a systematic literature review and quality analysis of javascript malware detection

A Systematic Literature Review and Quality Analysis of Javascript Malware Detection

• College of Computer and Information Systems

Umm Al Qura University

Research output : Contribution to journal › Review article › peer-review

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 refiect 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.

• Cybersecurity
• Javascript attacks
• Javascript malware detection
• Malicious code detection
• Systematic literature review

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• Malware Engineering & Materials Science 100%
• Computer programming languages Engineering & Materials Science 27%
• Websites Engineering & Materials Science 26%
• Machine learning Engineering & Materials Science 22%

T1 - A Systematic Literature Review and Quality Analysis of Javascript Malware Detection

AU - Sohan, Md Fahimuzzman

AU - Basalamah, Anas

N1 - Publisher Copyright: © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

N2 - 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 refiect 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.

AB - 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 refiect 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.

KW - Cybersecurity

KW - Javascript attacks

KW - Javascript malware detection

KW - Malicious code detection

KW - Systematic literature review

UR - http://www.scopus.com/inward/record.url?scp=85102811264&partnerID=8YFLogxK

U2 - https://doi.org/10.1109/ACCESS.2020.3031690

DO - https://doi.org/10.1109/ACCESS.2020.3031690

M3 - Review article

SN - 2169-3536

SP - 190539

EP - 190552

JO - IEEE Access

JF - IEEE Access

Android Malware Detection: A Literature Review

• Conference paper
• First Online: 16 February 2023
• Cite this conference paper

• Ahmed Sabbah   ORCID: orcid.org/0000-0001-5034-8038 9 ,
• Adel Taweel   ORCID: orcid.org/0000-0003-0240-9857 9 &
• Samer Zein   ORCID: orcid.org/0000-0003-3720-4384 9  

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1768))

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• International Conference on Ubiquitous Security

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Mobile applications are increasingly being used to support critical domains such as health, logistics, and banking, to name a few. These mobile apps, hence, became a target for malware attackers. Android is an open-source operating system, which runs apps that can be downloaded from official or third-party app stores. Malware exploits these applications to penetrate mobile devices in different ways for different purposes. To address this, different approaches for malware analysis have been proposed for the detection of malware, ranging from pre-installation to post-installation. This paper presents a literature review of recent malware detection approaches and methods. 21 prominent studies, that report three most common approaches, are identified and reviewed. Challenges, limitations, and research directions are identified and discussed. Findings show most studies focus on malware classification and detection, but lack studies that investigate securing apps and detecting vulnerabilities that malware exploits to stealth into mobile apps and devices. They also show that most studies focused on enhancing machine learning models rather than the malware analysis process.

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Ahmed Sabbah, Adel Taweel & Samer Zein

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Sabbah, A., Taweel, A., Zein, S. (2023). Android Malware Detection: A Literature Review. In: Wang, G., Choo, KK.R., Wu, J., Damiani, E. (eds) Ubiquitous Security. UbiSec 2022. Communications in Computer and Information Science, vol 1768. Springer, Singapore. https://doi.org/10.1007/978-981-99-0272-9_18

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A Systematic Literature Review and Quality Analysis of Javascript Malware Detection

• Md. Fahimuzzman Sohan , Anas Basalamah

Published in IEEE Access 2020

• Computer Science

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A systematic literature review on Windows malware detection: Techniques, research issues, and future directions

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The aim of this systematic literature review (SLR) is to provide a comprehensive overview of the current state of Windows malware detection techniques, research issues, and future directions. The SLR was conducted by analyzing scientific literature on Windows malware detection based on executable files (.EXE file format) published between 2009 and 2022. The study presents new insights into the categorization of malware detection techniques based on datasets, features, machine learning and deep learning algorithms. It identifies ten experimental biases that could impact the performance of malware detection techniques. We provide insights on performance evaluation metrics and discuss several research issues that impede the effectiveness of existing techniques. The study also provides recommendations for future research directions and is a valuable resource for researchers and practitioners working in the field of Windows malware detection.

