Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Reviewing
Published:Reviewing is fun. Really!
Why did I go for a PhD? (P2)
Published:A reflection spanning between 2013–2016
Inspirational Blogs of Researchers
Published:A collection
Why did I go for a PhD? (P1)
Published:A reflection spanning between 2010–2013
SecNoPageLim: Unlimited pages for Appendices and References
Published:My opinion on why we need them.
publications
[CyCon17] Scalable Architecture for Online Prioritisation of Cyber Threats
Conference Pierazzi, F., Apruzzese, G., Colajanni, M., Guido, A., & Marchetti, M., IEEE International Conference on Cyber Conflict, 2017
Oneliner: My very first paper!
[NCA17] Identifying Malicious Hosts Involved in Periodic Communications
Conference Apruzzese, G., Marchetti, M., Colajanni, M., Zoccoli, G. G., & Guido, A., IEEE International Symposium on Network Computing and Applications, 2017
Oneliner: Use one to find many (apparently, this paper has been integrated into a real SIEM product!)
[TETC17] Detection and Threat Prioritization of Pivoting Attacks in Large Networks
Journal Apruzzese, G., Pierazzi, F., Colajanni, M., & Marchetti, M., IEEE Transactions on Emerging Topics in Computing, 2017
Oneliner: How to detect lateral movement (through pivoting) using Network Flows.
[CyCon18] On the Effectiveness of Machine and Deep Learning for Cyber Security
Conference Apruzzese, G., Colajanni, M. Ferretti, L., Guido, A., & Marchetti, M., IEEE International Conference on Cyber Conflict, 2018
Oneliner: The right paper, at the right time, in the right place?
[NCA18] Evading Botnet Detectors Based on Flows and Random Forest with Adversarial Samples
Conference Apruzzese, G., & Colajanni, M., IEEE International Symposium on Network Computing and Applications [BEST STUDENT PAPER AWARD], 2018
Oneliner: The first paper using adversarial examples against Botnet Detectors (yes, the title has a typo).
[CyCon19] Addressing Adversarial Attacks Against Security Systems based on Machine Learning
Conference Apruzzese, G., Colajanni, M., Ferretti, L., & Marchetti, M., International Conference on Cyber Conflict, 2019
Oneliner: This is not just a review! We also propose an original defense against Poisoning!
[NCA19] Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors
Conference Apruzzese, G., Colajanni, M., & Marchetti, M., IEEE International Symposium on Network Computing and Applications [BEST STUDENT PAPER AWARD], 2019
Oneliner: Previously, in [NCA18], we evaded 1 classifier on 1 dataset. Now, we evade 12 classifiers on 4 datasets!
[Sym20] AppCon: Mitigating Evasion Attacks to ML Cyber Detectors
Journal Apruzzese, G., Andreolini, M., Marchetti, M., Colacino, V. G., & Russo, G., Symmetry, 2020
Oneliner: Ensembling ensembles: each detector focuses on a specific attack against a specific network application!
[TETCI20] Hardening Random Forest Cyber Detectors against Adversarial Attacks
Journal Apruzzese, G., Andreolini, M., Colajanni, M., & Marchetti, M., IEEE Transactions on Emerging Topics in Computational Intelligence, 2020
Oneliner: Applying Defensive Distillation to Random Forest!
[TNSM20] Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks
Journal Apruzzese, G., Andreolini, M., Marchetti, M., Venturi, A., & Colajanni, M., IEEE Transactions on Network and Service Management, 2020
Oneliner: Offense is the best Defense! At little-to-no performance degradation.
[DiB20] DReLAB - Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems
Journal Venturi, A., Apruzzese, G., Andreolini, M., Colajanni, M., & Marchetti, M., Data in Brief, 2021
Oneliner: Dataset, code snippet and tutorial for [TNSM20].
[IM21] Towards an Efficient Detection of Pivoting Activity
Workshop Husák, M., Apruzzese, G., Yang, S. J., & Werner, G., IFIP/IEEE International Symposium on Integrated Network Management, 2021
Oneliner: Uh-oh! It appears that detecting pivoting on external traffic is unfeasible!
[DTRAP21] Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
Journal Apruzzese, G., Andreolini, M., Ferretti, L., Marchetti, M., & Colajanni, M., ACM Digital Threats: Research and Practice, 2021
Oneliner: Using adversarial examples against ML-NIDS is not a feasible strategy.
[ARES21] On the Evaluation of Sequential Machine Learning for Network Intrusion Detection
Conference Corsini, A., Yang, S. J., & Apruzzese, G., International Conference on Availability, Reliability and Security, 2021
Oneliner: Are temporal patterns useful for ML-NIDS? Let's test this out with a fair comparison between LSTM and traditional FNN.
[TNSM22a] The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
Journal Apruzzese, G., Pajola, L., & Conti, M., IEEE Transactions on Network and Service Management, 2022
Oneliner: Let's mix 'n match those datasets!
[DLS22] Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike
Workshop Schneider, J., & Apruzzese, G., IEEE Symposium on Security and Privacy – Deep Learning and Security Workshop, 2022
Oneliner: What's the point of minimal perturbations if we want to fool humans?
[DTRAP22] The Role of Machine Learning in Cybersecurity
Journal Apruzzese, G., Laskov, P., de Oca, E. M., Mallouli, W., Rapa, L. B., Grammatopoulos, A. V., & Franco, F. D., ACM Digital Threats: Research and Practice, 2022
Oneliner: Explaining ML & Cybersecurity in a notation-free way -- a joint effort involving Researchers, Practitioners and Regulatory Bodies.
