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Hyperspectral images (HSIs) possess non-negative properties for both hyperspectral signatures and abundance coefficients, which can be naturally modeled using cone-based representation. However, in hyperspectral target detection, cone-based methods are barely studied. In this paper, we propose a new regularized cone-based representation approach to hyperspectral target detection, as well as its two...
In our previous work, we have applied ordinary linear regression equation to network anomaly detection. However, the performance of ordinary linear regression equation is susceptible to outliers. Unfortunately, it is almost impossible to obtain a “clean” traffic data set for ordinary regression model due to the burstiness of network traffic and the pervasive network attacks. In this paper, we make...
In this paper, we show that for a given pair of metrics, such as IGTE vs. IGFE, number of packets vs. number of network flows, etc., the functional relation between them may be complex and can not be described perfectly by linear equation. In order to capture this complex relationship, we make use of evidence function framework to automatically determine the optimal model for the metrics. Then we...
In this paper, we illustrate the significance and advantage of combining the results of multiple detection methods. We implement these methods as bolts in a Apache Storm cluster which is a famous real-time computation framework. We simulate two kinds of anomalies — one involving large number of small network flows and the other involving small number of large network flows. The experiments show that...
Anomaly detection has been a hot topic in recent years due to its capability of detecting zero attacks. In this paper, we propose a new on-line anomaly detection method based on LMS algorithm. The basic idea of the LMS-based detector is to predict IGTE using IGFE, given the high linear correlation between them. Using the artificial synthetic data, it is shown that the LMS-based detector possesses...
Anomaly detection has been a hot topic in recent years due to its capability of detecting zero day attacks. In this paper, we propose a new metric called Entropy-Ratio. We validate that the Entropy-Ratio is stationary. Making use of this observation, we combine the Least Mean Square algorithm and the Forward Linear Predictor to propose a new on-line detector called LMS-FLP detector. Using the two...
This paper described an infrared (IR) radiation sensor based on Film Bulk Acoustic-wave Resonator (FBAR). The resonant frequency of FBAR sensor downshifts linearly when there is IR (peak wavelength at 780nm) illumination on the device. This effect attributed to the temperature sensitivity of the FBAR. The noise equivalent temperature difference (NETD) and the detection limit for 780 nm IR of the sensor...
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