… 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 …

… attack scenario. Specifically, we propose two compatible goals for the query-based attack, ie,
absolute ranking attack and relative ranking attack… assign the specific ranks to chosen candi…

… of attacks where we explicitly target the top-K recommendations; our machine learning approach
… ity of a low-rank recommender to these learned attacks, serving as further motivation for …

… 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 …

… 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 …

… 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 …

… 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 …

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

… [15], many machine learning performance measures are not suitable in the context of security,
since they may provide insufficient estimations or obscure experimental results. In addition…

… a Rank attack named DCB-Attack that targets the latter process in RPL topologies is proposed.
This mechanism uses a trust threshold based on the ranks … , lightweight machine learning-…