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Android Malware Detection Based on Software Complexity Metrics [chapter]

Mykola Protsenko, Tilo Müller
2014 Lecture Notes in Computer Science  
According to these results, we advocate for our new method to be a useful detector for samples within a malware family sharing functionality and source code.  ...  In this paper, we propose a new approach for the static detection of Android malware by means of machine learning that is based on software complexity metrics, such as McCabe's Cyclomatic Complexity and  ...  A special thanks goes to Michael Spreitzenbarth for giving us access to a large set of benign and malicious Android apps.  ... 
doi:10.1007/978-3-319-09770-1_3 fatcat:3vp4dj6bszdf7ktbve5qdet5da

Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware? [article]

Marco Melis, Michele Scalas, Ambra Demontis, Davide Maiorca, Battista Biggio, Giorgio Giacinto, Fabio Roli
2021 arXiv   pre-print
In this work, we investigate whether gradient-based attribution methods, used to explain classifiers' decisions by identifying the most relevant features, can be used to help identify and select more robust  ...  While machine-learning algorithms have demonstrated a strong ability in detecting Android malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of fake components, e.g.  ...  a malware of the FakeInstaller family (top) and a malware of the Plankton family (bottom).  ... 
arXiv:2005.01452v2 fatcat:hgbpr63czfcuzmi23u6jex5huq

FamDroid: Learning-Based Android Malware Family Classification Using Static Analysis [article]

Wenhao fan, Liang Zhao, Jiayang Wang, Ye Chen, Fan Wu, Yuan'an Liu
2021 arXiv   pre-print
In this paper, we propose FamDroid, a learning-based Android malware family classification scheme using static analysis technology.  ...  Thus, classifying Android malware into their corresponding families is an important task in malware analysis.  ...  AOM(Android-Oriented Metrics) [7] uses Android-Oriented Metrics to identify Android malware families.  ... 
arXiv:2101.03965v2 fatcat:qhe6wrg2rvfkdmjwns5o7uhvwm

Android Malware Family Classification and Analysis: Current Status and Future Directions

Fahad Alswaina, Khaled Elleithy
2020 Electronics  
Finally, we highlight the limitations that we identify in the literature, challenges, and future research directions regarding the Android malware family.  ...  For the past twelve years, Android malicious applications have been grouped into families. In the research community, detecting new malware families is a challenge.  ...  Malware families should be deeply analyzed and identified.  ... 
doi:10.3390/electronics9060942 fatcat:ge3jufdgijc6hf3aiwd6cmrhy4

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections [article]

TonTon Hsien-De Huang, Hung-Yu Kao
2018 arXiv   pre-print
The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware.  ...  The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware.  ...  Also, the dataset used by the current research-oriented machine learning-based Android malware detection is rather small.  ... 
arXiv:1705.04448v5 fatcat:6yp5nztkonc75jupownho7besu

A Dynamic Robust DL-based Model for Android Malware Detection

Ikram Ul Haq, Tamim Ahmed Khan, Adnan Akhunzada
2021 IEEE Access  
Designed a fast android malware detection method using FCFB.  ...  Besides, our proposed scheme has the capability to identify zero-day sophisticated multi-vector malware in Android Environment.  ... 
doi:10.1109/access.2021.3079370 fatcat:5dq4wu4vqzhhjmmb7y76eqc3fq

Various Data Mining Techniques to Detect the Android Malware Applications: A Case Study

Rincy Raphael
2019 International Journal of New Technology and Research  
In this paper we conduct a survey of various datamining techniques conducted to analyse and detect the android malware applications.  ...  This rapidly increasing adoption of Android has resulted in significant increase in the number of malwares when compared with previous years.  ...  The general recognition precision of the SVC is more than 85% for unspecific versatile malware. Santos et al. [27] proposed another strategy to identify obscure malware families.  ... 
doi:10.31871/ijntr.5.6.33 fatcat:gja4rvquv5dgdl42zndcsocsqi

An Analysis of Android Malware Classification Services

Mohammed Rashed, Guillermo Suarez-Tangil
2021 Sensors  
Our results contribute to a better understanding of the limitations of using Android malware family labels as supplied by common AV engines.  ...  We, then, explore the limitations associated with the use of available malware classification services, namely VirusTotal (VT) engines, for determining the family of an Android sample.  ...  Authors of the Android malware classification systems that we studied measured the quality of their systems using certain indicators/metrics.  ... 
doi:10.3390/s21165671 pmid:34451112 pmcid:PMC8402456 fatcat:ddetxcq75vbjxejswdj5a5ue6m

