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TriggerScope: Towards Detecting Logic Bombs in Android Applications

Yanick Fratantonio, Antonio Bianchi, William Robertson, Engin Kirda, Christopher Kruegel, Giovanni Vigna
2016 2016 IEEE Symposium on Security and Privacy (SP)  
Existing static analyses are effective in detecting the presence of most malicious code and unwanted information flows.  ...  Android is the most popular mobile platform today, and it is also the mobile operating system that is most heavily targeted by malware.  ...  Finally, we would like to thank Betty Sebright and her team for their significant help in motivating the development of this work.  ... 
doi:10.1109/sp.2016.30 dblp:conf/sp/FratantonioBRKK16 fatcat:ycew5fzx2bci7fo5gq4lmyltky

Anomaly-based exploratory analysis and detection of exploits in android mediaserver

Guillermo Suárez-Tangil, Santanu Kumar Dash, Pedro García-Teodoro, José Camacho, Lorenzo Cavallaro
2018 IET Information Security  
We evaluate our system against one of the most critical vulnerable and widely exploited services in Android, i.e., the mediaserver.  ...  However, we maintain that these type of attacks indirectly renders a number of unexpected behaviors in the system that can be profiled.  ...  The outlier goodware presents an anomalous behavior (one single observations) right at the beginning of the execution.  ... 
doi:10.1049/iet-ifs.2017.0460 fatcat:zen74jh2k5g7bkycjgtsm4ybv4

Analysis of Mobile Malware: A Systematic Review of Evolution and Infection Strategies

Moses Ashawa, Sarah Morris
2021 Journal of Information Security and Cybercrimes Research  
The survey results facilitate the understanding of mobile malware evolution and the infection trend.  ...  Finally, we identify the need for a critical analysis of mobile malware frameworks to identify their weaknesses and strengths to develop a more robust, accurate, and scalable tool from an Android detection  ...  The common I behavioral goal amongst the two malware applications is their monetization motive towards the mobile space.  ... 
doi:10.26735/krvi8434 fatcat:ukj62xubvzgdhf42fzgglv4zea

Evolution, Detection and Analysis of Malware for Smart Devices

Guillermo Suarez-Tangil, Juan E. Tapiador, Pedro Peris-Lopez, Arturo Ribagorda
2014 IEEE Communications Surveys and Tutorials  
This article examines the problem of malware in smart devices and recent progress made in detection techniques.  ...  We first present a detailed analysis on how malware has evolved over the last years for the most popular platforms.  ...  ACKNOWLEDGEMENTS We thank the anonymous reviewers for valuable suggestions that helped to improve the quality and organization of this paper.  ... 
doi:10.1109/surv.2013.101613.00077 fatcat:u7qjrw4grvcorjjmy3ykddjeda

Android Security: A Survey of Issues, Malware Penetration, and Defenses

Parvez Faruki, Ammar Bharmal, Vijay Laxmi, Vijay Ganmoor, Manoj Singh Gaur, Mauro Conti, Muttukrishnan Rajarajan
2015 IEEE Communications Surveys and Tutorials  
This is the accepted version of the paper. This version of the publication may differ from the final published version.  ...  And here comes the role of dynamic approaches, where we execute the app in a protected environment, providing all the emulated resources it needs, making it feel at home, thereby learning its malicious  ...  To counter this, Virtual Machine Introspection approach can be employed, in which, behavior of apps is observed external to the emulator [101] . IX.  ... 
doi:10.1109/comst.2014.2386139 fatcat:dkweaqhfo5dtpnii7xpgui4pgu

Deep Ground Truth Analysis of Current Android Malware [chapter]

Fengguo Wei, Yuping Li, Sankardas Roy, Xinming Ou, Wu Zhou
2017 Lecture Notes in Computer Science  
We also report our observations on the current landscape of Android malware as depicted in the dataset.  ...  For such datasets to be maximally useful, they need to contain reliable and complete information on malware's behaviors and techniques used in the malicious activities.  ...  For understanding the nefarious techniques used in the state-of-the-art malware apps, detailed behavior profiles for each malware variety must be provided in such a dataset.  ... 
doi:10.1007/978-3-319-60876-1_12 fatcat:pnohhzizovhprpi4ad6fnvgfym

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  
With the widespread usage of Android smartphones in our daily lives, the Android platform has become an attractive target for malware authors.  ...  There is an urgent need for developing an automatic malware detection approach to prevent the spread of malware.  ...  From Table 2 , we observe that these critical APIs correspond to common malicious behaviors, including accessing sensitive user information, dynamically loading malicious payload, and scanning the Android  ... 
doi:10.1155/2021/5538841 fatcat:o4beznwd4zadvcqfqubbwgalmy

Two Trends in Mobile Security: Financial Motives and Transitioning from Static to Dynamic Analysis [article]

