46,848 research outputs found
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
Management and Security of IoT systems using Microservices
Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and fault tolerance. We aim to solve the above issues in a microservice architecture. We build a data pipeline for IoT devices to send data through a messaging platform Kafka and monitor the devices using the collected data by making real time dashboards and a machine learning model to give better insights of the data. For proof of concept, we test the proposed solution on a heterogeneous cluster, including Raspberry Piâs and IoT devices from different vendors. We validate our design by presenting some simple experimental results
Detection Rates for Close Binaries Via Microlensing
Microlensing is one of the most promising methods of reconstructing the
stellar mass function down to masses even below the hydrogen-burning limit. The
fundamental limit to this technique is the presence of unresolved binaries,
which can in principle significantly alter the inferred mass function. Here we
quantify the fraction of binaries that can be detected using microlensing,
considering specifically the mass ratio and separation of the binary. We find
that almost all binary systems with separations greater than of
their combined Einstein ring radius are detectable assuming a detection
threshold of . For two M dwarfs, this corresponds to a limiting separation
of \gsim 1 \au. Since very few observed M dwarfs have companions at
separations \lsim 1 \au, we conclude that close binaries will probably not
corrupt the measurements of the mass function. We find that the detectability
depends only weakly on the mass ratio. For those events for which individual
masses can be determined, we find that binaries can be detected down to .Comment: 19 pages including 6 figures. Uses phyyzx format. Send requests for
higher quality figures to [email protected]
Detection and localization of change-points in high-dimensional network traffic data
We propose a novel and efficient method, that we shall call TopRank in the
following paper, for detecting change-points in high-dimensional data. This
issue is of growing concern to the network security community since network
anomalies such as Denial of Service (DoS) attacks lead to changes in Internet
traffic. Our method consists of a data reduction stage based on record
filtering, followed by a nonparametric change-point detection test based on
-statistics. Using this approach, we can address massive data streams and
perform anomaly detection and localization on the fly. We show how it applies
to some real Internet traffic provided by France-T\'el\'ecom (a French Internet
service provider) in the framework of the ANR-RNRT OSCAR project. This approach
is very attractive since it benefits from a low computational load and is able
to detect and localize several types of network anomalies. We also assess the
performance of the TopRank algorithm using synthetic data and compare it with
alternative approaches based on random aggregation.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS232 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
Strong field limit analysis of gravitational retro-lensing
We present a complete treatment in the strong field limit of gravitational
retro-lensing by a static spherically symmetric compact object having a photon
sphere. The results are compared with those corresponding to ordinary lensing
in similar strong field situations. As examples of application of the
formalism, a supermassive black hole at the galactic center and a stellar mass
black hole in the galactic halo are studied as retro-lenses, in both cases
using the Schwarzschild and Reissner-Nordstrom geometries.Comment: 11 pages, 1 figure; v2: minor changes. Accepted for publication in
Physical Review
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