272 citations · 774 across the 19 of their papers we have counts for
10 papers · 1 filter
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra +3
Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learn…
Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors
Giovanni Apruzzese, V. S. Subrahmanian
Although machine learning based algorithms have been extensively used for detecting phishing websites, there has been relatively little work on how adversaries may attack such "phi…
Cybersecurity in the Smart Grid: Practitioners' Perspective
Jacqueline Meyer, Giovanni Apruzzese
The Smart Grid (SG) is a cornerstone of modern society, providing the energy required to sustain billions of lives and thousands of industries. Unfortunately, as one of the most cr…
Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
Ying Yuan, Giovanni Apruzzese, Mauro Conti
Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model, or defenses that withstand most attacks. Unfortunately, little…
Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2
Pier Paolo Tricomi, Lisa Facciolo, Giovanni Apruzzese +1
Did you know that over 70 million of Dota2 players have their in-game data freely accessible? What if such data is used in malicious ways? This paper is the first to investigate su…
Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples
Giovanni Apruzzese, Rodion Vladimirov, Aliya Tastemirova +1
Fifth Generation (5G) networks must support billions of heterogeneous devices while guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet with hum…
The Role of Machine Learning in Cybersecurity
Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de Oca +4
Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of M…
SoK: The Impact of Unlabelled Data in Cyberthreat Detection
Giovanni Apruzzese, Pavel Laskov, Aliya Tastemirova
Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of sp…
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…