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Electricity theft detection and prevention using technology-based models: a systematic literature review.

1. Introduction

2. literature review, 2.1. the impact of electricity theft on society, 2.2. electricity theft prevention solutions, 3. methodology, 3.1. search for empirical studies, 3.2. quality assessment of articles, 3.3. data extraction and synthesis of results, 4. results and discussion, 4.1. existence of empirical studies to address electricity-related problems, 4.2. the effectiveness of the existing solutions in addressing electricity-related problems, 4.2.1. classification models, 4.2.2. classification with clustering models, 4.2.3. regression models, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Kgaphola, P.M.; Marebane, S.M.; Hans, R.T. Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review. Electricity 2024 , 5 , 334-350. https://doi.org/10.3390/electricity5020017

Kgaphola PM, Marebane SM, Hans RT. Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review. Electricity . 2024; 5(2):334-350. https://doi.org/10.3390/electricity5020017

Kgaphola, Potego Maboe, Senyeki Milton Marebane, and Robert Toyo Hans. 2024. "Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review" Electricity 5, no. 2: 334-350. https://doi.org/10.3390/electricity5020017

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• Review Article
• Published: 31 May 2024

A systematic review and meta-analysis to evaluate the diagnostic accuracy of PSMA PET/CT in the initial staging of prostate cancer

• Andrea Mari   ORCID: orcid.org/0000-0001-9070-5706 1   na1 ,
• Anna Cadenar   ORCID: orcid.org/0000-0001-7782-2318 1   na1 ,
• Sofia Giudici 1 ,
• Gemma Cianchi   ORCID: orcid.org/0009-0000-2835-1345 1 ,
• Simone Albisinni   ORCID: orcid.org/0000-0001-5529-3064 2 ,
• Riccardo Autorino   ORCID: orcid.org/0000-0001-7045-7725 3 ,
• Fabrizio Di Maida   ORCID: orcid.org/0000-0003-1885-4808 1 ,
• Giorgio Gandaglia 4 ,
• M. Carmen Mir   ORCID: orcid.org/0000-0002-1391-1378 5 ,
• Massimo Valerio 6 ,
• Giancarlo Marra 7 ,
• Fabio Zattoni 8 ,
• Lorenzo Bianchi   ORCID: orcid.org/0000-0001-7321-1536 9 ,
• Riccardo Lombardo   ORCID: orcid.org/0000-0003-2890-3159 10 ,
• Shahrokh F. Shariat   ORCID: orcid.org/0000-0002-6627-6179 11 , 12 , 13 , 14 , 15 , 16 , 17 ,
• Morgan Roupret 18 ,
• Matteo Bauckneht   ORCID: orcid.org/0000-0002-1937-9116 19 , 20 ,
• Luca Vaggelli 21 ,
• Cosimo De Nunzio   ORCID: orcid.org/0000-0002-2190-512X 10 &
• Andrea Minervini 1  

Prostate Cancer and Prostatic Diseases ( 2024 ) Cite this article

74 Accesses

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• Cancer screening
• Prostate cancer

Positron Emission Tomography-Computed Tomography using Prostate-Specific Membrane Antigen (PSMA PET/CT) is notable for its superior sensitivity and specificity in detecting recurrent PCa and is under investigation for its potential in pre-treatment staging. Despite its established efficacy in nodal and metastasis staging in trial setting, its role in primary staging awaits fuller validation due to limited evidence on oncologic outcomes. This systematic review and meta-analysis aims to appraise the diagnostic accuracy of PSMA PET/CT compared to CI for comprehensive PCa staging.

Medline, Scopus and Web of science databases were searched till March 2023. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were followed to identify eligible studies. Primary outcomes were specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV) of PSMA PET/CT for local, nodal and metastatic staging in PCa patients. Due to the unavailability of data, a meta-analysis was feasible only for detection of seminal vesicles invasion (SVI) and LNI.

