Journal ArticleDOI
A transform domain-based anomaly detection approach to network-wide traffic
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TLDR
A novel method for detecting traffic anomalies in a network by exacting and capturing their features in the transform domain by combining network topology information and transform-domain analysis in the given time window and shows that the specious traffic components can be found and identified.About:
This article is published in Journal of Network and Computer Applications.The article was published on 2014-04-01. It has received 114 citations till now. The article focuses on the topics: Traffic generation model & Network topology.read more
Citations
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Journal ArticleDOI
Touch-less interactive augmented reality game on vision-based wearable device
TL;DR: In this article, a touchless motion interaction technology is designed and evaluated in order to develop touch-less, interactive and augmented reality games on vision-based wearable device, and three primitive AR games with eleven dynamic gestures are developed based on the proposed touchless interaction technology as proof.
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Preprint Touch-less Interactive Augmented Reality Game on Vision Based Wearable Device
TL;DR: In this paper, a touch-less motion interaction technology is designed and evaluated in order to develop touchless, interactive and augmented reality games on a vision-based wearable device, and three primitive AR games with eleven dynamic gestures are developed based on the proposed touchless interaction technology as proof.
Journal ArticleDOI
Web traffic anomaly detection using C-LSTM neural networks
Tae Young Kim,Sung-Bae Cho +1 more
TL;DR: A C-LSTM neural network for effectively modeling the spatial and temporal information contained in traffic data, which is a one-dimensional time series signal, and outperforms other state-of-the-art machine learning techniques on Yahoo's well-known Webscope S5 dataset.
Journal ArticleDOI
A survey of distributed denial-of-service attack, prevention, and mitigation techniques:
TL;DR: A systematic analysis of distributed denial-of-service attacks including motivations and evolution, analysis of different attacks so far, protection techniques and mitigation techniques, and possible limitations and challenges of existing research are provided.
Journal ArticleDOI
A Self-Assessment Stereo Capture Model Applicable to the Internet of Things.
TL;DR: The experimental results show that the proposed evaluation criteria can effectively predict the visual perception of stereo capture quality for long-distance shooting.
References
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Proceedings ArticleDOI
Mining anomalies using traffic feature distributions
TL;DR: It is argued that the distributions of packet features observed in flow traces reveals both the presence and the structure of a wide range of anomalies, and that using feature distributions, anomalies naturally fall into distinct and meaningful clusters that can be used to automatically classify anomalies and to uncover new anomaly types.
Proceedings ArticleDOI
Diagnosing network-wide traffic anomalies
TL;DR: A general method based on a separation of the high-dimensional space occupied by a set of network traffic measurements into disjoint subspaces corresponding to normal and anomalous network conditions to diagnose anomalies is proposed.
Proceedings ArticleDOI
A signal analysis of network traffic anomalies
TL;DR: This paper reports results of signal analysis of four classes of network traffic anomalies: outages, flash crowds, attacks and measurement failures, and shows that wavelet filters are quite effective at exposing the details of both ambient and anomalous traffic.
Proceedings ArticleDOI
Profiling internet backbone traffic: behavior models and applications
TL;DR: A general methodology for building comprehensive behavior profiles of Internet backbone traffic in terms of communication patterns of end-hosts and services and can identify common traffic profiles as well as anomalous behavior patterns that are of interest to network operators and security analysts is presented.
Proceedings ArticleDOI
ANTIDOTE: understanding and defending against poisoning of anomaly detectors
Benjamin I. P. Rubinstein,Blaine Nelson,Ling Huang,Anthony D. Joseph,Shing-hon Lau,Satish Rao,Nina Taft,J. D. Tygar +7 more
TL;DR: This work proposes an antidote based on techniques from robust statistics and presents a new robust PCA-based detector that substantially reduces the effectiveness of poisoning for a variety of scenarios and indeed maintains a significantly better balance between false positives and false negatives than the original method when under attack.