23,707 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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A survey of intrusion detection techniques in Cloud
Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks
PerfWeb: How to Violate Web Privacy with Hardware Performance Events
The browser history reveals highly sensitive information about users, such as
financial status, health conditions, or political views. Private browsing modes
and anonymity networks are consequently important tools to preserve the privacy
not only of regular users but in particular of whistleblowers and dissidents.
Yet, in this work we show how a malicious application can infer opened websites
from Google Chrome in Incognito mode and from Tor Browser by exploiting
hardware performance events (HPEs). In particular, we analyze the browsers'
microarchitectural footprint with the help of advanced Machine Learning
techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines,
and in contrast to previous literature also Convolutional Neural Networks. We
profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing
portals, on two machines featuring an Intel and an ARM processor. By monitoring
retired instructions, cache accesses, and bus cycles for at most 5 seconds, we
manage to classify the selected websites with a success rate of up to 86.3%.
The results show that hardware performance events can clearly undermine the
privacy of web users. We therefore propose mitigation strategies that impede
our attacks and still allow legitimate use of HPEs
Tiresias: Predicting Security Events Through Deep Learning
With the increased complexity of modern computer attacks, there is a need for
defenders not only to detect malicious activity as it happens, but also to
predict the specific steps that will be taken by an adversary when performing
an attack. However this is still an open research problem, and previous
research in predicting malicious events only looked at binary outcomes (e.g.,
whether an attack would happen or not), but not at the specific steps that an
attacker would undertake. To fill this gap we present Tiresias, a system that
leverages Recurrent Neural Networks (RNNs) to predict future events on a
machine, based on previous observations. We test Tiresias on a dataset of 3.4
billion security events collected from a commercial intrusion prevention
system, and show that our approach is effective in predicting the next event
that will occur on a machine with a precision of up to 0.93. We also show that
the models learned by Tiresias are reasonably stable over time, and provide a
mechanism that can identify sudden drops in precision and trigger a retraining
of the system. Finally, we show that the long-term memory typical of RNNs is
key in performing event prediction, rendering simpler methods not up to the
task
Intelligent Fault Analysis in Electrical Power Grids
Power grids are one of the most important components of infrastructure in
today's world. Every nation is dependent on the security and stability of its
own power grid to provide electricity to the households and industries. A
malfunction of even a small part of a power grid can cause loss of
productivity, revenue and in some cases even life. Thus, it is imperative to
design a system which can detect the health of the power grid and take
protective measures accordingly even before a serious anomaly takes place. To
achieve this objective, we have set out to create an artificially intelligent
system which can analyze the grid information at any given time and determine
the health of the grid through the usage of sophisticated formal models and
novel machine learning techniques like recurrent neural networks. Our system
simulates grid conditions including stimuli like faults, generator output
fluctuations, load fluctuations using Siemens PSS/E software and this data is
trained using various classifiers like SVM, LSTM and subsequently tested. The
results are excellent with our methods giving very high accuracy for the data.
This model can easily be scaled to handle larger and more complex grid
architectures.Comment: In proceedings of the 29th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI) 2017 (full paper); 6 pages; 13 figure
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