… Twenty-three features are collected and ranking the twenty-three features is done with
Information Gain and Chi-Square statistic which reduces the number of features to eight. All the …

… We focus on targeted poisoning attacks against machine learning classifiers. In this setting,
we refer to the victim classifier as Alice, the owner of the target instance as Bob, and the …

… machine learning methods to propose attacks on factorization-based recommendation
systems, which applies the adversarial learning … an adversarial attack for text ranking models. …

… or one type of attack, this paper covers all the aspects of machine learning security from
the … The technique first uses low-rank matrix factorization then uses principle component …

… Anomaly detection models for RPL can be developed using a variety of methods based on
machine learning models, Z-score value analysis, probabilistic or statistical modelling, linear …

… -parent attacks of RPL. Novelty of our approach is that, in addition to detecting the attack, the
… is used to identify its locality, using Machine Learning (ML) classification at the edge of the …

… framework for target attacks on pairwise ranking. Specifically, … attacks against pairwise
ranking algorithms. The framework consists of goal of the adversary, knowledge of the attacked …

… intersection of machine learning and security: training-set attacks on machine learners. In
such … First we show that optimal training-set attack can be formulated as a bilevel optimization …

… 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 …