Emre Erturk
2015 arXiv   pre-print
The goal of this paper is to analyze the behavior and intent of recent types of privacy invasive Android adware.  ...  Static analysis of malware provides high quality results and leads to a good understanding as shown in this paper.  ...  Dynamic analysis involves automated tools to execute the malware in a controlled system environment and check for malicious patterns. Therefore a large sample of malware can studied quickly.  ... 
arXiv:1504.06893v1 fatcat:hlwuowoxpvdq5hwtdnfpupedge

Two Trends in Mobile Malware: Financial Motives and Transitioning from Static to Dynamic Analysis

Emre Erturk
2013 International Journal of Intelligent Computing Research  
The goal of this paper is to analyze the behavior and intent of recent types of privacy-invasive Android adware.  ...  Static analysis of malware provides high quality results and leads to a good understanding as shown in this paper.  ...  Dynamic analysis involves automated tools to execute the malware in a controlled system environment and check for malicious patterns. Therefore a large sample of malware can studied quickly.  ... 
doi:10.20533/ijicr.2042.4655.2013.0039 fatcat:bjvbfmshfvhslp3ytu75ivtd2a

Review of Works Content Analyzer for Information Leakage Detection and Prevention in Android Smart Devices

T. Okebule, Afe Babalola University, Ado-Ekiti, Nigeria, Oluwaseyi A. Adeyemo, K. A. Olatunji, A. S. Awe, Afe Babalola University, Ado-Ekiti, Nigeria
2022 ABUAD International Journal of Natural and Applied Sciences  
The review will help to combine different concept to minimize false positives that will in turn lead to increase in code coverage towards detecting the maximum number of data leaks.  ...  This study presents a literature review of works on content Analyzers for information leakage detection and prevention on android-based devices.  ...  Different versions exist of Android and the system analyze an application in the context of Android 4.4.3.  ... 
doi:10.53982/aijnas.2022.0201.02-j fatcat:xltm3eyeczcibl37m52l5v23za

Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Mathew Ashik, A. Jyothish, S. Anandaram, P. Vinod, Francesco Mercaldo, Fabio Martinelli, Antonella Santone
2021 Electronics  
In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable  ...  Malware is one of the most significant threats in today's computing world since the number of websites distributing malware is increasing at a rapid rate.  ...  Malware analysis is the process aimed to inspect and understand a malicious behavior [3] .  ... 
doi:10.3390/electronics10141694 fatcat:wj5oa566gzcjldfq62kqnc3mrm

Toward Engineering a Secure Android Ecosystem

Meng Xu, Chenxiong Qian, Sangho Lee, Taesoo Kim, Chengyu Song, Yang Ji, Ming-Wei Shih, Kangjie Lu, Cong Zheng, Ruian Duan, Yeongjin Jang, Byoungyoung Lee
2016 ACM Computing Surveys  
The openness and extensibility of Android have made it a popular platform for mobile devices and a strong candidate to drive the Internet-of-Things.  ...  practices in the ecosystem.  ...  In this sense, merely measuring the code/path coverage might not be sufficient as malware can easily hide malicious behaviors deep inside the program, for example, executing malicious activities after  ... 
doi:10.1145/2963145 fatcat:d5vhxpdywrevvbh4as6vvt576q

Automatic Investigation Framework for Android Malware Cyber-Infrastructures [article]

ElMouatez Billah Karbab, Mouarad Debbabi
2018 arXiv   pre-print
However, there is a small coverage for the In- ternet/network dimension of the Android malicious apps.  ...  The popularity of Android system, not only in the handset devices but also in IoT devices, makes it a very attractive destination for malware.  ...  . is idea originates from the observation that Android malicious apps (and malware in general) make more use of the cloud as a low-cost infrastructure for their malicious activity. 3) In this step, we  ... 
arXiv:1806.08893v1 fatcat:lpuh6xwvuzgzzdjqja22khfdty

On The (In)Effectiveness of Static Logic Bomb Detector for Android Apps

Jordan Samhi, Alexandre Bartel
2021 IEEE Transactions on Dependable and Secure Computing  
Android is present in more than 85% of mobile devices, making it a prime target for malware.  ...  However, it could be used to speed up the location of malicious code, for instance, while reverse engineering applications.  ...  According to the authors, their approach is unsound but a stepping stone toward detecting new malware behavior.  ... 
doi:10.1109/tdsc.2021.3108057 fatcat:xhzislzgwvaynpkadjxxjlbz2e

A "Human-in-the-loop" approach for resolving complex software anomalies

Suresh Kothari, Akshay Deepak, Ahmed Tamrawi, Benjamin Holland, Sandeep Krishnan
2014 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)  
The case studies highlight the desired characteristics of an ART based tool and the type of role it plays in amplifying human intelligence.  ...  Automated static analysis tools are widely used in identifying software anomalies, such as memory leak, unsafe thread synchronization and malicious behaviors in smartphone applications.  ...  For example, to check the possibility of information leaks in an Android app, the human analyst would want to examine all the occurrences of Android APIs that are relevant in the context of a given app  ... 
doi:10.1109/smc.2014.6974210 dblp:conf/smc/KothariDTHK14 fatcat:xgnt3tvu4bgqrnpnwlou4c72ym
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