Towards a systematic description of the field using bibliometric analysis: malware evolution

Knowledge management and performance: a bibliometric analysis based on Scopus and WOS data (1988–2021)

Purpose

This study aims to analyze the trends manifested in literature from the area of knowledge management and performance, with emphasis on bibliometric analysis.

Design/methodology/approach

To explore the studies focused on the area under investigation, the authors performed a search in ISI Web of Science and Scopus using the combination of keywords such as “Knowledge management” AND “Performance.” Generally, this study covered a period of 33 years, from 1988 to 2021 because the first study was published in 1970 and the databases have not covered all the journals and studies which date back to the early 1970s or 1980s. The final data set comprised 1,583 publications with 40 articles removed during the screening and eligibility process.

Findings

The results of the bibliometric analysis indicate that the interest in the area of knowledge management and performance has significantly increased, especially from 2000 to 2021. The application of bibliometric analysis on the relationship between knowledge management and performance uncovered various themes, productive authors and widely cited documents. The study highlighted how the knowledge management–performance relationship has evolved over the years and how the interplay between knowledge management and performance may help the firms in gaining the sustainable competitive advantage.

Originality/value

To the best of the author’s knowledge, this study is the first of its kind to conduct the bibliometric analysis on knowledge management and performance. This study can be a starting point for scholars interested in understanding how knowledge management is related to performance.

The Malware Detection Approach in the Design of Mobile Applications

Background: security has become a major concern for smartphone users in line with the increasing use of mobile applications, which can be downloaded from unofficial sources. These applications make users vulnerable to penetration and viruses. Malicious software (malware) is unwanted software that is frequently used by cybercriminals to launch cyber-attacks. Therefore, the motive of the research was to detect malware early before infection by discovering it at the application-design level and not at the code level, where the virus will have already damaged the system. Methods: in this article, we proposed a malware detection method at the design level based on reverse engineering, the unified modeling language (UML) environment, and the web ontology language (OWL). The proposed method detected “Data_Send_Trojan” malware by designing a UML model that simulated the structure of the malware. Then, by generating the ontology of the model, and using RDF query language (SPARQL) to create certain queries, the malware was correctly detected. In addition, we proposed a new classification of malware that was suitable for design detection. Results: the proposed method detected Trojan malware that appeared 552 times in a sample of 600 infected android application packages (APK). The experimental results showed a good performance in detecting malware at the design level with precision and recall of 92% and 91%, respectively. As the dataset increased, the accuracy of detection increased significantly, which made this methodology promising.

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.