Android mobile malware detection using fuzzy AHP

Arif, Juliza Mohamad; Razak, Mohd Faizal Ab; Mat, Sharfah Ratibah Tuan; Awang, Suryanti; Ismail, Nor Syahidatul Nadiah; Firdaus, Ahmad

doi:10.1016/j.jisa.2021.102929

Cited by 30 publications

(11 citation statements)

References 30 publications

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“…Recursive Feature Elimination (RFE) Smmarwar et al [167] Binary Grey Wolf Optimization (BGWO) Arif et al [168] Information Gain Manzanares et al [169] Extracted permissions and other static and dynamic features to prepare a comprehensive dataset Bhat et al [170] Information Gain Deleted the features that are too infrequent and the ones that are present in almost the same number in both the datasets Elayan et al [171] Fed the extracted results to various machine classifiers Syrris et al [172] Removed the features having low variance Idrees et al [173] Information Gain Found the correlation between permissions and intents using Pearson correlation coefficient Rehman et al [174] Fed the results to various machine learning classifiers to find the cosine similarity between features Martin et al [175] Extracted to create a comprehensive and complete dataset, then fed the results to various machine learning classifiers for detection Navarro et al [ Clustering based k-means ++ algorithm to form separate clusters for each view which were combined later using stacking-based fusion method to learn the consensus malware detection pattern Zhu et al [190] Fed the results to RFbased machine learning classifier A.…”

Section: Techniques Usedmentioning

confidence: 99%

“…Malware datasets used Smmarwar et al [167] CICInvesAndMal2019 Arif et al [168] AndroZoo, Drebin Manzanares et al [169] Drebin, AMD, VirusTotal, VirusShare Bhat et al [170] Virustotal, VirusShare, Drebin Elayan et al [171] CICAndMal2017 Syrris et al [172] Drebin Idrees et al [173] Contagiodump, Genome, Virus Total, theZoo, MalShare, VirusShare Rehman et al [174] M0DROID Martin et al [175] Koodous, AndroZoo Navarro et al [176] AndroZoo, [217] , VirusShare, AndroMalShare Milosevic et al [177] M0Droid Alzaylaee et al [178] McAfee Cai et al [179] AMD, Drebin Badhani et al [180] Andro-DumpSys, AndroZoo, Contagio , AndroMalShare , AMD, VirusTotal Hijawi et al [181] [218] Sheen et al [182] Genome Nisha et al [183] AndroZoo, VirusShare, Andro-MalShare, PRAGuard[226] Song et al [184] Not mentioned Zhang et al [185] Genome Yang et al [186] No malware Thiyagarajan et al [187] AndroZoo Qaisar et al [188] Android PRAGuard, Drebin, Open-source apps, Kharon, Androzoo, CICAAGM Appice et al [189] Alternative Chinese and Russian Markets, Android websites, malware forums, security blogs, Genome Zhu et al [190] VirusShare A. Altaher [191] Genome Su et al [192] Drebin, Genome, Contagio Mahindru et al [193] Genome , AndroMalShare, [218] Dehkordy et al [194] Drebin, AMD Nguyen et al [195] AMD, Drebin Taheri et al [196] Drebin, Contagio, Genome Mahesh et al [197] Private companies Firdaus et al [198] Drebin, Genome Shrivastava et al [199] Third-party applications Varsha et al [20...…”

Section: Related Workmentioning

confidence: 99%

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A comprehensive review on permissions-based Android malware detection

Sharma,

Arora

2024

Int. J. Inf. Secur.

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The first Android-ready "G1" phone debuted in late October 2008. Since then, the growth of Android malware has been explosive analogous to the rise in the popularity of Android. The major positive aspect of Android has been its open-source nature, which empowers app developers to expand their work. But at the same time, authors with malicious intentions pose grave threats to users. In the presence of such threats, Android malware detection is the need of an hour. Consequently, researchers have proposed various techniques involving static, dynamic, and hybrid analysis to address such threats using numerous features in the last decade. But the feature that most researchers have extensively used to perform malware analysis and detection in Android security is Android permission. Hence, to provide a clarified overview of the latest and past work done in Android malware analysis and detection, we perform a comprehensive literature review using permissions as a central feature or in combination with other components by collecting and analyzing 200 studies from January 2009 to February 2023. We extracted information such as the choice opted by researchers between analysis or detection, techniques used to select or rank the permissions feature set, features used along with permissions, detection models employed, the malware datasets used by researchers, and lastly, the limitations and challenges in the field of Android malware detection to propose some future research directions. Additionally, based on the information extracted, we answer the six research questions designed considering the above factors.

