IEEE ANDROID APPLICATION 2016 TITLE AND ABSTRACT

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IEEE TRANSACTION ON ANDROID APPLICATON TOPICS 2016
MADAM: Effective and Efficient Behavior-based Android MalwaSre Detection and
Prevention
Abstract - Android users are constantly threatened by an increasing number of malicious
applications (apps), generically called malware. Malware constitutes a serious threat to user
privacy, money, device and file integrity. In this paper we note that, by studying their actions, we
can classify malware into a small number of behavioral classes, each of which performs a limited
set of misbehaviors that characterize them. These misbehaviors can be defined by monitoring
features belonging to different Android levels. In this paper we present MADAM, a novel host-
based malware detection system for Android devices which simultaneously analyzes and
correlates features at four levels: kernel, application, user and package, to detect and stop
malicious behaviors. MADAM has been designed to take into account those behaviors
characteristics of almost every real malware which can be found in the wild. MADAM detects
and effectively blocks more than 96% of malicious apps, which come from three large datasets
with about 2,800 apps, by exploiting the cooperation of two parallel classifiers and a behavioral
signature-based detector. Extensive experiments, which also includes the analysis of a testbed of
9,804 genuine apps, have been conducted to show the low false alarm rate, the negligible
performance overhead and limited battery consumption.
IEEE Transactions on Dependable and Secure Computing (March 2016)
FLANDROID: Energy-Efficient Recommendations of Reliable Context Providers for
Android Applications
Abstract - Mobile applications are becoming more and more popular with the prevalence of
mobile operating systems and mobile Internet. Many of them consume services provided by the
underlying infrastructure and platforms as a part of their application environmental contexts.
However, application failures or downgrade in performance may be the results due to inadequate
provisions of these environmental issues in the implementations of the mobile applications. In
this paper, we propose a framework to enable mobile applications to consume services offered
by a reliable context provider with high probability in run time. We report a case study on a suite

of five real-world mobile applications with 74 real faults on real mobile phones involving 50
users. The results of the case study show that our framework can significantly improve the
reliability of mobile applications with respect to the failures due to buggy-context-provider faults
with low slowdown and energy overheads.
IEEE Transactions on Services Computing (May 2016)
Light-weight, Inter-procedural and Callback-aware Resource Leak Detection for Android
Apps
Abstract - Android devices include many embedded resources such as Camera, Media Player and
Sensors. These resources require programmers to explicitly request and release them. Missing
release operations might cause serious problems such as performance degradation or system
crash. This kind of defects is called resource leak. Despite a large body of existing works on
testing and analyzing Android apps, there still remain several challenging problems. In this work,
we present Relda2, a light-weight and precise static resource leak detection tool. We first
systematically collected a resource table, which includes the resources that the Android reference
requires developers release manually. Based on this table, we designed a general approach to
automatically detect resource leaks. To make a more precise inter-procedural analysis, we
constructed a Function Call Graph for each Android application, which handles function calls of
user-defined methods and the callbacks invoked by the Android framework at the same time. To
evaluate Relda2’s effectiveness and practical applicability, we downloaded 103 apps from
popular app stores and an open source community, and found 67 real resource leaks, which we
have confirmed manually.
IEEE Transactions on Software Engineering (March 2016)
Composite Constant Propagation and its Application to Android Program Analysis
Abstract - Many program analyses require statically inferring the possible values of composite
types. However, current approaches either do not account for correlations between object fields
or do so in an ad hoc manner. In this paper, we introduce the problem of composite constant

propagation. We develop the first generic solver that infers all possible values of complex
objects in an interprocedural, flow and context-sensitive manner, taking field correlations into
account. Composite constant propagation problems are specified using COAL, a declarative
language. We apply our COAL solver to the problem of inferring Android Inter-Component
Communication (ICC) values, which is required to understand how the components of Android
applications interact. Using COAL, we model ICC objects in Android more thoroughly than the
state-of-the-art. We compute ICC values for 489 applications from the Google Play store. The
ICC values we infer are substantially more precise than previous work. The analysis is efficient,
taking two minutes per application on average. While this work can be used as the basis for
many whole-program analyses of Android applications, the COAL solver can also be used to
infer the values of composite objects in many other contexts.
IEEE Transactions on Software Engineering (April 2016)
ICCDetector: ICC-Based Malware Detection on Android
Abstract - Most existing mobile malware detection methods (e.g., Kirin and DroidMat) are
designed based on the resources required by malwares (e.g., permissions, application
programming interface (API) calls, and system calls). These methods capture the interactions
between mobile apps and Android system, but ignore the communications among components
within or cross application boundaries. As a consequence, the majority of the existing methods
are less effective in identifying many typical malwares, which require a few or no suspicious
resources, but leverage on inter-component communication (ICC) mechanism when launching
stealthy attacks. To address this challenge, we propose a new malware detection method, named
ICCDetector. ICCDetector outputs a detection model after training with a set of benign apps and
a set of malwares, and employs the trained model for malware detection. The performance of
ICCDetector is evaluated with 5264 malwares, and 12026 benign apps. Compared with our
benchmark, which is a permission-based method proposed by Peng et al. in 2012 with an
accuracy up to 88.2%, ICCDetector achieves an accuracy of 97.4%, roughly 10% higher than the

benchmark, with a lower false positive rate of 0.67%, which is only about a half of the
benchmark. After manually analyzing false positives, we discover 43 new malwares from the
benign data set, and reduce the number of false positives to seven. More importantly,
ICCDetector discovers 1708 more advanced malwares than the benchmark, while it misses 220
obvious malwares, which can be easily detected by the benchmark. For the detected malwares,
ICCDetector further classifies them into five newly defined malware categories, which help
understand the relationship between malicious behaviors and ICC characteristics. We also
provide a systemic analysis of ICC patterns of benign apps and malwares.
IEEE Transactions on Information Forensics and Security (June 2016)
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