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Catch Me If You Can: Evaluating Android Anti-Malware Against Transformation Attacks

Vaibhav Rastogi, Yan Chen, Xuxian Jiang
2014 IEEE Transactions on Information Forensics and Security  
Our results on ten popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques.  ...  In this paper, we evaluate the state-of-the-art commercial mobile anti-malware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware  ...  CONCLUSION We evaluated ten anti-malware products on Android for their resilience against malware transformations.  ... 
doi:10.1109/tifs.2013.2290431 fatcat:hw4gfomb45aq5c3zjxfcp5b27i

DroidChameleon

Vaibhav Rastogi, Yan Chen, Xuxian Jiang
2013 Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security - ASIA CCS '13  
In this paper, we evaluate the state-of-the-art commercial mobile anti-malware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware  ...  Our results on ten popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques.  ...  CONCLUSION We evaluated ten anti-malware products on Android for their resilience against malware transformations.  ... 
doi:10.1145/2484313.2484355 dblp:conf/ccs/RastogiCJ13 fatcat:uxf5fhiahffm7ieqksaslhax2m

Self-protection of Android systems from inter-component communication attacks

Mahmoud Hammad, Joshua Garcia, Sam Malek
2018 Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering - ASE 2018  
Anti-malware Products We have evaluated the accuracy and the resiliency of 61 commercial anti-malware products against obfuscations.  ...  To assess the effects of code obfuscation on Android apps and anti-malware products, we have conducted a large-scale empirical study that evaluates the effectiveness of the top anti-malware products against  ...  Therefore, the current anti-malware products are insufficient in protecting smartphone users against the increasing number and sophisticated malicious apps.  ... 
doi:10.1145/3238147.3238207 dblp:conf/kbse/HammadGM18 fatcat:qht4e54ehjfltlht6wjuwzsata

The rise of obfuscated Android malware and impacts on detection methods

Wael F. Elsersy, Ali Feizollah, Nor Badrul Anuar
2022 PeerJ Computer Science  
The malware authors adopt the obfuscation and transformation techniques to defeat the anti-malware detections, which this paper refers to as evasions.  ...  The study concludes the research gaps in evaluating the current Android malware detection framework robustness against state-of-the-art evasion techniques.  ...  AAMO (Preda & Maggi, 2016) and Droidchameleon (Rastogi, Chen & Jiang, 2013) study the effectiveness of evading commercial anti-malware applications by using their evaluation tools; Droidchameleon  ... 
doi:10.7717/peerj-cs.907 pmid:35494876 pmcid:PMC9044361 fatcat:cpbfkiw4bvd3rjx7a3f7ckictu

Automatic Generation of Mobile Malwares Using Genetic Programming [chapter]

Emre Aydogan, Sevil Sen
2015 Lecture Notes in Computer Science  
We aim to evaluate the efficacy of current anti-virus products, using static analysis techniques, in the market.  ...  However developing methodologies that detect unknown malwares is a research challenge, especially on devices with limited resources.  ...  In [8] has developed a system called DroidChameleon that evaluates Android anti-malware products against obfuscation attacks that are extended form of the attacks in In [7] .  ... 
doi:10.1007/978-3-319-16549-3_60 fatcat:yx63ilyhtzcdje42f4xl3jdvma

Mystique

Guozhu Meng, Yinxing Xue, Chandramohan Mahinthan, Annamalai Narayanan, Yang Liu, Jie Zhang, Tieming Chen
2016 Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security - ASIA CCS '16  
With the help of MYSTIQUE, we conduct experiments to 1) understand Android malware and the associated attack features as well as evasion techniques; 2) evaluate and compare the 57 off-the-shelf anti-malware  ...  Thus, it is desired to conduct a systematic investigation and evaluation of anti-malware solutions and tools based on different attacks and evasion techniques.  ...  DROIDCHAMELEON [41, 42] collects three types of transformation attacks in Android, and the authors have used these attacks to audit the off-the-shelf detection tools. Huang et al.  ... 
doi:10.1145/2897845.2897856 dblp:conf/ccs/MengXCN0ZC16 fatcat:ssubdviipffe3k5lc7ue2evzu4

The Evolution of Android Malware and Android Analysis Techniques

Kimberly Tam, Ali Feizollah, Nor Badrul Anuar, Rosli Salleh, Lorenzo Cavallaro
2017 ACM Computing Surveys  
This article presents a comprehensive survey on leading Android malware analysis and detection techniques, and their effectiveness against evolving malware.  ...  Sophisticated mobile malware, particularly Android malware, acquire or utilize such data without user consent.  ...  In 2014, an attack against the Android In-app Billing was launched using dynamically loaded code and was successful against 60% of the top 85 Android apps .  ... 
doi:10.1145/3017427 fatcat:f2vdpgntincgvd4xv52l2ovray

