8,828 research outputs found
Android Malware Family Classification Based on Resource Consumption over Time
The vast majority of today's mobile malware targets Android devices. This has
pushed the research effort in Android malware analysis in the last years. An
important task of malware analysis is the classification of malware samples
into known families. Static malware analysis is known to fall short against
techniques that change static characteristics of the malware (e.g. code
obfuscation), while dynamic analysis has proven effective against such
techniques. To the best of our knowledge, the most notable work on Android
malware family classification purely based on dynamic analysis is DroidScribe.
With respect to DroidScribe, our approach is easier to reproduce. Our
methodology only employs publicly available tools, does not require any
modification to the emulated environment or Android OS, and can collect data
from physical devices. The latter is a key factor, since modern mobile malware
can detect the emulated environment and hide their malicious behavior. Our
approach relies on resource consumption metrics available from the proc file
system. Features are extracted through detrended fluctuation analysis and
correlation. Finally, a SVM is employed to classify malware into families. We
provide an experimental evaluation on malware samples from the Drebin dataset,
where we obtain a classification accuracy of 82%, proving that our methodology
achieves an accuracy comparable to that of DroidScribe. Furthermore, we make
the software we developed publicly available, to ease the reproducibility of
our results.Comment: Extended Versio
MPeersim: Simulation Environment for Mobile P2P Networks
Abstract: In understanding technical aspects of technology, simulation environments play a very important role. Emergence of mobile P2P networks and their widespread adoption has accentuated the need for development of a simulation platform for modeling and analysis of these networks. This paper presents MPeersim, a simulation environment capable of modeling mobile P2P networks by incorporating configurable node and network related parameters to attain various statistics for subsequent analysis. MPeersim presents a novel concept of mobile P2P network monitoring. It not only provides with a pivotal platform for conducting propagation analysis of legitimate mobile P2P traffic but also of 13 mobile P2P malware families that encompass around 25% of the total discovered mobile malware. 3-tier statistics collection model of MPeersim enables it to collect generic mobile and network statistics on network and community levels while behaviour statistics on agent nodes. These statistics help detect network and community based mobile P2P threats and malware families
Malware "Ecology" Viewed as Ecological Succession: Historical Trends and Future Prospects
The development and evolution of malware including computer viruses, worms,
and trojan horses, is shown to be closely analogous to the process of community
succession long recognized in ecology. In particular, both changes in the
overall environment by external disturbances, as well as, feedback effects from
malware competition and antivirus coevolution have driven community succession
and the development of different types of malware with varying modes of
transmission and adaptability.Comment: 13 pages, 3 figure
Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats
Network steganography is the art of hiding secret information within innocent
network transmissions. Recent findings indicate that novel malware is
increasingly using network steganography. Similarly, other malicious activities
can profit from network steganography, such as data leakage or the exchange of
pedophile data. This paper provides an introduction to network steganography
and highlights its potential application for harmful purposes. We discuss the
issues related to countering network steganography in practice and provide an
outlook on further research directions and problems.Comment: 11 page
PlaceRaider: Virtual Theft in Physical Spaces with Smartphones
As smartphones become more pervasive, they are increasingly targeted by
malware. At the same time, each new generation of smartphone features
increasingly powerful onboard sensor suites. A new strain of sensor malware has
been developing that leverages these sensors to steal information from the
physical environment (e.g., researchers have recently demonstrated how malware
can listen for spoken credit card numbers through the microphone, or feel
keystroke vibrations using the accelerometer). Yet the possibilities of what
malware can see through a camera have been understudied. This paper introduces
a novel visual malware called PlaceRaider, which allows remote attackers to
engage in remote reconnaissance and what we call virtual theft. Through
completely opportunistic use of the camera on the phone and other sensors,
PlaceRaider constructs rich, three dimensional models of indoor environments.
Remote burglars can thus download the physical space, study the environment
carefully, and steal virtual objects from the environment (such as financial
documents, information on computer monitors, and personally identifiable
information). Through two human subject studies we demonstrate the
effectiveness of using mobile devices as powerful surveillance and virtual
theft platforms, and we suggest several possible defenses against visual
malware
The future of Cybersecurity in Italy: Strategic focus area
This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management
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