Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201

Survey on Ranking Fraud for Mobile Apps

In today's world there are many fraud ways through which app developers try to put their app at the first position. The developers try hard to configure the positions of various apps in the list of apps in that particular area. Mobile phones operating system is developing day by day but research in fraud apps is limited or not much discovered. Fraud ranking in mobile phones lead to download of the false app which allows damaging the mobile phones and falsely getting famous by that false apps. Fraud ranking in mobile phones is very important and this paper shows the misinterpretation of the apps information and configured apps position. Also a framework is used for fraud detection in apps. The work is grouped basically into three categories. First is web ranking spam detection, second is the online review spam detection and third one is mobile app recommendation. The first method Web ranking spam refers to any kind of actions which bring to selected Web pages an unjustifiable favorable relevance or give much importance. The second one is Review spam which is designed to give unfair view of some objects so as to influence the consumers' perception of the objects by directly or indirectly damaging the object's reputation. The third one is mobile app recommendation which tells users to check the app usage record