799 research outputs found
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
Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection
Recent studies observe that app foreground is the most striking component
that influences the access control decisions in mobile platform, as users tend
to deny permission requests lacking visible evidence. However, none of the
existing permission models provides a systematic approach that can
automatically answer the question: Is the resource access indicated by app
foreground? In this work, we present the design, implementation, and evaluation
of COSMOS, a context-aware mediation system that bridges the semantic gap
between foreground interaction and background access, in order to protect
system integrity and user privacy. Specifically, COSMOS learns from a large set
of apps with similar functionalities and user interfaces to construct generic
models that detect the outliers at runtime. It can be further customized to
satisfy specific user privacy preference by continuously evolving with user
decisions. Experiments show that COSMOS achieves both high precision and high
recall in detecting malicious requests. We also demonstrate the effectiveness
of COSMOS in capturing specific user preferences using the decisions collected
from 24 users and illustrate that COSMOS can be easily deployed on smartphones
as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201
GuardFS: a File System for Integrated Detection and Mitigation of Linux-based Ransomware
Although ransomware has received broad attention in media and research, this
evolving threat vector still poses a systematic threat. Related literature has
explored their detection using various approaches leveraging Machine and Deep
Learning. While these approaches are effective in detecting malware, they do
not answer how to use this intelligence to protect against threats, raising
concerns about their applicability in a hostile environment. Solutions that
focus on mitigation rarely explore how to prevent and not just alert or halt
its execution, especially when considering Linux-based samples. This paper
presents GuardFS, a file system-based approach to investigate the integration
of detection and mitigation of ransomware. Using a bespoke overlay file system,
data is extracted before files are accessed. Models trained on this data are
used by three novel defense configurations that obfuscate, delay, or track
access to the file system. The experiments on GuardFS test the configurations
in a reactive setting. The results demonstrate that although data loss cannot
be completely prevented, it can be significantly reduced. Usability and
performance analysis demonstrate that the defense effectiveness of the
configurations relates to their impact on resource consumption and usability
Advanced Security Analysis for Emergent Software Platforms
Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems.
This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms.
Advisers: Hamid Bagheri and Qiben Ya
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
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