Monet: A User-oriented Behavior-based Malware Variants Detection System for Android [article]

Mingshen Sun, Xiaolei Li, John C.S. Lui, Richard T.B. Ma, Zhenkai Liang
2016 arXiv   pre-print
Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks.  ...  We collect 3723 malware samples and top 500 benign apps to carry out extensive experiments of detecting malware variants and defending against malware transformation.  ...  Experiment 2 (Defending Against Malware Transformation): Transformation attacks use static obfuscation tools to hide malicious logic.  ... 
arXiv:1612.03312v1 fatcat:jrd4eke62zelzpakbzwhmeeba4

Resilient and Adaptive Framework for Large Scale Android Malware Fingerprinting using Deep Learning and NLP Techniques [article]

ElMouatez Billah Karbab, Mourad Debbabi
2021 arXiv   pre-print
Android malware detection is a significat problem that affects billions of users using millions of Android applications (apps) in existing markets.  ...  This paper proposes PetaDroid, a framework for accurate Android malware detection and family clustering on top of static analyses.  ...  We conducted a thorough evaluation of different reference datasets and various settings. We evaluate PetaDroid on a market scale Android dataset, 10 Million samples and over 100TB of data.  ... 
arXiv:2105.13491v1 fatcat:5byz64ros5hwpni7wubwarli3i

DroidNative: Semantic-Based Detection of Android Native Code Malware [article]

Shahid Alam, Zhengyang Qu, Ryan Riley, Yan Chen, Vaibhav Rastogi
2016 arXiv   pre-print
we know is the first system that operates at the Android native code level, allowing it to detect malware embedded in both native code and bytecode.  ...  According to the Symantec and F-Secure threat reports, mobile malware development in 2013 and 2014 has continued to focus almost exclusively ~99% on the Android platform.  ...  Previous studies [26, 37, 51] have evaluated the resilience of commercial anti-malware products when tested against variants of known malware.  ... 
arXiv:1602.04693v2 fatcat:72ibq3qxx5fadp77qva2uooorm

Android Malware Clustering using Community Detection on Android Packages Similarity Network [article]

ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb
2020 arXiv   pre-print
Using this concept, we presumably assume that multiple similar Android apps with different authors are most likely to be malicious.  ...  The daily amount of Android malicious applications (apps) targeting the app repositories is increasing, and their number is overwhelming the process of fingerprinting.  ...  Obfuscation is considered a big issue in malware detection systems, including Cypider, where the adversary uses an obfuscated content or transformation attacks.  ... 
arXiv:2005.06075v1 fatcat:43wg2wbvejg35ix6yz6tskmie4

Black box analysis of android malware detectors

Guruswamy Nellaivadivelu, Fabio Di Troia, Mark Stamp
2020 Array  
In this research, we obfuscate selected features of known Android malware samples and determine whether these obfuscated samples can still be reliably detected.  ...  Using this approach, we discover which features are most significant for various sets of Android malware detectors, in effect, performing a black box analysis of these detectors.  ...  [22] , the authors propose and develop a framework that they refer to as DroidChameleon, which provides a way to transform Android applications into different forms, each having the same functionality  ... 
doi:10.1016/j.array.2020.100022 fatcat:c235i7noh5fwbkw2thfms4mutu

Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach [article]

Sen Chen, Minhui Xue, Lingling Fan, Shuang Hao, Lihua Xu, Haojin Zhu, Bo Li
2017 arXiv   pre-print
Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data, rendering most recent machine learning-based malware detection tools (such as  ...  The evolution of mobile malware poses a serious threat to smartphone security.  ...  We reviewed several challenges for the malware detection problem. We showed how the conventional machine learning classifiers can fail against determined attackers.  ... 
arXiv:1706.04146v3 fatcat:f7yzifuahff6dfyaihlrnn3gfa

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.  ...  We evaluate DroidMiner using 2,466 malicious apps, identified from a corpus of over 67,000 third-party market Android apps, plus an additional set of over 10,000 official market Android apps.  ...  As observed by DroidChameleon [47] , common malware transformation techniques (e.g., repackaging, changing field names, and changing control-flow logics) could evade many existing commercial anti-malware  ... 
doi:10.1007/978-3-319-11203-9_10 fatcat:kmo5cyvvgjfk3axizuirx2eole

Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach [article]

Rahim Taheri, Reza Javidan, Mohammad Shojafar, Vinod P, Mauro Conti
2020 arXiv   pre-print
The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform.  ...  Hence, to evaluate the vulnerability of machine learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications (apps).  ...  -We evaluate attack and defense using three benchmark malware dataset.  ... 
arXiv:1904.09433v2 fatcat:b4nxve7hx5fmdkaihgtyucbue4
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