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Review of cyberattack implementation, detection, and mitigation methods in cyber-physical systems
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 …
critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing …
The role of machine learning in cybersecurity
Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …
systems, and many domains already leverage the capabilities of ML. However, deployment …
APT beaconing detection: A systematic review
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 …
of researchers, particularly in the industrial security field. APTs are cyber intrusions carried …
On the effectiveness of machine and deep learning for cyber security
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 …
traditional rule-based algorithms. These methods are being integrated in cyber detection …
The cross-evaluation of machine learning-based network intrusion detection systems
Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …
Sok: Pragmatic assessment of machine learning for network intrusion detection
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 …
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
Addressing adversarial attacks against security systems based on machine learning
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 …
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
Machine learning is increasingly adopted for a wide array of applications, due to its
promising results and autonomous capabilities. However, recent research efforts have …
promising results and autonomous capabilities. However, recent research efforts have …
Evaluating the effectiveness of adversarial attacks against botnet detectors
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 …
the creation of malicious samples that are not classified correctly. While this phenomenon …
On the evaluation of sequential machine learning for network intrusion detection
Recent advances in deep learning renewed the research interests in machine learning for
Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to …
Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to …