272 citations · 774 across the 19 of their papers we have counts for
19 papers
"What is the Problem Space?" Defining Host-space Adversarial Perturbations against Network Intrusion Detection Systems
Miel Verkerken, Laurens D'hooge, Bruno Volckaert +2
Network Intrusion Detection Systems (NIDS) are now increasingly leveraging Machine Learning (ML) techniques to detect malicious network activities. Numerous papers have scrutinized…
SoK: Reshaping Research on Network Intrusion Detection Systems
Giovanni Apruzzese
Network Intrusion Detection Systems (NIDS) have been studied for decades. Hundreds of papers have, e.g., proposed ways to enhance, harden or bypass NIDS. However, the findings of p…
ConCap: Practical Network Traffic Generation for (ML- and) Flow-based Intrusion Detection Systems
Miel Verkerken, Laurens D'hooge, Bruno Volckaert +2
Network Intrusion Detection Systems (NIDS) have been studied in research for almost four decades. Yet, despite thousands of papers claiming scientific advances, a non-negligible nu…
The Ephemeral Threat: Assessing the Security of Algorithmic Trading Systems powered by Deep Learning
Advije Rizvani, Giovanni Apruzzese, Pavel Laskov
We study the security of stock price forecasting using Deep Learning (DL) in computational finance. Despite abundant prior research on the vulnerability of DL to adversarial pertur…
Attacking logo-based phishing website detectors with adversarial perturbations
Jehyun Lee, Zhe Xin, Melanie Ng Pei See +3
Recent times have witnessed the rise of anti-phishing schemes powered by deep learning (DL). In particular, logo-based phishing detectors rely on DL models from Computer Vision to…
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection
Giovanni Apruzzese, Pavel Laskov, Johannes Schneider
Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For Network Intrusion Detection (NID), however, scientific advances in ML are still seen with skep…
"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…
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…
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection
Andrea Corsini, Shanchieh Jay Yang, Giovanni Apruzzese
Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequen…
Hardening Random Forest Cyber Detectors Against Adversarial Attacks
Giovanni Apruzzese, Mauro Andreolini, Michele Colajanni +1
Machine learning algorithms are effective in several applications, but they are not as much successful when applied to intrusion detection in cyber security. Due to the high sensit…