… ) attack: a machine learning‐based attack that evaluates the effectiveness of UUP in privacy
protection. QuPiD attack … The results show that the proposed QuPiD attack associates more …

… complicated machine learning models often requires finding a local minimum of non-convex
functions, as exemplified by training deep neural networks and other high-capacity learning …

… of our proposed notion of Certified Top𝐾 Robustness for ranking models to such attacks. …
we do not attack the documents ranked from 1 to 𝐾, since there is no need to attack user’s …

… learning to rank … attack was introduced by [CRR03], but their proposal was limited by the
computational complexity. Very similar to profiled attacks, the application of machine learning …

… learning (FL) allows mutually untrusted clients to collaboratively train a common machine
learning … Under this threat model we design a worst case attack on FRL (Algorithm 3), which …

… , there is an urge to address rank attack immediately. In this paper, a thorough review on the
effects of rank attack and its … It is a machine learning approach. In the proposed technique, …

… His research interests are in natural language processing, machine learning, artificial
intelligence, bioinformatics and applications of AI techniques to computer and network security. He …

… To simulate the evasion attack, we rank each API call and group them into two sets: M (those
highly relevant to malware) and B (those highly relevant to benign files) in the descent …

… machine learning models in a distributed way without the need of data sharing, they can be
susceptible to attacks that … In this paper, we consider attacks on FOLTR systems that aim to …

… the addition of a Machine Learning (ML) module that aims at detecting unknown attacks. ML
algorithms … An important attack that has been studied in this work is the rank attack [13]. In a …