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Section: Techniques Usedmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A comprehensive review on permissions-based Android malware detection

Sharma,

Arora

2024

Int. J. Inf. Secur.

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“…A fuzzy logic framework is designed to detect the malwares [16]. Analytical hierarchy process (AHP) fuzzy model achieves 90.54% based on the info gain value evaluation parameter.…”

Section: Introductionmentioning

confidence: 99%

An intelligent obfuscated mobile malware detection using deep supervised learning algorithms

Ganapathi,

Arumugam,

Dhathathri

2024

Bulletin EEI

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Obfuscated mobile malware (OMM) is a malicious software in mobile that hides to avoid detection and annihilation. These types of malwares are thorny to identify due to their inevitable nature. Deep learning (DL) algorithms are the most desirable to detect obfuscated malware based on the ‘n’ number of iterations with adjustable weights and neurons. This study investigates the accurate detection of OMM using significant DL algorithms such as multi-layer perceptron (MLP), self-organizing maps (SOM), long short-term memory (LSTM) networks, auto encoders (AE), and convolutional neural network (CNN) based on appropriate parameter tuning. The dataset taken for the study is CICMalMem2022 that contains 58,596 samples with 57 features which is basically designed for OMM detection. The dataset comprises Spyware, Ransomware, Trojan horse, and Benign. The DL models are evaluated based on performance metrics such as precision, recall, accuracy, training accuracy, test accuracy, validation accuracy, training loss, validation loss and receiver operating characteristic (ROC) curve. Based on the experimental evaluation, the study reveals that LSTM outperforms with 100% accuracy and MLP achieves 99.9% accuracy in detecting and classifying the OMM using deep supervised learning (SL) mechanism.

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“…The traditional security protection of power mobile terminals mainly carries out security monitoring and protection through Firewall, Flow Monitoring, WAF and other security devices 4 , which lacks a special security protection system for power mobile terminals 5 . At the same time, common security assessment methods at home and abroad at this stage mainly include: 1) Security risk assessment based on hierarchical method is a decision analysis method combining qualitative and quantitative methods 6 , for example, Arif et al used the fuzzy AHP method to detect Android malware 7 ; Chen et al used AHP method to assess the security risk of power mobile terminals 8 ; 2) The security risk assessment based on information fusion is mainly realized by fusing the security information collected by multi-sensor, Zhang et al realized the assessment of network security situation by collecting and integrating multi-source heterogeneous data 9 ; Li et al put forward an evidence theory based on "D-S", and used SVM, LR and KNN as multi-source information fusion sensors to achieve host security analysis 10 ; 3) Modeling based security risk assessment, which described the system behavior and state by building a security model, and gave an overall assessment of security risk based on the model. Such as Li et al proposes a network security situation assessment method based on attack tree, which realizes the assessment of Internet of Vehicles security 11 .…”

Section: Introductionmentioning

confidence: 99%

Research on security assessment and control technology for power mobile terminal

et al. 2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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With the rapid development of mobile Internet and the transformation of information technology in power energy enterprises, the demand for mobile office is increasing rapidly. However, the network security risks and attacks faced by mobile applications and terminals are also increasing, which brings hidden troubles to the safe production of enterprises. Therefore, this paper proposes a security assessment and control method for power mobile terminals, designs a mobile security protection system, puts forward indicators and methods for terminal security risk assessment, and designs a control mode for terminal risks, which effectively improves the security protection capability and emergency disposal ability of power mobile terminals, and helps the stable operation of power grid and ensure energy safety and reliability.

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Android mobile malware detection using fuzzy AHP

Cited by 30 publications

References 30 publications

A comprehensive review on permissions-based Android malware detection

A comprehensive review on permissions-based Android malware detection

An intelligent obfuscated mobile malware detection using deep supervised learning algorithms

Research on security assessment and control technology for power mobile terminal

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