A Systematic Literature Review and Quality Analysis of Javascript Malware Detection

Abstract

Context: JavaScript (JS) is an often-used programming language by millions of web pages and is also affected by thousands of malicious attacks. Objective: In this investigation, we provided a general view and a quick understanding of JavaScript Malware Detection (JSMD) research reported in the scientific literature from several perspectives. Method: We performed a Systematic Literature Review (SLR) and quality analysis of published research articles on the topic. We investigated 32 articles published between the year 2009 to the year 2019. Results: Selected 32 papers explained in this article reflect the outline of what was published so far. One of our key findings is the performance of Machine Learning (ML) based detection models were relatively higher than others. We also found that only a few papers were able to achieve high scores according to the quality assessment criteria. Conclusion: In this SLR, we summarized and synthesized the existing JSMD studies to identify the previous research practices and also to shed light on future guidelines in the malware detection space. This study will guide and help future researchers to investigate the previous literature efficiently and effectively.

A Systematic Review on Federated Learning in Medical Image Analysis

“…Mainly two SLR methods are popular in practice, one is PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and another is Kitchenham's guidelines; the second one is mainly considered in computer science and software engineering research fields [14]. To conduct this review we followed the PRISMA procedures which is the most common way of performing SLR in the healthcare sector [6].…”

“…Our investigation revealed 14 articles worked on data classification (binary and multi-class), one article worked on data segmentation, and remaining two considered both of them (listed in Table 4). Usually performance of classification tasks is assessed by accuracy, it represents the report of correctly identified samples from all of the data [14]. We divided the performance into three categories according to the achieved accuracy by the 17 studies: high (>=90%), medium (80%-89%), and low (<80%).…”

“…Table 8 shows 12 Quality Questions (QQ) and scores, mostly motivated from our previous article [14]. The goal of such inquiry was to check the basic quality of the articles published in FL oriented medical imaging.…”

Federated Learning: A Cross‐Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery

“…[10][11][12] In conventional deep learning training strategies, the data from multiple institutions must be centralized for model training, but protecting patient privacy after the data are transferred outside their original hospitals is challenging. 13,14 Federated training strategies can be a solution to this concern because they can aggregate data for deep learning model training and preserve patient privacy. 13 Mehta, Manan, and Chenhui Shao.…”

“…13,14 Federated training strategies can be a solution to this concern because they can aggregate data for deep learning model training and preserve patient privacy. 13 Mehta, Manan, and Chenhui Shao. have demonstrated the feasibility of semantic segmentation for FL, which achieved the comparable performance to that of centralized learning (CL), and also outperformed to individual learning.…”