A total of 49 studies, comprising 3876 patients, were included. Of these, 6 investigated accuracy of PSMA PET/CT in detection of SVI. Pooled sensitivity, specificity, PPV and NPV were 42.29% (95%CI: 29.85–55.78%), 87.59% (95%CI: 77.10%–93.67%), 93.39% (95%CI: 74.95%–98.52%) and 86.60% (95%CI: 58.83%–96.69%), respectively. Heterogeneity analysis revealed significant variability for PPV and NPV. 18 studies investigated PSMA PET/CT accuracy in detection of LNI. Aggregate sensitivity, specificity, PPV and NPV were 43.63% (95%CI: 34.19–53.56%), 85.55% (95%CI: 75.95%–91.74%), 67.47% (95%CI: 52.42%–79.6%) and 83.61% (95%CI: 79.19%–87.24%). No significant heterogeneity was found between studies.

Conclusions

The present systematic review and meta-analysis highlights PSMA PET-CT effectiveness in detecting SVI and its good accuracy in LNI compared to CI. Nonetheless, it also reveals a lack of high-quality research on its performance in clinical T staging, extraprostatic extension and distant metastasis evaluation, emphasizing the need for further rigorous studies.

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Data availability.

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These authors contributed equally: Andrea Mari, Anna Cadenar.

Authors and Affiliations

Oncologic Minimally Invasive Urology and Andrology Unit, Department of Experimental and Clinical Medicine, Careggi Hospital, University of Florence, 50121, Florence, Italy

Andrea Mari, Anna Cadenar, Sofia Giudici, Gemma Cianchi, Fabrizio Di Maida & Andrea Minervini

Urology Unit, Department of Surgical Sciences, Tor Vergata University Hospital, University of Rome Tor Vergata, Rome, Italy

Simone Albisinni

Department of Urology, Rush University Medical Center, Chicago, IL, USA

Riccardo Autorino

Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, 20132, Milan, Italy

Giorgio Gandaglia

Department of Urology, Hospital Universitario La Ribera, Valencia, Spain

M. Carmen Mir

Department of Urology, University Hospital of Geneva, Geneva, Switzerland

Massimo Valerio

Division of Urology, Department of Surgical Sciences, University of Turin and Città della Salute e della Scienza, Turin, Italy

Giancarlo Marra

Department Surgery, Oncology and Gastroenterology, Urologic Unit, University of Padova, Padova, Italy

Fabio Zattoni

Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy

Lorenzo Bianchi

Department of Urology, Sant’Andrea Hospital, Sapienza University, Rome, Italy

Riccardo Lombardo & Cosimo De Nunzio

Department of Urology, Medical University of Vienna, Vienna, Austria

Shahrokh F. Shariat

Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria

Department of Urology, University of Texas Southwestern, Dallas, TX, USA

Department of Urology, Weill Cornell Medical College, New York, NY, 10065, USA

Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic

European Association of Urology Research Foundation, Arnhem, The Netherlands

Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordanien

Urology, Predictive Onco-Urology, AP-HP, Urology Hôpital Pitié-Salpêtrière, Sorbonne Université, Paris, France

Morgan Roupret

Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genoa, Italy

Matteo Bauckneht

Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy

Nuclear Medicine Division, Careggi University Hospital, Florence, Italy

Luca Vaggelli

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AM and AC: Conceptualization, data analysis, original draft preparation; SG and GC: Data collection and curation; SA, RA, FDM, GG, MCM, MV, GM, FZ, LB, RL, SFS, MR, MB, LV: Manuscript revision and editing; CDM, AM: Supervision, conceptualization.

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Cosimo De Nunzio, Editor in Chief of prostate Cancer and Prostatic Disease.

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Mari, A., Cadenar, A., Giudici, S. et al. A systematic review and meta-analysis to evaluate the diagnostic accuracy of PSMA PET/CT in the initial staging of prostate cancer. Prostate Cancer Prostatic Dis (2024). https://doi.org/10.1038/s41391-024-00850-y

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Received : 12 February 2024

Revised : 04 May 2024

Accepted : 20 May 2024

Published : 31 May 2024

DOI : https://doi.org/10.1038/s41391-024-00850-y

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