Review of cyberattack implementation, detection, and mitigation methods in cyber-physical systems

N Mtukushe, AK Onaolapo, A Aluko, DG Dorrell - Energies, 2023‏ - mdpi.com
With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including
critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing …

The role of machine learning in cybersecurity

G Apruzzese, P Laskov, E Montes de Oca… - … Threats: Research and …, 2023‏ - dl.acm.org
Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

APT beaconing detection: A systematic review

MA Talib, Q Nasir, AB Nassif, T Mokhamed… - Computers & …, 2022‏ - Elsevier
Abstract Advanced Persistent Threat (APT) is a type of threat that has grabbed the attention
of researchers, particularly in the industrial security field. APTs are cyber intrusions carried …

On the effectiveness of machine and deep learning for cyber security

G Apruzzese, M Colajanni, L Ferretti… - … conference on cyber …, 2018‏ - ieeexplore.ieee.org
Machine learning is adopted in a wide range of domains where it shows its superiority over
traditional rule-based algorithms. These methods are being integrated in cyber detection …

The cross-evaluation of machine learning-based network intrusion detection systems

G Apruzzese, L Pajola, M Conti - IEEE Transactions on Network …, 2022‏ - ieeexplore.ieee.org
Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …

Sok: Pragmatic assessment of machine learning for network intrusion detection

G Apruzzese, P Laskov… - 2023 IEEE 8th European …, 2023‏ - ieeexplore.ieee.org
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 …

Addressing adversarial attacks against security systems based on machine learning

G Apruzzese, M Colajanni, L Ferretti… - … conference on cyber …, 2019‏ - ieeexplore.ieee.org
Machine-learning solutions are successfully adopted in multiple contexts but the application
of these techniques to the cyber security domain is complex and still immature. Among the …

Evading botnet detectors based on flows and random forest with adversarial samples

G Apruzzese, M Colajanni - 2018 IEEE 17th International …, 2018‏ - ieeexplore.ieee.org
Machine learning is increasingly adopted for a wide array of applications, due to its
promising results and autonomous capabilities. However, recent research efforts have …

Evaluating the effectiveness of adversarial attacks against botnet detectors

G Apruzzese, M Colajanni… - 2019 IEEE 18th …, 2019‏ - ieeexplore.ieee.org
Classifiers based on Machine Learning are vulnerable to adversarial attacks, which involve
the creation of malicious samples that are not classified correctly. While this phenomenon …

On the evaluation of sequential machine learning for network intrusion detection

A Corsini, SJ Yang, G Apruzzese - Proceedings of the 16th International …, 2021‏ - dl.acm.org
Recent advances in deep learning renewed the research interests in machine learning for
Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to …