3,379 research outputs found
The Evolution of Android Malware and Android Analysis Techniques
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An in-Depth Study of the Jisut Family of Android Ransomware
Android malware is increasing in spread and complexity. Advanced obfuscation, emulation detection, delayed payload activation or dynamic code loading are some of the techniques employed by the current malware to hinder the use of reverse engineering techniques and anti-malware tools. This growing complexity is particularly noticeable in the evolution of different strands of the same malware family. Over the years, these families mature to become more effective by incorporating new and enhanced techniques. In this paper, we focus on a particular Android ransomware family named Jisut, and perform a thorough technical analysis. We also provide a detailed overall perspective, which will hopefully help to create new tools and techniques to tackle more effectively the threat posed by ransomware
Malware detection techniques for mobile devices
Mobile devices have become very popular nowadays, due to its portability and
high performance, a mobile device became a must device for persons using
information and communication technologies. In addition to hardware rapid
evolution, mobile applications are also increasing in their complexity and
performance to cover most needs of their users. Both software and hardware
design focused on increasing performance and the working hours of a mobile
device. Different mobile operating systems are being used today with different
platforms and different market shares. Like all information systems, mobile
systems are prone to malware attacks. Due to the personality feature of mobile
devices, malware detection is very important and is a must tool in each device
to protect private data and mitigate attacks. In this paper, analysis of
different malware detection techniques used for mobile operating systems is
provides. The focus of the analysis will be on the to two competing mobile
operating systems - Android and iOS. Finally, an assessment of each technique
and a summary of its advantages and disadvantages is provided. The aim of the
work is to establish a basis for developing a mobile malware detection tool
based on user profiling.Comment: 11 pages, 6 figure
Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned
Despite the growing threat posed by Android malware,
the research community is still lacking a comprehensive
view of common behaviors and trends exposed by malware families
active on the platform. Without such view, the researchers
incur the risk of developing systems that only detect outdated
threats, missing the most recent ones. In this paper, we conduct
the largest measurement of Android malware behavior to date,
analyzing over 1.2 million malware samples that belong to 1.2K
families over a period of eight years (from 2010 to 2017). We
aim at understanding how the behavior of Android malware
has evolved over time, focusing on repackaging malware. In
this type of threats different innocuous apps are piggybacked
with a malicious payload (rider), allowing inexpensive malware
manufacturing.
One of the main challenges posed when studying repackaged
malware is slicing the app to split benign components apart from
the malicious ones. To address this problem, we use differential
analysis to isolate software components that are irrelevant to the
campaign and study the behavior of malicious riders alone. Our
analysis framework relies on collective repositories and recent
advances on the systematization of intelligence extracted from
multiple anti-virus vendors. We find that since its infancy in
2010, the Android malware ecosystem has changed significantly,
both in the type of malicious activity performed by the malicious
samples and in the level of obfuscation used by malware to avoid
detection. We then show that our framework can aid analysts
who attempt to study unknown malware families. Finally, we
discuss what our findings mean for Android malware detection
research, highlighting areas that need further attention by the
research community.Accepted manuscrip
Security Toolbox for Detecting Novel and Sophisticated Android Malware
This paper presents a demo of our Security Toolbox to detect novel malware in
Android apps. This Toolbox is developed through our recent research project
funded by the DARPA Automated Program Analysis for Cybersecurity (APAC)
project. The adversarial challenge ("Red") teams in the DARPA APAC program are
tasked with designing sophisticated malware to test the bounds of malware
detection technology being developed by the research and development ("Blue")
teams. Our research group, a Blue team in the DARPA APAC program, proposed a
"human-in-the-loop program analysis" approach to detect malware given the
source or Java bytecode for an Android app. Our malware detection apparatus
consists of two components: a general-purpose program analysis platform called
Atlas, and a Security Toolbox built on the Atlas platform. This paper describes
the major design goals, the Toolbox components to achieve the goals, and the
workflow for auditing Android apps. The accompanying video
(http://youtu.be/WhcoAX3HiNU) illustrates features of the Toolbox through a
live audit.Comment: 4 pages, 1 listing, 2 figure
A family of droids -- Android malware detection via behavioral modeling: static vs dynamic analysis
Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach. To this end, we build on MaMaDroid, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AuntieDroid and instantiate it by using both automated (Monkey) and user-generated inputs. We find that combining both static and dynamic analysis yields the best performance, with F-measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and, finally, investigate the reasons for inconsistent misclassifications across methods.Accepted manuscrip
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