MAPMon: A Host-Based Malware Detection Tool

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

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A Two-Layered Permission-Based Android Malware Detection Scheme

2014 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering ◽

10.1109/mobilecloud.2014.22 ◽

2014 ◽

Cited By ~ 32

Author(s):

Xing Liu ◽

Jiqiang Liu

Keyword(s):

Malware Detection ◽

Detection Scheme ◽

Android Malware ◽

Android Malware Detection

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Content and analysis of a knowledge translation activity for an elder abuse detection tool: a descriptive study

BMC Geriatrics ◽

10.1186/s12877-021-02402-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Mark J. Yaffe

Keyword(s):

Public Health ◽

Knowledge Translation ◽

Information Needs ◽

Elder Abuse ◽

Descriptive Study ◽

Public Health Research ◽

Descriptive Data ◽

Detection Tool ◽

Suspicion Index ◽

Structured Questionnaire

Abstract Background Knowledge translation (KT) is challenging to carry out and assess. The content of a program developed to foster KT activities pertaining to the Elder Abuse Suspicion Index (EASI)©, a tool to help identify elder abuse, is described, along with reporting and analysis of some of its outcomes. Methods Enquiries about the use of the EASI were encouraged through completion of a structured questionnaire available on an EASI website. These were submitted by email and guided individualized responses. Descriptive data collated anonymously from the questionnaires described in aggregate corresponders’ occupations, countries of work, information needs about the tool, and intent of use. The processes that generated this data were evaluated as to whether they conformed to established elements of KT. Results One hundred thirty-eight queries were received over 6 years coming from enquirers with 12 different professional backgrounds, working in 25 countries. The information sought aimed to facilitate EASI use in clinical, quality improvement, public health, research, teaching, KT, and commercial ventures. Conclusions This activity, incorporating recognized elements of a KT undertaking, documents specific global interests in elder abuse detection. It suggests a model for researchers to gauge interest in their findings and to promote exchange around them.

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