Mahdi Abadi

Zero-day malware is malware that has never been seen before or is so new that no anti-malware software can catch it. This novelty and the lack of existing mitigation strategies make zero-day malware challenging to detect and defend against. In recent years, deep learning has become the dominant and leading branch of machine learning in various research fields, including malware detection. Considering the significant threat of zero-day malware to cybersecurity and business continuity, it is necessary to identify deep learning techniques that can somehow be effective in detecting or classifying such malware. But so far, such a comprehensive review has not been conducted. In this article, we study deep learning techniques in terms of their ability to detect or classify zero-day malware. Based on our findings, we propose a taxonomy and divide different zero-day resistant, deep malware detection and classification techniques into four main categories: unsupervised, semi-supervised, few-shot, and adversarial resistant. We compare the techniques in each category in terms of various factors, including deep learning architecture, feature encoding, platform, detection or classification functionality, and whether the authors have performed a zero-day evaluation. We also provide a summary view of the reviewed papers and discuss their main characteristics and challenges.

Moving objects databases have become an enabling technology for location-based applications. They mostly focus on the storing and processing of data about moving objects. Privacy protection is one of the most important concerns related to such databases. In recent years, some mechanisms have been proposed to answer statistical queries over moving objects databases, while satisfying differential privacy. However, none of them consider the case where a moving objects database contains non-spatiotemporal sensitive attributes other than spatiotemporal attributes. Besides, most of them do not support the personalized privacy protection requirements of different moving objects. In this paper, we address these problems by presenting PDP-SAG, a differentially private mechanism that combines the sensitive attribute generalization with personalized privacy in a unified manner. By this combination, we aim to provide different levels of differential privacy protection for moving objects that have non-spatiotemporal sensitive attributes as well. In this regard, we generalize the sensitive attribute values of trajectory data records based on their privacy descriptor and define a new personalized differentially private tree structure to keep different noisy frequencies for each trajectory according to the generalized sensitive attribute values of trajectory data records passing through that trajectory. We also propose intra-and inter-consistency constraints enforcements to make noisy frequencies consistent with each other. Extensive experiments on synthetic and real datasets verify that PDP-SAG significantly improves the utility of sensitive query answers and provides the required level of privacy protection for each moving object, in comparison to the case when no personalization and generalization are permitted.