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Found 6 papers, 3 papers with code
Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples
ML, however, is known to be vulnerable to adversarial examples; moreover, as our paper will show, the 5G context is exposed to a yet another type of adversarial ML attacks that cannot be formalized with existing threat models.
The Role of Machine Learning in Cybersecurity
This paper is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain -- to any potential reader with an interest in this topic.
SoK: The Impact of Unlabelled Data in Cyberthreat Detection
A potential solution to this problem are semisupervised learning (SsL) methods, which combine small labelled datasets with large amounts of unlabelled data.
Evasion Attacks against Machine Learning at Test Time
In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data.
Poisoning Attacks against Support Vector Machines
Such attacks inject specially crafted training data that increases the SVM's test error.
Efficient and Accurate Lp-Norm Multiple Kernel Learning
Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations and hence support interpretability.
