<i>PermPair</i>: Android Malware Detection Using Permission Pairs

Arora, Anshul; Peddoju, Sateesh K.; Conti, Mauro

doi:10.1109/tifs.2019.2950134

Cited by 133 publications

(89 citation statements)

References 45 publications

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“…Arora et al [ 3 ] proposed PermPair, in which they construct and compare the graphs by extracting permissions from benign and malware-infected apps. Empirical result reveals that proposed malware detection model achieved an accuracy of 95.44% when compared to other similar approaches and favorite mobile anti-malware apps.…”

Section: Related Work and Overview Of Proposed Frameworkmentioning

confidence: 99%

FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques

Mahindru

Sangal

2021

Multimed Tools Appl

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With the recognition of free apps, Android has become the most widely used smartphone operating system these days and it naturally invited cyber-criminals to build malwareinfected apps that can steal vital information from these devices. The most critical problem is to detect malware-infected apps and keep them out of Google play store. The vulnerability lies in the underlying permission model of Android apps. Consequently, it has become the responsibility of the app developers to precisely specify the permissions which are going to be demanded by the apps during their installation and execution time. In this study, we examine the permission-induced risk which begins by giving unnecessary permissions to these Android apps. The experimental work done in this research paper includes the development of an effective malware detection system which helps to determine and investigate the detective influence of numerous well-known and broadly used set of features for malware detection. To select best features from our collected features data set we implement ten distinct feature selection approaches. Further, we developed the malware detection model by utilizing LSSVM (Least Square Support Vector Machine) learning approach connected through three distinct kernel functions i.e., linear, radial basis and polynomial. Experiments were performed by using 2,00,000 distinct Android apps. Empirical result reveals that the model build by utilizing LSSVM with RBF (i.e., radial basis kernel function) named as FSdroid is able to detect 98.8% of malware when compared to distinct anti-virus scanners and also achieved 3% higher detection rate when compared to different frameworks or approaches proposed in the literature.

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Section: Related Work and Overview Of Proposed Frameworkmentioning

confidence: 99%

FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques

Mahindru

Sangal

2021

Multimed Tools Appl

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“…The evolving evasion techniques being used by malware writers to thwart static analysis led to the development of dynamic analysis. Moser et al [12], explored the drawbacks of static analysis methodology. In their work, they introduced a scheme based on code obfuscation revealing the fact that the static analysis alone is not enough to detect or classify malware.…”

Section: Static Analysismentioning

confidence: 99%

“…Few recent studies have been done on static and dynamic analysis of Android malware [11], detection using permission [12][13][14], based on system call sequences and LSTM [15].…”

mentioning

confidence: 99%

A Survey on Malware Detection and Analysis Tools

Talukder¹,

Talukder²

2020

IJNSA

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The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools.

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“…Arora et al [22] suggested a static approach to analyse permissions using the manifest file. A lightweight technique for malware detection was proposed, and its effectiveness was experimentally demonstrated using real Android malware samples.…”

Section: Malware Detection Using Static Analysismentioning

confidence: 99%

cHybriDroid: A Machine Learning-Based Hybrid Technique for Securing the Edge Computing

Maryam

Ahmed

Aleem

et al. 2020

Security and Communication Networks

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Smart phones are an integral component of the mobile edge computing (MEC) framework. Securing the data stored on mobile devices is very crucial for ensuring the smooth operations of cloud services. A growing number of malicious Android applications demand an in-depth investigation to dissect their malicious intent to design effective malware detection techniques. The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. The selection of application’s features plays a very crucial role to capture the appropriate behavioural patterns of malware instances for a useful classification of mobile applications. In this study, we propose a novel hybrid approach to detect android malware, wherein static features in conjunction with dynamic features of smart phone applications are employed. We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography’s exploitation, and network manipulations) to analyse the effectiveness of the employed techniques for malware detection. We conduct experiments using over 5,000 real-world applications. The outcomes of the study reveal that the proposed set of features has successfully detected malware threats with 97% F-measure results.

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PermPair: Android Malware Detection Using Permission Pairs

Cited by 133 publications

References 45 publications

FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques

FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques

A Survey on Malware Detection and Analysis Tools

cHybriDroid: A Machine Learning-Based Hybrid Technique for Securing the Edge Computing

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