5,946 research outputs found
An Efficient Fog-Assisted Unstable Sensor Detection Scheme with Privacy Preserved
The Internet of Thing (IoT) has been a hot topic in both research community
and industry. It is anticipated that in future IoT, an enormous number of
sensors will collect the physical information every moment to enable the
control center making better decisions to improve the quality of service (QoS).
However, the sensors maybe faulty and thus generate inaccurate data which would
compromise the decision making. To guarantee the QoS, the system should be able
to detect faulty sensors so as to eliminate the damages of inaccurate data.
Various faulty sensor detection mechanisms have been developed in the context
of wireless sensor network (WSN). Some of them are only fit for WSN while the
others would bring a communication burden to control center. To detect the
faulty sensors for general IoT applications and save the communication resource
at the same time, an efficient faulty sensor detection scheme is proposed in
this paper. The proposed scheme takes advantage of fog computing to save the
computation and communication resource of control center. To preserve the
privacy of sensor data, the Paillier Cryptosystem is adopted in the fog
computing. The batch verification technique is applied to achieve efficient
authentication. The performance analyses are presented to demonstrate that the
proposed detection scheme is able to conserve the communication resource of
control center and achieve a high true positive ratio while maintaining an
acceptable false positive ratio. The scheme could also withstand various
security attacks and preserve data privacy.Comment: 11 pages, 5 figure
Making Availability as a Service in the Clouds
Cloud computing has achieved great success in modern IT industry as an
excellent computing paradigm due to its flexible management and elastic
resource sharing. To date, cloud computing takes an irrepalceable position in
our socioeconomic system and influences almost every aspect of our daily life.
However, it is still in its infancy, many problems still exist.Besides the
hotly-debated security problem, availability is also an urgent issue.With the
limited power of availability mechanisms provided in present cloud platform, we
can hardly get detailed availability information of current applications such
as the root causes of availability problem,mean time to failure, etc. Thus a
new mechanism based on deep avaliability analysis is neccessary and
benificial.Following the prevalent terminology 'XaaS',this paper proposes a new
win-win concept for cloud users and providers in term of 'Availability as a
Service' (abbreviated as 'AaaS').The aim of 'AaaS' is to provide comprehensive
and aimspecific runtime avaliabilty analysis services for cloud users by
integrating plent of data-driven and modeldriven approaches. To illustrate this
concept, we realize a prototype named 'EagleEye' with all features of 'AaaS'.
By subscribing corresponding services in 'EagleEye', cloud users could get
specific availability information of their applications deployed in cloud
platform. We envision this new kind of service will be merged into the cloud
management mechanism in the near future.Comment:
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
A Smart Home Gateway Platform for Data Collection and Awareness
Smart homes have attracted much attention due to the expanding of
Internet-of-Things (IoT) and smart devices. In this paper, we propose a smart
gateway platform for data collection and awareness in smart home networks. A
smart gateway will replace the traditional network gateway to connect the home
network and the Internet. A smart home network supports different types of
smart devices, such as in home IoT devices, smart phones, smart electric
appliances, etc. A traditional network gateway is not capable of providing
quality-of-service measurement, user behavioral analytics, or network
optimization. Such tasks are traditionally performed with measurement agents
such as optical splitters or network probes deployed in the core network. Our
proposed platform is a lightweight plug-in for the smart gateway to accomplish
data collection, awareness and reporting. While the smart gateway is able to
adjust the control policy for data collection and awareness locally, a
cloud-based controller is also included for more refined control policy
updates. Furthermore, we propose a multi-dimensional awareness framework to
achieve accurate data awareness at the smart gateway. The efficiency of data
collection and accuracy of data awareness of the proposed platform is
demonstrated based on the tests using actual data traffic from a large number
of smart home users
Privacy-Preserving DDoS Attack Detection Using Cross-Domain Traffic in Software Defined Networks
Existing distributed denial-of-service attack detection in software defined
networks (SDNs) typically perform detection in a single domain. In reality,
abnormal traffic usually affects multiple network domains. Thus, a cross-domain
attack detection has been proposed to improve detection performance. However,
when participating in detection, the domain of each SDN needs to provide a
large amount of real traffic data, from which private information may be
leaked. Existing multiparty privacy protection schemes often achieve privacy
guarantees by sacrificing accuracy or increasing the time cost. Achieving both
high accuracy and reasonable time consumption is a challenging task. In this
paper, we propose Predis, which is a privacypreserving cross-domain attack
detection scheme for SDNs. Predis combines perturbation encryption and data
encryption to protect privacy and employs a computationally simple and
efficient algorithm k-Nearest Neighbors (kNN) as its detection algorithm. We
also improve kNN to achieve better efficiency. Via theoretical analysis and
extensive simulations, we demonstrate that Predis is capable of achieving
efficient and accurate attack detection while securing sensitive information of
each domain
A Novel PMU Fog based Early Anomaly Detection for an Efficient Wide Area PMU Network
Based on phasor measurement units (PMUs), a synchronphasor system is widely
recognized as a promising smart grid measurement system. It is able to provide
high-frequency, high-accuracy phasor measurements sampling for Wide Area
Monitoring and Control (WAMC) applications. However, the high sampling
frequency of measurement data under strict latency constraints introduces new
