144 citations · 273 across the 3 of their papers we have counts for
3 papers
Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike
Johannes Schneider, Giovanni Apruzzese
We propose to generate adversarial samples by modifying activations of upper layers encoding semantically meaningful concepts. The original sample is shifted towards a target sampl…
The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
Giovanni Apruzzese, Luca Pajola, Mauro Conti
Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where benign a…
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
Giovanni Apruzzese, Mauro Andreolini, Luca Ferretti +2
The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have…