ACM Computing Surveys最新文献

Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to disassemble binaries to obtain authorship-related features. We perform a systematic analysis of works in the area of malware authorship attribution. We identify key findings, some shortcomings of current approaches and explore the open research challenges. To mitigate the lack of ground truth datasets in this domain, we publish alongside this survey the largest and most diverse meta-information dataset of 17,513 malware labeled to 275 threat actor groups.

Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.

The Densest Subgraph Problem requires to find, in a given graph, a subset of vertices whose induced subgraph maximizes a measure of density. The problem has received a great deal of attention in the algorithmic literature over the last five decades, with many variants proposed and many applications built on top of this basic definition. Recent years have witnessed a revival of research interest in this problem with several important contributions, including some groundbreaking results, published in 2022 and 2023. This survey provides a deep overview of the fundamental results and an exhaustive coverage of the many variants proposed in the literature, with a special attention to the most recent results. The survey also presents a comprehensive overview of applications and discusses some interesting open problems for this evergreen research topic.

Knowing the exploitability and severity of software vulnerabilities helps practitioners prioritize vulnerability mitigation efforts. Researchers have proposed and evaluated many different exploitability assessment methods. The goal of this research is to assist practitioners and researchers in understanding existing methods for assessing vulnerability exploitability through a survey of exploitability assessment literature. We identify three exploitability assessment approaches: assessments based on original, manual CVSS, automated Deterministic assessments, and automated Probabilistic assessments. Other than the original CVSS, the two most common subcategories are Deterministic, Program-State-Based, and Probabilistic Learning Model (LM) Assessments.

Minimization of energy consumption is the main attention of researchers while developing a routing protocol for wireless sensor networks (WSNs), since sensor nodes are equipped with limited power supply. Virtual topology is an integral part of routing, and grid-based routing protocols are very popular due to their simplified and efficient virtual grid topology construction. Although a list of surveys exists that either focus on routing protocols in a broader way or on other virtual topologies, none of these surveys are concerned about grid topology and grid-based routing protocols. Having this motivation in mind and considering the impact of these routing protocols on controlling and managing the energy consumption of WSNs, this paper provides an expansive assessment of grid-based routing protocols based on their methodology. In this survey, the existing grid-based routing protocols are classified into two categories from the perspective of sink mobility, i.e., static sink- and mobile sink-based grid routing protocols. A phase-wise comprehensive overview of these routing protocols is provided in chronological order, along with their comparative analysis. Furthermore, the characteristics, design issues, and challenges of grid-based routing protocols are also provided.

One of the challenges in the Comparative Genomics field is to infer how close two organisms are based on the similarities and differences between their genetic materials. Recent advances in DNA sequencing have made complete genomes increasingly available. That said, several new algorithms trying to infer the distance between two organisms based on genome rearrangements have been proposed in the literature. However, given the diversity of approaches, the diversity of genome rearrangement events, or even how each work models the genomes and what assumptions are made by each of them, finding the ideal algorithm for each situation or simply knowing the range of applicable approaches can be challenging. In this work, we review these approaches having the algorithmic and combinatorial advances since 2010 as our main focus. This survey aims to organize the recently published papers using a concise notation and to indicate the gaps filled by each of them in the literature. This makes it easier to understand what still needs to be done and what has room for enhancement.

Existing surveys and reviews on Influential Node Ranking Methods (INRMs) have primarily focused on technical details, neglecting thorough research on verifying the actual influence of these nodes in a network. This oversight may result in erroneous rankings. In this survey, we address this gap by conducting an extensive analysis of 82 primary studies related to INRMs based on the epidemic model over the past 20 years. We statistically analyze and categorize benchmark networks into four types, and conclude that high-quality networks with complete information are insufficient and most INRMs only focus on undirected and unweighted networks, which encourages collaboration between industry and academia to provide optimized networks. Additionally, we categorize and discuss the strengths, weaknesses, and optimized crafts of seven categories of INRMs, helping engineers and researchers narrow down their choices when selecting appropriate INRMs for their specific needs. Furthermore, we define the Capability and Correctness metrics and utilize their usage frequency and functionality to assist engineers and researchers in prioritizing and selecting suitable metrics for different INRMs and networks. To our knowledge, this is the first survey that systematically summarizes the Capability and Correctness of INRMs, providing insights for the complex network community and aiding INRM selection and evaluation.

Building a secure and privacy-preserving health data sharing framework is a topic of great interest in the healthcare sector, but its success is subject to ensuring the privacy of user data. We clarified the definitions of privacy, confidentiality and security (PCS) because these three terms have been used interchangeably in the literature. We found that researchers and developers must address the differences of these three terms when developing electronic health record (EHR) solutions. We surveyed 130 studies on EHRs, privacy-preserving techniques, and tools that were published between 2012 and 2022, aiming to preserve the privacy of EHRs. The observations and findings were summarized with the help of the identified studies framed along the survey questions addressed in the literature review. Our findings suggested that the usage of access control, blockchain, cloud-based, and cryptography techniques is common for EHR data sharing. We summarized the commonly used strategies for preserving privacy that are implemented by various EHR tools. Additionally, we collated a comprehensive list of differences and similarities between PCS. Finally, we summarized the findings in a tabular form for all EHR tools and techniques and proposed a fusion of techniques to better preserve the PCS of EHRs.

Concerns for the resilience of Cyber-Physical Systems (CPS) in critical infrastructure are growing. CPS integrate sensing, computation, control, and networking into physical objects and mission-critical services, connecting traditional infrastructure to internet technologies. While this integration increases service efficiency, it has to face the possibility of new threats posed by the new functionalities. This leads to cyber-threats, such as denial-of-service, modification of data, information leakage, spreading of malware, and many others. Cyber-resilience refers to the ability of a CPS to prepare, absorb, recover, and adapt to the adverse effects associated with cyber-threats, e.g., physical degradation of the CPS performance resulting from a cyber-attack. Cyber-resilience aims at ensuring CPS survival, by keeping the core functionalities of the CPS in case of extreme events. The literature on cyber-resilience is rapidly increasing, leading to a broad variety of research works addressing this new topic. In this article, we create a systematization of knowledge about existing scientific efforts of making CPS cyber-resilient. We systematically survey recent literature addressing cyber-resilience with a focus on techniques that may be used on CPS. We first provide preliminaries and background on CPS and threats, and subsequently survey state-of-the-art approaches that have been proposed by recent research work applicable to CPS. In particular, we aim at differentiating research work from traditional risk management approaches, based on the general acceptance that it is unfeasible to prevent and mitigate all possible risks threatening a CPS. We also discuss questions and research challenges, with a focus on the practical aspects of cyber-resilience, such as the use of metrics and evaluation methods, as well as testing and validation environments.

Mobile near-infrared sensing is becoming an increasingly important method in many research and industrial areas. To help consolidate progress in this area, we use the PRISMA guidelines to conduct a systematic review of mobile near-infrared sensing, including 1) existing prototypes and commercial products; 2) data collection techniques; 3) machine learning methods; 4) relevant application areas. Our work measures historical and current trends, and identifies current challenges and future directions for this emerging topic.