635,867 research outputs found
A consensus based network intrusion detection system
Network intrusion detection is the process of identifying malicious behaviors
that target a network and its resources. Current systems implementing intrusion
detection processes observe traffic at several data collecting points in the
network but analysis is often centralized or partly centralized. These systems
are not scalable and suffer from the single point of failure, i.e. attackers
only need to target the central node to compromise the whole system. This paper
proposes an anomaly-based fully distributed network intrusion detection system
where analysis is run at each data collecting point using a naive Bayes
classifier. Probability values computed by each classifier are shared among
nodes using an iterative average consensus protocol. The final analysis is
performed redundantly and in parallel at the level of each data collecting
point, thus avoiding the single point of failure issue. We run simulations
focusing on DDoS attacks with several network configurations, comparing the
accuracy of our fully distributed system with a hierarchical one. We also
analyze communication costs and convergence speed during consensus phases.Comment: Presented at THE 5TH INTERNATIONAL CONFERENCE ON IT CONVERGENCE AND
SECURITY 2015 IN KUALA LUMPUR, MALAYSI
System for collecting world-wide lower thermosphere data
Collecting lower thermosphere data by global satellite network measurements of atmospheric composition, density, temperature, and airglo
Decentralized mobility models for data collection in wireless sensor networks
Controlled mobility in wireless sensor networks provides many benefits towards enhancing the network performance and prolonging its lifetime. Mobile elements, acting as mechanical data carriers, traverse the network collecting data using single-hop communication, instead of the more energy demanding multi-hop routing to the sink. Scaling up from single to multiple mobiles is based more on the mobility models and the coordination methodology rather than increasing the number of mobile elements in the network. This work addresses the problem of designing and coordinating decentralized mobile elements for scheduling data collection in wireless sensor networks, while preserving some performance measures, such as latency and amount of data collected. We propose two mobility models governing the behaviour of the mobile element, where the incoming data collection requests are scheduled to service according to bidding strategies to determine the winner element. Simulations are run to measure the performance of the proposed mobility models subject to the network size and the number of mobile elements.<br /
An efficient data collecting network structure in wireless sensor networks
Author name used in this publication: Chi K. TseAuthor name used in this publication: Francis C. M. LauRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe
Making Good Use of Survey Paradata
Paradata are data about the process of collecting survey data. They can include things like call record data, CAI keystroke files and interviewer observations. Such data can be put to a variety of uses that can improve surveys. In this article we describe the work of a network that has been set up in the UK to explore and promote the use of survey paradata and we outline some of the key challenges in making the most of paradata
Lower bounds on data collection time in sensory networks
Data collection, i.e., the aggregation at the user location of information gathered by sensor nodes, is a fundamental function of sensory networks. Indeed, most sensor network applications rely on data collection capabilities, and consequently, an inefficient data collection process may adversely affect the performance of the network. In this paper, we study via simple discrete mathematical models, the time performance of the data collection and data distribution tasks in sensory networks. Specifically, we derive the minimum delay in collecting sensor data for networks of various topologies such as line, multiline, and tree and give corresponding optimal scheduling strategies. Furthermore, we bound the data collection time on general graph networks. Our analyses apply to networks equipped with directional or omnidirectional antennas and simple comparative results of the two systems are presented
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