[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!
Conference Pierazzi, F., Apruzzese, G., Colajanni, M., Guido, A., & Marchetti, M., IEEE International Conference on Cyber Conflict, 2017
Oneliner: My very first paper!
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!)
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.
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?
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).
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!
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!
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!
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!
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.
Journal Venturi, A., Apruzzese, G., Andreolini, M., Colajanni, M., & Marchetti, M., Data in Brief, 2021
Oneliner: Dataset, code snippet and tutorial for [TNSM20].
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!
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.
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.
Journal Apruzzese, G., Pajola, L., & Conti, M., IEEE Transactions on Network and Service Management, 2022
Oneliner: Let's mix 'n match those datasets!
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?
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.
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.
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.
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.
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)
Workshop Meyer, J. & Apruzzese, G., Industrial Control System Security Workshop (co-located with ACSAC), 2022
Oneliner: Elucidating the disconnection between 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.
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!
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!
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.
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.
Conference Koh, F., Grosse, K., Apruzzese, G., Hawaii International Conference on System Sciences, 2023
Oneliner: What do AI practitioners think about the European regulation?
My first conference presentation!
I briefly presented my research to the other lab members of DSAIL!
The beginning of my future…
After not even two months, I am back to Boston…
An intriguing research project I participated in during my PhD.
Data Analytics and Cybersecurity for dummies.
I was the Moderator between Academia and Industry!
Addressing the resilience of AICA against adversarial ML attacks.
Anticipation of the [TNSM22b] paper at Huawei!
Teaching some MSc. students the link between ML and Cybersecurity
Anticipation of [DLS22] and [EuroSP22] @ TU Delft!
The only presentation done physically at [DLS22]!
Once upon a time…
A very negative (informal) talk!
Going back (close) to my origin!
Revealing some overlooked aspects of ML & Cybersecurity research
These findings are thanks to an excellent BSc. student.
A joint effort with UniPD, casting light on some overlooked aspects of adversarial ML in the context of phishing website detection.
Besides the content of the paper, the talk has a meta-message.
Revisiting ML in Network Intrusion Detection