Automatic privacy leakage detection for massive android apps via a novel hybrid approach

Author(s):  
Hongyi Chen ◽  
Ho-fung Leung ◽  
Biao Han ◽  
Jinshu Su
2014 ◽  
Vol 556-562 ◽  
pp. 2658-2662 ◽  
Author(s):  
Pu Han Zhang ◽  
Jing Zhe Li ◽  
Shuai Shao ◽  
Peng Wang

The prevalence of Android makes it face the severe security threats from malicious apps. Many Android malware can steal users’ sensitive data and leak them out. The data flow analysis is a popular technique used to detect privacy leakages by tracking the sensitive information flow statically. In practice, an effective data flow analysis should employ inter-procedure information tracking. However, the Android event-driven programming model brings a challenge to construct the call graph (CG) for a target app. This paper presents a method which employs the inter-procedural and context-sensitive data flow analysis to detect privacy leakage in Android apps. To make the analysis accurate, a flow-sensitive and points-to call target analysis is employed to construct and improve the call graph. A prototype system, called PDroid, has been implemented and applied to some real malware. The experiment shows that our method can effective detect the privacy leakages cross multiple method call instances.


Author(s):  
Eliyas Girma Mohammed ◽  
Ethiopia Bisrat Zeleke ◽  
Surafel Lemma Abebe

Abstract A significant percentage of treated water is lost due to leakage in water distribution systems. The state-of-the-art leak detection and localization schemes use a hybrid approach of hydraulic modeling and data-driven techniques. Most of these works, however, focus on single leakage detection and localization. In this research, we propose to use combined pressure and flow residual data to detect and localize multiple leaks. The proposed approach has two phases: detection and localization. The detection phase uses the combination of pressure and flow residuals to build a hydraulic model and classification algorithm to identify leaks. The localization phase analyzes the pattern of isolated leak residuals to localize multiple leaks. To evaluate the performance of the proposed approach, we conducted experiments using Hanoi Water Network benchmark and a dataset produced based on LeakDB benchmark's dataset preparation procedure. The result for a well-calibrated hydraulic model shows that leak detection is 100% accurate while localization is 90% accurate, thereby outperforming minimum night flow and raw- and residual-based methods in localizing leaks. The proposed approach performed relatively well with the introduction of demand and noise uncertainty. The proposed localization approach is also able to locate two to four leaks that existed simultaneously.


Author(s):  
Michalis Pachilakis ◽  
Spiros Antonatos ◽  
Killian Levacher ◽  
Stefano Braghin

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1184 ◽  
Author(s):  
Jingjing Gu ◽  
Ruicong Huang ◽  
Li Jiang ◽  
Gongzhe Qiao ◽  
Xiaojiang Du ◽  
...  

Intelligent medical service system integrates wireless internet of things (WIoT), including medical sensors, wireless communications, and middleware techniques, so as to collect and analyze patients’ data to examine their physical conditions by many personal health devices (PHDs) in real time. However, large amount of malicious codes on the Android system can compromise consumers’ privacy, and further threat the hospital management or even the patients’ health. Furthermore, this sensor-rich system keeps generating large amounts of data and saturates the middleware system. To address these challenges, we propose a fog computing security and privacy protection solution. Specifically, first, we design the security and privacy protection framework based on the fog computing to improve tele-health and tele-medicine infrastructure. Then, we propose a context-based privacy leakage detection method based on the combination of dynamic and static information. Experimental results show that the proposed method can achieve higher detection accuracy and lower energy consumption compared with other state-of-art methods.


2021 ◽  
Author(s):  
Pranav Kotak ◽  
Shweta Bhandari ◽  
Akka Zemmari ◽  
Jaykrishna Joshi
Keyword(s):  

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