1,251 research outputs found
Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications
Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead
Evaluation of Android anti-malware resistance against transformation attacks
Android being most popular and user-friendly is targeted by most of the malware authors. The malware authors use various transformation techniques to create different variants of malwares. Different transformation techniques such as obfuscation, repackaging, renaming are used mostly. Many anti-malwares are developed to secure the Android devices. Android does not offer file access permissions to all the applications installed. Thus anti-malwares may not provide complete security to the Android devices. In this paper, many such different techniques are presented that can be used to evaluate different anti-malwares
Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph
As the security landscape evolves over time, where thousands of species of
malicious codes are seen every day, antivirus vendors strive to detect and
classify malware families for efficient and effective responses against malware
campaigns. To enrich this effort, and by capitalizing on ideas from the social
network analysis domain, we build a tool that can help classify malware
families using features driven from the graph structure of their system calls.
To achieve that, we first construct a system call graph that consists of system
calls found in the execution of the individual malware families. To explore
distinguishing features of various malware species, we study social network
properties as applied to the call graph, including the degree distribution,
degree centrality, average distance, clustering coefficient, network density,
and component ratio. We utilize features driven from those properties to build
a classifier for malware families. Our experimental results show that
influence-based graph metrics such as the degree centrality are effective for
classifying malware, whereas the general structural metrics of malware are less
effective for classifying malware. Our experiments demonstrate that the
proposed system performs well in detecting and classifying malware families
within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201
Leveraging the Cloud for Software Security Services.
This thesis seeks to leverage the advances in cloud computing in order to address modern
security threats, allowing for completely novel architectures that provide dramatic
improvements and asymmetric gains beyond what is possible using current approaches.
Indeed, many of the critical security problems facing the Internet and its users are inadequately
addressed by current security technologies. Current security measures often are deployed
in an exclusively network-based or host-based model, limiting their efficacy against
modern threats. However, recent advancements in the past decade in cloud computing and
high-speed networking have ushered in a new era of software services. Software services
that were previously deployed on-premise in organizations and enterprises are now being
outsourced to the cloud, leading to fundamentally new models in how software services are
sold, consumed, and managed.
This thesis focuses on how novel software security services can be deployed that leverage
the cloud to scale elegantly in their capabilities, performance, and management. First,
we introduce a novel architecture for malware detection in the cloud. Next, we propose
a cloud service to protect modern mobile devices, an ever-increasing target for malicious
attackers. Then, we discuss and demonstrate the ability for attackers to leverage the same
benefits of cloud-centric services for malicious purposes. Next, we present new techniques
for the large-scale analysis and classification of malicious software. Lastly, to demonstrate
the benefits of cloud-centric architectures outside the realm of malicious software,
we present a threshold signature scheme that leverages the cloud for robustness and resiliency.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91385/1/jonojono_1.pd
- …