638 research outputs found
Indian armed forces approach to managing ISR big data
The importance of information and intelligence for the military is apparent from the fact that it is fundamental to the planning of any military operation in peace or war. It forms the core component of the military kill chain. The Military uses a term Kill Chain to describe the sequence of events leading to the destruction of the target. It includes the steps of target detection and identification, dispatch of force or weapon to track it, decision making, and final command to destroy it. It is also known as F2T2EA cycle or the Find, Fix, Track, Target, Engage and Assess cycle. ISR is the key determinant of detection and identification of the target
Enhanced cyberspace defense with real-time distributed systems using covert channel publish-subscribe broker pattern communications
In this thesis, we propose a novel cyberspace defense solution to the growing sophistication of threats facing networks within the Department of Defense. Current network defense strategies, including traditional intrusion detection and firewall-based perimeter defenses, are ineffective against increasingly sophisticated social engineering attacks such as spear-phishing which exploit individuals with targeted information. These asymmetric attacks are able to bypass current network defense technologies allowing adversaries extended and often unrestricted access to portions of the enterprise. Network defense strategies are hampered by solutions favoring network-centric designs which disregard the security requirements of the specific data and information on the networks. Our solution leverages specific technology characteristics from traditional network defense systems and real-time distributed systems using publish-subscribe broker patterns to form the foundation of a full-spectrum cyber operations capability. Building on this foundation, we present the addition of covert channel communications within the distributed systems framework to protect sensitive Command and Control and Battle Management messaging from adversary intercept and exploitation. Through this combined approach, DoD and Service network defense professionals will be able to meet sophisticated cyberspace threats head-on while simultaneously protecting the data and information critical to warfighting Commands, Services and Agencies.http://archive.org/details/enhancedcyberspa109454049US Air Force (USAF) author.Approved for public release; distribution is unlimited
Understanding Traffic Density from Large-Scale Web Camera Data
Understanding traffic density from large-scale web camera (webcam) videos is
a challenging problem because such videos have low spatial and temporal
resolution, high occlusion and large perspective. To deeply understand traffic
density, we explore both deep learning based and optimization based methods. To
avoid individual vehicle detection and tracking, both methods map the image
into vehicle density map, one based on rank constrained regression and the
other one based on fully convolution networks (FCN). The regression based
method learns different weights for different blocks in the image to increase
freedom degrees of weights and embed perspective information. The FCN based
method jointly estimates vehicle density map and vehicle count with a residual
learning framework to perform end-to-end dense prediction, allowing arbitrary
image resolution, and adapting to different vehicle scales and perspectives. We
analyze and compare both methods, and get insights from optimization based
method to improve deep model. Since existing datasets do not cover all the
challenges in our work, we collected and labelled a large-scale traffic video
dataset, containing 60 million frames from 212 webcams. Both methods are
extensively evaluated and compared on different counting tasks and datasets.
FCN based method significantly reduces the mean absolute error from 10.99 to
5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on
http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data
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