5,843 research outputs found
An improved adaptive sidelobe blanker
We propose a two-stage detector consisting of a subspace
detector followed by the whitened adaptive beamformer orthogonal rejection test. The performance analysis shows that it possesses the constant false alarm rate property with respect to the unknown covariance matrix of the noise and that it can guarantee a wider range of directivity values with respect to previously proposed two-stage detectors. The probability of false alarm and the probability of detection (for both matched and mismatched signals) have been evaluated by means of numerical integration techniques
Spectral Detection of Human Skin in VIS-SWIR Hyperspectral Imagery without Radiometric Calibration
Many spectral detection algorithms require precise ground truth measurements that are hand-selected in the image to apply radiometric calibration, converting image pixels into estimated reflectance vectors. That process is impractical for mobile, real-time hyperspectral target detection systems, which cannot empirically derive a pixel-to-reflectance relationship from objects in the image. Implementing automatic target recognition on high-speed snapshot hyperspectral cameras requires the ability to spectrally detect targets without performing radiometric calibration. This thesis demonstrates human skin detection on hyperspectral data collected at a high frame rate without using calibration panels, even as the illumination in the scene changes. Compared to an established skin detection method that requires calibration panels, the illumination-invariant methods in this thesis achieve nearly as good detection performance in sunny scenes and superior detection performance in cloudy scenes
Performance metrics for the evaluation of hyperspectral chemical identification systems
Remote sensing of chemical vapor plumes is a difficult but important task for many military and civilian applications. Hyperspectral sensors operating in the long-wave infrared regime have well-demonstrated detection capabilities. However, the identification of a plume’s chemical constituents, based on a chemical library, is a multiple hypothesis testing problem which standard detection metrics do not fully describe. We propose using an additional performance metric for identification based on the so-called Dice index. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and identification metric. Using the proposed metrics, we demonstrate that the intuitive system design of a detector bank followed by an identifier is indeed justified when incorporating performance information beyond the standard detection metrics.United States. Defense Threat Reduction Agency (Air Force contract FA8721-05-C-0002
A notch filter for ship detection with polarimetric SAR data
Ship detection with Synthetic Aperture Radar (SAR) is a major topic for the security and monitoring of maritime areas. One of the advantages of using SAR lay in its capability to acquire useful images with any-weather conditions and at night time. Specifically, this paper proposes a new methodology exploiting polarimetric acquisitions (dual- and quad-polarimetric).
The methodology adopted for the detector algorithm was introduced by the author and performs a perturbation analysis in space of polarimetric targets checking for coherence between the target to detect and its perturbed version on the data. In the present work, this methodology is optimized for detection of marine features. In the end, the algorithm can be considered to be a negative (notch) filter focused on sea. Consequently, all the features which have a polarimetric behavior different from the sea are detected (i.e. ships, icebergs, buoys, etc). Moreover, a dual polarimetric version of the detector is designed, to be exploited in the circumstances where quad polarimetric data cannot be acquired.
The detector was tested with TerraSAR-X quad polarimetric data showing significant agreement with the available ground truth. Moreover, the theoretical performances of the detector are tested with Monte Carlo simulations in order to extract the probabilities of detection and false alarm. An important result is that the detector is, up to some extend, independent of the sea conditions
Performance Evaluation of Network Anomaly Detection Systems
Nowadays, there is a huge and growing concern about security in information and communication
technology (ICT) among the scientific community because any attack or anomaly in
the network can greatly affect many domains such as national security, private data storage,
social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad
research area, and many different techniques and approaches for this purpose have emerged
through the years.
Attacks, problems, and internal failures when not detected early may badly harm an
entire Network system. Thus, this thesis presents an autonomous profile-based anomaly detection
system based on the statistical method Principal Component Analysis (PCADS-AD). This
approach creates a network profile called Digital Signature of Network Segment using Flow Analysis
(DSNSF) that denotes the predicted normal behavior of a network traffic activity through
historical data analysis. That digital signature is used as a threshold for volume anomaly detection
to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow
attributes: Bits, Packets and Number of Flows to detect problems, and Source and Destination IP
addresses and Ports, to provides the network administrator necessary information to solve them.
Via evaluation techniques, addition of a different anomaly detection approach, and
comparisons to other methods performed in this thesis using real network traffic data, results
showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection
accuracy on the detection schema.
The observed results seek to contribute to the advance of the state of the art in methods
and strategies for anomaly detection that aim to surpass some challenges that emerge from
the constant growth in complexity, speed and size of today’s large scale networks, also providing
high-value results for a better detection in real time.Atualmente, existe uma enorme e crescente preocupação com segurança em tecnologia
da informação e comunicação (TIC) entre a comunidade científica. Isto porque qualquer
ataque ou anomalia na rede pode afetar a qualidade, interoperabilidade, disponibilidade, e integridade
em muitos domínios, como segurança nacional, armazenamento de dados privados,
bem-estar social, questões econômicas, e assim por diante. Portanto, a deteção de anomalias
é uma ampla área de pesquisa, e muitas técnicas e abordagens diferentes para esse propósito
surgiram ao longo dos anos.
Ataques, problemas e falhas internas quando não detetados precocemente podem prejudicar
gravemente todo um sistema de rede. Assim, esta Tese apresenta um sistema autônomo
de deteção de anomalias baseado em perfil utilizando o método estatístico Análise de Componentes
Principais (PCADS-AD). Essa abordagem cria um perfil de rede chamado Assinatura Digital
do Segmento de Rede usando Análise de Fluxos (DSNSF) que denota o comportamento normal
previsto de uma atividade de tráfego de rede por meio da análise de dados históricos. Essa
assinatura digital é utilizada como um limiar para deteção de anomalia de volume e identificar
disparidades na tendência de tráfego normal. O sistema proposto utiliza sete atributos de fluxo
de tráfego: bits, pacotes e número de fluxos para detetar problemas, além de endereços IP e
portas de origem e destino para fornecer ao administrador de rede as informações necessárias
para resolvê-los.
Por meio da utilização de métricas de avaliação, do acrescimento de uma abordagem
de deteção distinta da proposta principal e comparações com outros métodos realizados nesta
tese usando dados reais de tráfego de rede, os resultados mostraram boas previsões de tráfego
pelo DSNSF e resultados encorajadores quanto a geração de alarmes falsos e precisão de deteção.
Com os resultados observados nesta tese, este trabalho de doutoramento busca contribuir
para o avanço do estado da arte em métodos e estratégias de deteção de anomalias,
visando superar alguns desafios que emergem do constante crescimento em complexidade, velocidade
e tamanho das redes de grande porte da atualidade, proporcionando também alta
performance. Ainda, a baixa complexidade e agilidade do sistema proposto contribuem para
que possa ser aplicado a deteção em tempo real
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