A Systematic Overview of Android Malware Detection

Li, Meijin; Fang, Zhiyang; Wang, Junfeng; Cheng, Luyu; Zeng, Qi; Yang, Tao; Wu, Yinwei

doi:10.1080/08839514.2021.2007327

Cited by 18 publications

(16 citation statements)

References 110 publications

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“…The uses of such techniques are presented as follows: The SVM classification approach looks for unusual behavior in IoT devices and malware on Android to assure the dependability of IoT services [ 106 ]. Anomalies, denial-of-service assaults, IoT intrusions, and irregularities in smart cities are all detected using the random forest approach [ 107 ]. Two other methods for detecting abnormalities include a Naive-Bayes-based classification model and a linear-regression-based strategy for spotting malicious IoT malicious nodes [ 108 ].…”

Section: Resultsmentioning

confidence: 99%

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Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

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Section: Resultsmentioning

confidence: 99%

“…Anomalies, denial-of-service assaults, IoT intrusions, and irregularities in smart cities are all detected using the random forest approach [ 107 ].…”

Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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“…In the context of malware classification, it has been observed that machine learning‐based Android detectors face sustainability challenges. These detectors typically undergo performance degradation due to the Android ecosystem's constant development and the emergence of novel malware types (Meijin et al, 2022). In the research domain, researchers (Kantchelian et al, 2013) have repeatedly retrained the malware classification model to address this issue.…”

Section: Related Workmentioning

confidence: 99%

“…The feature extraction techniques include data cleaning, transformation, reduction, and discretization. Data cleaning: Following acquiring the initial data using feature extraction, data cleaning involves eliminating irrelevant properties. For instance, this process addresses the elimination of inconsequential permissions present in benign and malicious Android applications (Meijin et al, 2022). Data transformation: Data transformation encompasses data conversion from one format to another.…”

Section: Android Malware Detection Modelmentioning

confidence: 99%

“…Data transformation: Data transformation encompasses data conversion from one format to another. A standard research methodology involves converting data into images before input into a deep neural network (Meijin et al, 2022). Data reduction: To address the issues of the Android feature vector's excessively high dimensionality, data reduction primarily relates to the problematic algorithm‐based method of dimensionality reduction of the data (Meijin et al, 2022).…”

Section: Android Malware Detection Modelmentioning

confidence: 99%

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A review of detecting malware in android devices based on machine learning techniques

Sharma,

Kaul

2023

Expert Systems

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Malware developers install malware on mobile users' devices and steal their personal information without their knowledge. According to recent studies, it has been observed that malware developers are now targeting Android mobile devices. Researchers have examined the issues of detecting malware in these devices and proposed different methods and techniques. This study's main goal is to aid researchers in gaining a basic understanding of Android malware and its numerous detection methods. Earlier experiments that used machine learning to detect Android malware will be carefully reviewed in this paper. This in‐depth review article thoroughly examines the origins, evolution, and sustainability of Android malware detection. It offers an in‐depth literature review that includes the most recent approaches and research trends for detecting malware, from static analysis to dynamic analysis, machine learning, and deep learning. Additionally, we review current approaches' shortcomings and difficulties and suggest possible paths for further investigation. The paper aims to stimulate further innovation in this essential field by providing researchers and practitioners with a comprehensive overview of the current status of Android malware detection.

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