37,624 research outputs found
Selective sampling importance resampling particle filter tracking with multibag subspace restoration
Applying Bag of System Calls for Anomalous Behavior Detection of Applications in Linux Containers
In this paper, we present the results of using bags of system calls for
learning the behavior of Linux containers for use in anomaly-detection based
intrusion detection system. By using system calls of the containers monitored
from the host kernel for anomaly detection, the system does not require any
prior knowledge of the container nature, neither does it require altering the
container or the host kernel.Comment: Published version available on IEEE Xplore
(http://ieeexplore.ieee.org/document/7414047/) arXiv admin note: substantial
text overlap with arXiv:1611.0305
Supplement to MTI Study on Selective Passenger Screening in the Mass Transit Rail Environment, MTI Report 09-05
This supplement updates and adds to MTIs 2007 report on Selective Screening of Rail Passengers (Jenkins and Butterworth MTI 07-06: Selective Screening of Rail Passengers). The report reviews current screening programs implemented (or planned) by nine transit agencies, identifying best practices. The authors also discuss why three other transit agencies decided not to implement passenger screening at this time. The supplement reconfirms earlier conclusions that selective screening is a viable security option, but that effective screening must be based on clear policies and carefully managed to avoid perceptions of racial or ethnic profiling, and that screening must have public support. The supplement also addresses new developments, such as vapor-wake detection canines, continuing challenges, and areas of debate. Those interested should also read MTI S-09-01 Rail Passenger Selective Screening Summit
Rail Passenger Selective Screening Summit, MTI S-09-01
This publication is an edited transcript of the Rail Passenger Selective Screening Summit, which was co-sponsored by MTI and the American Public Transportation Association (APTA) in Chicago, Illinois on June 18, 2009, during APTA´s annual Rail Conference. The workshop was moderated by Brian Michael Jenkins, director, Mineta Transportation Institute\u27s National Transportation Security Center of Excellence (NTSCOE). Speakers included Bruce R. Butterworth, co-author, Selective Screening of Rail Passengers; Greg Hull, president, American Public Transportation Association (APTA); Paul MacMillan, chief of police, Massachusetts Bay Transportation Authority, Transit Police Department; Ron Masciana, deputy chief, Metropolitan Transit Authority (MTA), New York; Jesus Ojeda, security coordinator, Southern California Regional Rail Authority; Ed Phillips, operations deputy, Office of Security, Amtrak; and John P. Sammon, assistant administrator, Transportation Sector Network Management, Transportation Security Administration (TSA
Audio Event Detection using Weakly Labeled Data
Acoustic event detection is essential for content analysis and description of
multimedia recordings. The majority of current literature on the topic learns
the detectors through fully-supervised techniques employing strongly labeled
data. However, the labels available for majority of multimedia data are
generally weak and do not provide sufficient detail for such methods to be
employed. In this paper we propose a framework for learning acoustic event
detectors using only weakly labeled data. We first show that audio event
detection using weak labels can be formulated as an Multiple Instance Learning
problem. We then suggest two frameworks for solving multiple-instance learning,
one based on support vector machines, and the other on neural networks. The
proposed methods can help in removing the time consuming and expensive process
of manually annotating data to facilitate fully supervised learning. Moreover,
it can not only detect events in a recording but can also provide temporal
locations of events in the recording. This helps in obtaining a complete
description of the recording and is notable since temporal information was
never known in the first place in weakly labeled data.Comment: ACM Multimedia 201
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