A Semi-automated Approach for Identification of Trends in Android Ransomware Literature

Abstract

Android ransomware is seen in the highlights of cyber security world reports. Ransomware is considered to be the most popular as well as threatening mobile malware. These are specsial malware used to extort money in return of access and data without user’s consent. The exponential growth in mobile transactions from 9.47 crore in 2013–14 to 72 crores in 2016–17 could be a potential motivation for numerous ransomware attacks in the recent past. Attackers are consistently working on producing advanced methods to deceit the victim and generate revenue. Therefore, study of Android stealth malware, its detection and analysis gained a substantial interest among researchers, thereby producing sufficiently large body of literature in a very short period. Manual reviews do provide insight but they are prone to be biased, time consuming and pose a great challenge on number of articles that needs investigation. This study uses Latent Semantic Analysis (LSA), an information modelling technique to deduce core research areas, research trends and widely investigated areas within corpus. This work takes a large corpus of 487 research articles (published during 2009–2019) as input and produce three core research areas and thirty emerging research trends in field of stealth malwares as primary goal. LSA, a semi-automated approach is helpful in achieving a significant innovation over traditional methods of literature review and had shown great performance in many other research fields like medical, supply chain management, open street map etc. The secondary aim of this study is to investigate popular latent topics by mapping core research trends with core research areas. This study also provides prospective future directions for heading researchers.KeywordsStealth malwareRansomwareLatent semantic analysisResearch trendsTopic solution