A Review of Android Malware Detection Approaches based on Machine Learning

Kaijun Liu, Shengwei Xu, Guoai Xu, Miao Zhang, Dawei Sun, Haifeng Liu
2020 IEEE Access  
Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream.  ...  We briefly introduce some background on Android applications, including the Android system architecture, security mechanisms, and classification of Android malware.  ...  us identify appropriate evaluation methods and metrics.  ... 
doi:10.1109/access.2020.3006143 fatcat:5rn2qg67ezdixkrefwxmyejhsi

OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning

Weina Niu, Rong Cao, Xiaosong Zhang, Kangyi Ding, Kaimeng Zhang, Ting Li
2020 Sensors  
Many new solutions use syntactic features and machine learning techniques to classify Android malware.  ...  Currently, most malware detection products on the market are based on malware signatures, which have a fast detection speed and normally a low false alarm rate for known malware families.  ...  Then, it identified Android malware using a k-Nearest Neighbor (k-NN) algorithm. Unfortunately, DroidMat couldn't perform well on detecting two Android malware families: BaseBridge and DroidKungFu.  ... 
doi:10.3390/s20133645 pmid:32610606 pmcid:PMC7374318 fatcat:iemyl4b6m5hmpd3gno756mmot4

Dynamic Mobile Malware Detection through System Call-based Image representation

Rosangela Casolare, Carlo De Dominicis, Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, Antonella Santone
2021 Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications  
Mobile devices, with particular regard to the ones equipped with the Android operating system, are currently targeted by malicious writers that continuously develop harmful code able to gather private  ...  Thus, we consider this representation to input a classifier to automatically discriminate whether an application under analysis is malware or legitimate.  ...  Signature-based malware detection is used to identify malware that security analysts already know.  ... 
doi:10.22667/jowua.2021.03.31.044 dblp:journals/jowua/CasolareDIMMS21 fatcat:zlvvjt6scffcdgjmyyklfnru3a

Classification and Analysis of Android Malware Images Using Feature Fusion Technique

Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali
2021 IEEE Access  
Performance metrics obtained using Feature Fusion-SVM classifier achieved the highest accuracy of 93.24% using CR+AM malware images.  ...  The primary focus of this study, was on the feature fusion technique to identify the descriptors, which could help to differentiate between different types of Android malware families.  ...  His research interests include computer vision, malware classification, Android Security, and deep learning.  ... 
doi:10.1109/access.2021.3090998 fatcat:2rwq6muatnahddw6wgbjsutkxi

DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications [chapter]

Chao Yang, Zhaoyan Xu, Guofei Gu, Vinod Yegneswaran, Phillip Porras
2014 Lecture Notes in Computer Science  
DroidMiner is a new malicious Android app detection system that uses static analysis to automatically mine malicious program logic from known Android malware.  ...  Once trained on a mobile malware corpus, DroidMiner can automatically scan a new Android app to (i) determine whether it contains malicious modalities, (ii) diagnose the malware family to which it is most  ...  Modality Use Cases We introduce how to use an Android app's Modality Vector to address the following three use-case scenarios: Malware Detection, Malware Family Classification, and Malicious Behavior Characterization  ... 
doi:10.1007/978-3-319-11203-9_10 fatcat:kmo5cyvvgjfk3axizuirx2eole

Android Malware Detection via Graph Representation Learning

Pengbin Feng, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi, Di Lu, Raul Montoliu
2021 Mobile Information Systems  
Then, we use the graph neural network to generate a vector representation of the application, and then malware detection is performed on this representation space.  ...  With the widespread usage of Android smartphones in our daily lives, the Android platform has become an attractive target for malware authors.  ...  can be effective in Android malware family detection.  ... 
doi:10.1155/2021/5538841 fatcat:o4beznwd4zadvcqfqubbwgalmy

Function-Oriented Mobile Malware Analysis as First Aid

Jae-wook Jang, Huy Kang Kim
2016 Mobile Information Systems  
In this paper, we propose a novel method for function-oriented malware analysis approach based on analysis of suspicious API call patterns.  ...  Instead of extracting API call patterns for malware in each family, we focus on extracting such patterns for certain malicious functionalities.  ...  We demonstrate that our method provides an effective metric for detecting packed malicious application and identifying malicious application as malware.  ... 
doi:10.1155/2016/6707524 fatcat:gyo22ksv65c7fd7bv6fpqd2ukq
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