Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However …
The majority of machine learning methodologies operate with the assumption that their environment is benign. However, this assumption does not always hold, as it is often …
B Biggio, F Roli - Proceedings of the 2018 ACM SIGSAC Conference on …, 2018 - dl.acm.org
Deep neural networks and machine-learning algorithms are pervasively used in several applications, ranging from computer vision to computer security. In most of these …
N Liu, H Yang, X Hu - Proceedings of the 24th ACM SIGKDD …, 2018 - dl.acm.org
Machine learning (ML) systems have been increasingly applied in web security applications such as spammer detection, malware detection and fraud detection. These applications …
Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover …
Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test …
Machine Learning (ML) algorithms, specifically supervised learning, are widely used in modern real-world applications, which utilize Computational Intelligence (CI) as their core …
U Jang, X Wu, S Jha - Proceedings of the 33rd Annual Computer …, 2017 - dl.acm.org
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms are being used in diverse domains where security is a concern, such as, automotive …