327 research outputs found
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
The IoT (Internet of Things) technology has been widely adopted in recent
years and has profoundly changed the people's daily lives. However, in the
meantime, such a fast-growing technology has also introduced new privacy
issues, which need to be better understood and measured. In this work, we look
into how private information can be leaked from network traffic generated in
the smart home network. Although researchers have proposed techniques to infer
IoT device types or user behaviors under clean experiment setup, the
effectiveness of such approaches become questionable in the complex but
realistic network environment, where common techniques like Network Address and
Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic
analysis using traditional methods (e.g., through classical machine-learning
models) is much less effective under those settings, as the features picked
manually are not distinctive any more. In this work, we propose a traffic
analysis framework based on sequence-learning techniques like LSTM and
leveraged the temporal relations between packets for the attack of device
identification. We evaluated it under different environment settings (e.g.,
pure-IoT and noisy environment with multiple non-IoT devices). The results
showed our framework was able to differentiate device types with a high
accuracy. This result suggests IoT network communications pose prominent
challenges to users' privacy, even when they are protected by encryption and
morphed by the network gateway. As such, new privacy protection methods on IoT
traffic need to be developed towards mitigating this new issue
Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection
A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have
NoVT: Eliminating C++ Virtual Calls to Mitigate Vtable Hijacking
The vast majority of nowadays remote code execution attacks target virtual function tables (vtables).
Attackers hijack vtable pointers to change the control flow of a vulnerable program to their will, resulting in full control over the underlying system.
In this paper, we present NoVT, a compiler-based defense against vtable hijacking.
Instead of protecting vtables for virtual dispatch, our solution replaces them with switch-case constructs that are inherently control-flow safe, thus preserving control flow integrity of C++ virtual dispatch.
NoVT extends Clang to perform a class hierarchy analysis on C++ source code.
Instead of a vtable, each class gets unique identifier numbers which are used to dispatch the correct method implementation.
Thereby, NoVT inherently protects all usages of a vtable, not just virtual dispatch.
We evaluate NoVT on common benchmark applications and real-world programs including Chromium.
Despite its strong security guarantees, NoVT improves runtime performance of most programs (mean overhead -0.5%, -3.7% min, 2% max).
In addition, protected binaries are slightly smaller than unprotected ones.
NoVT works on different CPU architectures and protects complex C++ programs against strong attacks like COOP and ShrinkWrap
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the
microarchitecture of modern processors pose a serious threat to the privacy of
mobile phone users. This is because applications leave distinct footprints in
the processor, which can be used by malware to infer user activities. In this
work, we show that these inference attacks are considerably more practical when
combined with advanced AI techniques. In particular, we focus on profiling the
activity in the last-level cache (LLC) of ARM processors. We employ a simple
Prime+Probe based monitoring technique to obtain cache traces, which we
classify with Deep Learning methods including Convolutional Neural Networks. We
demonstrate our approach on an off-the-shelf Android phone by launching a
successful attack from an unprivileged, zeropermission App in well under a
minute. The App thereby detects running applications with an accuracy of 98%
and reveals opened websites and streaming videos by monitoring the LLC for at
most 6 seconds. This is possible, since Deep Learning compensates measurement
disturbances stemming from the inherently noisy LLC monitoring and unfavorable
cache characteristics such as random line replacement policies. In summary, our
results show that thanks to advanced AI techniques, inference attacks are
becoming alarmingly easy to implement and execute in practice. This once more
calls for countermeasures that confine microarchitectural leakage and protect
mobile phone applications, especially those valuing the privacy of their users
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