challenges for real time communication. It would be very helpful if the
collected data can be prioritized according to its importance such that the
existing quality of service (QoS) mechanisms in the communication networks can
be leveraged. To achieve this goal, certain anomaly detection functions should
be conducted by the PMUs. Inspired by the recent emerging edge-fog-cloud
computing hierarchical architecture, which allows computing tasks to be
conducted at the network edge, a novel PMU fog is proposed in this paper. Two
anomaly detection approaches, Singular Spectrum Analysis (SSA) and K-Nearest
Neighbors (KNN), are evaluated in the PMU fog using the IEEE 16-machine 68-bus
system. The simulation experiments based on Riverbed Modeler demonstrate that
the proposed PMU fog can effectively reduce the data flow end-to-end (ETE)
delay without sacrificing data completeness.Comment: presented at the 2nd IEEE International Conference on Fog and Edge
Computing (ICFEC 2018), Washington DC, USA, May 1, 201
A Decade of Mal-Activity Reporting: A Retrospective Analysis of Internet Malicious Activity Blacklists
This paper focuses on reporting of Internet malicious activity (or
mal-activity in short) by public blacklists with the objective of providing a
systematic characterization of what has been reported over the years, and more
importantly, the evolution of reported activities. Using an initial seed of 22
blacklists, covering the period from January 2007 to June 2017, we collect more
than 51 million mal-activity reports involving 662K unique IP addresses
worldwide. Leveraging the Wayback Machine, antivirus (AV) tool reports and
several additional public datasets (e.g., BGP Route Views and Internet
registries) we enrich the data with historical meta-information including
geo-locations (countries), autonomous system (AS) numbers and types of
mal-activity. Furthermore, we use the initially labelled dataset of approx 1.57
million mal-activities (obtained from public blacklists) to train a machine
learning classifier to classify the remaining unlabeled dataset of approx 44
million mal-activities obtained through additional sources. We make our unique
collected dataset (and scripts used) publicly available for further research.
The main contributions of the paper are a novel means of report collection,
with a machine learning approach to classify reported activities,
characterization of the dataset and, most importantly, temporal analysis of
mal-activity reporting behavior. Inspired by P2P behavior modeling, our
analysis shows that some classes of mal-activities (e.g., phishing) and a small
number of mal-activity sources are persistent, suggesting that either
blacklist-based prevention systems are ineffective or have unreasonably long
update periods. Our analysis also indicates that resources can be better
utilized by focusing on heavy mal-activity contributors, which constitute the
bulk of mal-activities.Comment: ACM Asia Conference on Computer and Communications Security
(AsiaCCS), 13 page
Mobile Device Identification via Sensor Fingerprinting
We demonstrate how the multitude of sensors on a smartphone can be used to
construct a reliable hardware fingerprint of the phone. Such a fingerprint can
be used to de-anonymize mobile devices as they connect to web sites, and as a
second factor in identifying legitimate users to a remote server. We present
two implementations: one based on analyzing the frequency response of the
speakerphone-microphone system, and another based on analyzing device-specific
accelerometer calibration errors. Our accelerometer-based fingerprint is
especially interesting because the accelerometer is accessible via JavaScript
running in a mobile web browser without requesting any permissions or notifying
the user. We present the results of the most extensive sensor fingerprinting
experiment done to date, which measured sensor properties from over 10,000
mobile devices. We show that the entropy from sensor fingerprinting is
sufficient to uniquely identify a device among thousands of devices, with low
probability of collision
From Network Traces to System Responses: Opaquely Emulating Software Services
Enterprise software systems make complex interactions with other services in
their environment. Developing and testing for production-like conditions is
therefore a challenging task. Prior approaches include emulations of the
dependency services using either explicit modelling or record-and-replay
approaches. Models require deep knowledge of the target services while
record-and-replay is limited in accuracy. We present a new technique that
improves the accuracy of record-and-replay approaches, without requiring prior
knowledge of the services. The approach uses multiple sequence alignment to
derive message prototypes from recorded system interactions and a scheme to
match incoming request messages against message prototypes to generate response
messages. We introduce a modified Needleman-Wunsch algorithm for distance
calculation during message matching, wildcards in message prototypes for high
variability sections, and entropy-based weightings in distance calculations for
increased accuracy. Combined, our new approach has shown greater than 99%
accuracy for four evaluated enterprise system messaging protocols.Comment: Technical Report. Swinburne University of Technology, Faculty of
Information and Technolog
The Challenges in SDN/ML Based Network Security : A Survey
Machine Learning is gaining popularity in the network security domain as many
more network-enabled devices get connected, as malicious activities become
stealthier, and as new technologies like Software Defined Networking (SDN)
emerge. Sitting at the application layer and communicating with the control
layer, machine learning based SDN security models exercise a huge influence on
the routing/switching of the entire SDN. Compromising the models is
consequently a very desirable goal. Previous surveys have been done on either
adversarial machine learning or the general vulnerabilities of SDNs but not
both. Through examination of the latest ML-based SDN security applications and
a good look at ML/SDN specific vulnerabilities accompanied by common attack
methods on ML, this paper serves as a unique survey, making a case for more
secure development processes of ML-based SDN security applications.Comment: 8 pages. arXiv admin note: substantial text overlap with
arXiv:1705.0056
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