[EuroSP22] SoK: The Impact of Unlabelled Data in Cyberthreat Detection
Conference Apruzzese, G., Laskov, P., & Tastemirova, A., IEEE European Symposium on Security and Privacy [OUTSTANDING PRESENTATION AWARD], 2022
Oneliner: How to properly evaluate semisupervised learning methods.
[TNSM22b] Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples
Journal Apruzzese, G., Vladimirov, R., Tastemirova, A., & Laskov, P., IEEE Transactions on Network and Service Management, 2022
Oneliner: Introducing the "myopic" threat model for adversarial ML attacks.
[TDSC22] Mitigating Adversarial Gray-Box Attacks against Phishing Detectors
Journal Apruzzese, G., & Subrahmanian, V.S., IEEE Transactions on Dependable and Secure Computing, 2022
Oneliner: A new phishing dataset, and a new defensive mechanism based on feature randomization.
[ACSAC22] SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
Conference Apruzzese, G., Conti, M., & Yuan, Y., Annual Computer Security Applications Conference, 2022
Oneliner: Revisiting adversarial attacks against phishing website detectors—even real ones. (Artifact: Reusable)
[ICSS22] Cybersecurity in the Smart Grid: Practitioners` Perspective
Workshop Meyer, J. & Apruzzese, G., Industrial Control System Security Workshop (co-located with ACSAC), 2022
Oneliner: Elucidating the disconnection between Research and Practice.
[SaTML23] Real Attackers Don`t Compute Gradients: Bridging the Gap Between Adversarial ML Research and Practice
Conference Apruzzese, G., Anderson, H. S., Dambra, S., Freeman, D., Pierazzi, F., & Roundy, K. A., IEEE Conference on Secure and Trustworthy Machine Learning, 2023
Oneliner: Let's change the domain of adversarial ML. For real.
[CODASPY23] Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2
Conference Tricomi, P. P., Facciolo, L., Apruzzese, G., & Conti, M., ACM Conference on Data and Application Security and Privacy, 2023
Oneliner: We discovered a privacy issue affecting millions of video gamers!
[JISA23] Dual Adversarial Attacks: Fooling Humans and Classifiers
Journal Schneider, J., & Apruzzese, G., Journal of Information Security and Applications, 2023
Oneliner: We extend the [DLS22] paper and we also carry out a user-study!
[EuroSP23] SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection
Conference Apruzzese, G., Laskov, P., & Schneider, J., IEEE European Symposium on Security and Privacy, 2023
Oneliner: Changing the evaluation methodology of research papers on ML applications for NIDS.
[ESORICS23] Attacking Logo-based Phishing Website Detectors with Adversarial Perturbations
Conference Lee, J., Xin, Z., Ng. M. P. S., Sabharwal, K., Apruzzese, G., Divakaran. D. M., European Symposium on Research In Computer Security, 2023
Oneliner: A novel attack against state-of-the-art DL methods for logo identification, validated via two user-studies.
[HICSS24] Voices from the Frontline: Revealing the AI Practitioners` viewpoint on the European AI Act
Conference Koh, F., Grosse, K., Apruzzese, G., Hawaii International Conference on System Sciences, 2023
Oneliner: What do AI practitioners think about the European regulation?
talks
Evading Botnet Detectors based on Flows and Random Forest with Adversarial Samples
Published:My first conference presentation!
Cybersecurity & Machine Learning
Published:I briefly presented my research to the other lab members of DSAIL!
Big Data Security Analytics
Published:The beginning of my future…
Evaluating the Effectiveness of Adversarial Attacks against Botnet Detectors
Published:After not even two months, I am back to Boston…
ASGARD Hackatons
Published:An intriguing research project I participated in during my PhD.
Big Data Security Analytics: Opportunities and Issues
Published:Data Analytics and Cybersecurity for dummies.
Cybersecurity: Machine Learning and Industry 5.0
Published:I was the Moderator between Academia and Industry!
Adversarial Attacks against ML Agents
Published:Addressing the resilience of AICA against adversarial ML attacks.
Exposure of 5G Network Infrastructures to Adversarial Examples
Published:Anticipation of the [TNSM22b] paper at Huawei!
The relationship between Machine Learning & Cybersecurity
Published:Teaching some MSc. students the link between ML and Cybersecurity
Some Pragmatic Relationships between Machine Learning & Cybersecurity
Published:Anticipation of [DLS22] and [EuroSP22] @ TU Delft!
Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike
Published:The only presentation done physically at [DLS22]!
SoK: The Impact of Unlabelled Data in Cyberthreat Detection
Published:Once upon a time…
So good that it is bad. On the (re)use of Datasets in Machine Learning Security
Published:A very negative (informal) talk!
Cybersecurity and Machine Learning: Facts and Myths
Published:Going back (close) to my origin!
Doing Practical Research on Machine Learning & Cybersecurity
Published:Revealing some overlooked aspects of ML & Cybersecurity research
Cybersecurity in the Smart Grid: Practitioners` Perspective
Published:These findings are thanks to an excellent BSc. student.
SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
Published:A joint effort with UniPD, casting light on some overlooked aspects of adversarial ML in the context of phishing website detection.
Real Attackers Don`t Compute Gradients: Bridging the Gap Between Adversarial ML Research and Practice
Published:Besides the content of the paper, the talk has a meta-message.
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection
Published:Revisiting ML in Network Intrusion Detection