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Why did I go for a PhD? (P2)

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Why did I go for a PhD? (P1)

SecNoPageLim: Unlimited pages for Appendices and References

[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., Marchetti, M., Colajanni, M., Zoccoli, G. G., 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!

Evading Botnet Detectors based on Flows and Random Forest with Adversarial Samples

Cybersecurity & Machine Learning

Big Data Security Analytics

Evaluating the Effectiveness of Adversarial Attacks against Botnet Detectors

ASGARD Hackatons

Big Data Security Analytics: Opportunities and Issues

Cybersecurity: Machine Learning and Industry 5.0

Adversarial Attacks against ML Agents

Exposure of 5G Network Infrastructures to Adversarial Examples

The relationship between Machine Learning & Cybersecurity

Some Pragmatic Relationships between Machine Learning & Cybersecurity

Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike

SoK: The Impact of Unlabelled Data in Cyberthreat Detection

So good that it is bad. On the (re)use of Datasets in Machine Learning Security

Cybersecurity and Machine Learning: Facts and Myths

Doing Practical Research on Machine Learning & Cybersecurity

Cybersecurity in the Smart Grid: Practitioners` Perspective

SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning

Real Attackers Don`t Compute Gradients: Bridging the Gap Between Adversarial ML Research and Practice