Biblio

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 many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider realistic types of poisoning and evasion attacks targeting security solutions devoted to malware, spam and network intrusion detection. We explore the possible damages that an attacker can cause to a cyber detector and present some existing and original defensive techniques in the context of intrusion detection systems. This paper contains several performance evaluations that are based on extensive experiments using large traffic datasets. The results highlight that modern adversarial attacks are highly effective against machine-learning classifiers for cyber detection, and that existing solutions require improvements in several directions. The paper paves the way for more robust machine-learning-based techniques that can be integrated into cyber security platforms.

Delegated authorization protocols have become wide-spread to implement Web applications and services, where some popular providers managing people identity information and personal data allow their users to delegate third party Web services to access their data. In this paper, we analyze the risks related to untrusted providers not behaving correctly, and we solve this problem by proposing the first verifiable delegated authorization protocol that allows third party services to verify the correctness of users data returned by the provider. The contribution of the paper is twofold: we show how delegated authorization can be cryptographically enforced through authenticated data structures protocols, we extend the standard OAuth2 protocol by supporting efficient and verifiable delegated authorization including database updates and privileges revocation.