“Less Give More”: Evaluate and zoning Android applications

Razak, Mohd Faizal Ab; Anuar, Nor Badrul; Salleh, Rosli; Firdaus, Ahmad; Faiz, Muhammad Khairi; Alamri, Hammoudeh S.

doi:10.1016/j.measurement.2018.10.034

Cited by 15 publications

(7 citation statements)

References 43 publications

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“…These were used for classifying the risk impact, and for providing the response to mitigate the risk. Razak et al ( 2019 ) also presented the risk factor based on zoning approaches.…”

Section: Challenges and Future Directionmentioning

confidence: 99%

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

et al. 2021

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Malware is a blanket term for Trojan, viruses, spyware, worms, and other files that are purposely created to harm computers, mobile devices, or computer networks. Malware commonly steals, encrypts, damages, and causes a mess in these devices. The growth of malware attacks has a consequence on the growth and attractiveness of mobile features in mobile devices. Most malware research aims to probe the different methods of preventing, analysing, and detecting malware attacks. This paper aims to demonstrate an exhaustive knowledge map of the Android malware by collecting a ten (10) year dataset from the Web of Science database. A bibliometric analysis was employed for analysing articles published between 2010 and 2019. Using the keyword "malware", 5622 articles were retrieved. After scrutinising with the keywords of "Android malware", 1278 articles were then collected. This study provides an overview of the articles, productivity, research area, the Web of Science categories, authors, high-cited articles, institutions, and impact journals examining malware. Research activities are continued by placing terms in the classification of malware detection systems that outline important areas in malware research. From the analysis, it can be concluded that the highest number of publications focusing on malware studies came from the continent of Asia. Additionally, this study discusses the challenges of malware studies in the recent research studies as well as the future direction.

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“…These were used for classifying the risk impact, and for providing the response to mitigate the risk. Razak et al ( 2019 ) also presented the risk factor based on zoning approaches.…”

Section: Challenges and Future Directionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…References of the articles with the use of the respective datasets Malgenome Anonymous (0000d) Yerima, Sezer & McWilliams (2014), Firdaus et al (2017), Firdaus & Anuar (2015), Firdaus et al (2018) Drebin Anonymous (0000c) Firdaus et al (2017); Firdaus et al (2018); Firdaus et al (2018) Android malware dataset (AMD) Badhani & Muttoo (2019) Contagio MilaParkour (2019) Feldman, Stadther & Wang (2014; Islamic & Minna (2015) Androzoo Université du Luxembourg ( 2018) Razak et al (2019); Firdaus et al (2017); Razak et al (2018); Firdaus et al (2017) Figure 4 Reverse engineer tools for static analysis. This is the example of reverse engineer tools that have been used by the previous researchers to extract the code for malware.…”

Section: Datasetmentioning

confidence: 99%

Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

Jusoh

Firdaus

Anwar³

et al. 2021

PeerJ Computer Science

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Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis.

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“…In order to select the relevant features, an optimization approach is implemented to optimize the URLs features. This optimization approach could reduce the time for training, Indonesian [20,26]. Besides, it is also important for data processing [22].…”

Section: Machine Learningmentioning

confidence: 99%

A malicious URLs detection system using optimization and machine learning classifiers

Lee

Heryanto

Razak

et al. 2020

IJEECS

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<span>The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy. The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.</span>

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“Less Give More”: Evaluate and zoning Android applications

Cited by 15 publications

References 43 publications

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

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

Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

A malicious URLs detection system using optimization and machine learning classifiers

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