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Defense strategies for adversarial machine learning: A survey
Abstract Adversarial Machine Learning (AML) is a recently introduced technique, aiming to
deceive Machine Learning (ML) models by providing falsified inputs to render those models …
deceive Machine Learning (ML) models by providing falsified inputs to render those models …
[HTML][HTML] SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection
Abstract Machine Learning (ML) can be incredibly valuable to automate anomaly detection
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …
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 …
[HTML][HTML] A machine learning and blockchain based efficient fraud detection mechanism
In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These
are common problems in e-banking and online transactions. However, as the financial …
are common problems in e-banking and online transactions. However, as the financial …
Modeling realistic adversarial attacks against network intrusion detection systems
The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …
creating novel defensive opportunities but also new types of risks. Multiple researches have …
TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems
Nowadays, intrusion detection systems based on deep learning deliver state-of-the-art
performance. However, recent research has shown that specially crafted perturbations …
performance. However, recent research has shown that specially crafted perturbations …
Adv-Bot: Realistic adversarial botnet attacks against network intrusion detection systems
Due to the numerous advantages of machine learning (ML) algorithms, many applications
now incorporate them. However, many studies in the field of image classification have …
now incorporate them. However, many studies in the field of image classification have …
[HTML][HTML] Spear siem: A security information and event management system for the smart grid
The technological leap of smart technologies has brought the conventional electrical grid in
a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way …
a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way …
Enhanced intrusion detection systems performance with UNSW-NB15 data analysis
The rapid proliferation of new technologies such as Internet of Things (IoT), cloud
computing, virtualization, and smart devices has led to a massive annual production of over …
computing, virtualization, and smart devices has led to a massive annual production of over …
FGMD: A robust detector against adversarial attacks in the IoT network
Since network intrusion detectors for the Internet of Things (IoT) increasingly rely on
machine learning models, attacks against these detectors are also escalating. Machine …
machine learning models, attacks against these detectors are also escalating. Machine …