The proliferation of ransomware has become a significant threat to cybersecurity in recent years, causing significant financial, reputational, and operational damage to individuals and organizations. This paper aims to provide a comprehensive overview of the evolution of ransomware, its taxonomy, and its state-of-the-art research contributions. We begin by tracing the origins of ransomware and its evolution over time, highlighting the key milestones and major trends. Next, we propose a taxonomy of ransomware that categorizes different types of ransomware based on their characteristics and behavior. Subsequently, we review the existing research over several years in regard to detection, prevention, mitigation, and prediction techniques. Our extensive analysis, based on more than 150 references, has revealed that significant research, specifically 72.8%, has focused on detecting ransomware. However, a lack of emphasis has been placed on predicting ransomware. Additionally, of the studies focused on ransomware detection, a significant portion, 70%, have utilized Machine Learning methods. We further discuss the challenges found such as the ones related to obtaining ransomware datasets. In addition, our study uncovers a range of shortcomings in research pertaining to real-time protection and identifying zero-day ransomware. Adversarial machine learning exploitation has been identified as an under-researched area in the field. This survey is a constructive roadmap for researchers interested in ransomware research matters.

Facility Location problems are one of the most prominent optimization problems in computer science, operations research, and combinatorial optimization. Their simple yet intrinsic structure has led to their widespread application in diverse fields, such as leasing, clustering, and urban planning. In facility location problems, potential sites for opening facilities, like warehouses, each have an associated opening cost. Client locations, such as stores, require service from a facility, incurring a cost proportional to their distance from them when assigned. Identifying optimal facility locations to minimize total costs associated with opening facilities and client assignment is the aim. This paper focuses on the online setting, in which client locations are revealed to the algorithm incrementally, unlike the classical offline setting, in which all client locations are given in advance. In this dynamic context, often referred to as the online algorithm scenario, immediate decisions are made to determine whether the newly disclosed client should be assigned to an existing facility or necessitate the establishment of a new facility. To gauge the effectiveness of online algorithms, the concept of competitive analysis is employed. This approach assesses the worst-case performance of online algorithms by comparing them to an optimal algorithm equipped with complete knowledge of forthcoming input sequences. Despite the considerable significance of the Facility Location problem and the substantial body of literature on it within the online algorithm framework, this article represents the first comprehensive summary of these research findings. It explores the algorithmic techniques employed, delves into practical applications, and identifies pertinent research gaps within this domain.