humans and Machine Learning (ML). The intuition is to generate adversarial samples by "shifting" an original sample towards a target sample, so that humans can perceive some difference between the original and the adversarial sample. Such assumptions are in stark contrast to common attacks involving perturbations of single pixels that are not recognizable by humans. This approach is relevant in, e.g., multi-stage processing of inputs, where both humans and machines are involved in decision-making because invisible perturbations will not fool a human.

The latter analyzes the impact of unlabelled data in cyberthreat detection (CTD). Despite abundant efforts propose to use ML to solve CTD problems, the realistic integration of ML methods is hindered by the difficulty in obtaining the large sets of labelled data to train ML detectors. A potential solution to this problem are semisupervised learning (SsL) methods, which combine small labelled datasets with large amounts of unlabelled data. In this talk, I investigate the utility of unlabelled data by (i) proposing a formal cost model for SsL in CTD; and (ii) formalizing a set of requirements for evaluation of SsL methods, which elucidates the contribution of unlabelled data. I will then show that the state-of-the-art does not allow to assess the impact of unlabelled data in CTD. By performing some experiments, I will then demonstrate "how" to empirically assess the role played by unlabelled data in SsL methods for CTD.

Giovanni Apruzzese is a Post-Doctoral researcher within the Institute of Information Systems at the University of Liechtenstein since 2020. He received the PhD Degree and the Master's Degree in Computer Engineering (summa cum laude) in 2020 and 2016 respectively at the University of Modena, Italy. In 2019 he spent 6 months as a Visiting Researcher at Dartmouth College (Hanover, NH, USA) under the supervision of Prof. VS Subrahmanian. His research interests involve all aspects of big data security analytics with a focus on machine learning, and his main expertise lies in the analysis of Network Intrusions, Phishing, and Adversarial Attacks.