80,266 research outputs found
A Survey of Techniques for Improving Security of GPUs
Graphics processing unit (GPU), although a powerful performance-booster, also
has many security vulnerabilities. Due to these, the GPU can act as a
safe-haven for stealthy malware and the weakest `link' in the security `chain'.
In this paper, we present a survey of techniques for analyzing and improving
GPU security. We classify the works on key attributes to highlight their
similarities and differences. More than informing users and researchers about
GPU security techniques, this survey aims to increase their awareness about GPU
security vulnerabilities and potential countermeasures
NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem
As a consequence of the growing popularity of smart mobile devices, mobile
malware is clearly on the rise, with attackers targeting valuable user
information and exploiting vulnerabilities of the mobile ecosystems. With the
emergence of large-scale mobile botnets, smartphones can also be used to launch
attacks on mobile networks. The NEMESYS project will develop novel security
technologies for seamless service provisioning in the smart mobile ecosystem,
and improve mobile network security through better understanding of the threat
landscape. NEMESYS will gather and analyze information about the nature of
cyber-attacks targeting mobile users and the mobile network so that appropriate
counter-measures can be taken. We will develop a data collection infrastructure
that incorporates virtualized mobile honeypots and a honeyclient, to gather,
detect and provide early warning of mobile attacks and better understand the
modus operandi of cyber-criminals that target mobile devices. By correlating
the extracted information with the known patterns of attacks from wireline
networks, we will reveal and identify trends in the way that cyber-criminals
launch attacks against mobile devices.Comment: Accepted for publication in Proceedings of the 28th International
Symposium on Computer and Information Sciences (ISCIS'13); 9 pages; 1 figur
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Continuous Authentication for Voice Assistants
Voice has become an increasingly popular User Interaction (UI) channel,
mainly contributing to the ongoing trend of wearables, smart vehicles, and home
automation systems. Voice assistants such as Siri, Google Now and Cortana, have
become our everyday fixtures, especially in scenarios where touch interfaces
are inconvenient or even dangerous to use, such as driving or exercising.
Nevertheless, the open nature of the voice channel makes voice assistants
difficult to secure and exposed to various attacks as demonstrated by security
researchers. In this paper, we present VAuth, the first system that provides
continuous and usable authentication for voice assistants. We design VAuth to
fit in various widely-adopted wearable devices, such as eyeglasses,
earphones/buds and necklaces, where it collects the body-surface vibrations of
the user and matches it with the speech signal received by the voice
assistant's microphone. VAuth guarantees that the voice assistant executes only
the commands that originate from the voice of the owner. We have evaluated
VAuth with 18 users and 30 voice commands and find it to achieve an almost
perfect matching accuracy with less than 0.1% false positive rate, regardless
of VAuth's position on the body and the user's language, accent or mobility.
VAuth successfully thwarts different practical attacks, such as replayed
attacks, mangled voice attacks, or impersonation attacks. It also has low
energy and latency overheads and is compatible with most existing voice
assistants
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