27638687
A latent class modeling approach to detect network intrusion
This study presents a latent class modeling approach to examine network traffic data when labeled abnormal events are absent in training data, or such events are insufficient to fit a conventional regression model. Using six anomaly-associated risk factors identified from previous studies, the latent class model based on an unlabeled sample yielded acceptable classification results compared with a logistic regression model based on a labeled sample (correctly classified: 0.95 vs. 0.98, sensitivity: 0.99 vs. 0.99, and specificity: 0.77 vs. 0.97). The study demonstrates a great potency for using the latent class modeling technique to analyze network traffic data.
14073504
A multinomial logistic regression modeling approach for anomaly intrusion detection
Although researchers have long studied using statistical modeling techniques to detect anomaly intrusion and profile user behavior, the feasibility of applying multinomial logistic regression modeling to predict multi-attack types has not been addressed, and the risk factors associated with individual major attacks remain unclear. To address the gaps, this study used the KDD-cup 1999 data and bootstrap simulation method to fit 3000 multinomial logistic regression models with the most frequent attack types (probe, DoS, U2R, and R2L) as an unordered independent variable, and identified 13 risk factors that are statistically significantly associated with these attacks. These risk factors were then used to construct a final multinomial model that had an ROC area of 0.99 for detecting abnormal events. Compared with the top KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm, the multinomial logistic model-based classification results had similar sensitivity values in detecting normal (98.3% vs. 99.5%), probe (85.6% vs. 83.3%), and DoS (97.2% vs. 97.1%); remarkably high sensitivity in U2R (25.9% vs. 13.2%) and R2L (11.2% vs. 8.4%); and a significantly lower overall misclassification rate (18.9% vs. 35.7%). The study emphasizes that the multinomial logistic regression modeling technique with the 13 risk factors provides a robust approach to detect anomaly intrusion.
18765129
Security Analysis of Enterprise Network Based on Stochastic Game Nets Model
In this paper, we propose a novel modeling method, Stochastic Game Nets (SGN), and use it to model and analyze the security issues in enterprise networks. Firstly, the definition and modeling algorithm of Stochastic Game Nets are given. And then we apply the Stochastic Game Nets method to describe the attack and defense course in the enterprise networks successfully, and find a Nash equilibrium. Finally we analyze the confidentiality and integrity of the enterprise network quantificationally based on the model. The method can also be applied to other areas with respect to a game.
2358468
Human Action Recognition by Semilatent Topic Models
We propose two new models for human action recognition from video sequences using topic models. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". Our models differ from previous latent topic models for visual recognition in two major aspects: first of all, the latent topics in our models directly correspond to class labels; second, some of the latent variables in previous topic models become observed in our case. Our models have several advantages over other latent topic models used in visual recognition. First of all, the training is much easier due to the decoupling of the model parameters. Second, it alleviates the issue of how to choose the appropriate number of latent topics. Third, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification results on five different data sets. Our results are either comparable to, or significantly better than previously published results on these data sets.
8808466
Joint region tracking with switching hypothesized measurements
We propose a switching hypothesized measurements (SHM) model supporting multimodal probability distributions and present the application of the model in handling potential variability in visual environments when tracking multiple objects jointly. For a set of occlusion hypotheses, a frame is measured once under each hypothesis, resulting in a set of measurements at each time instant. A computationally efficient SHM filter is derived for online joint region tracking. Both occlusion relationships and states of the objects are recursively estimated from the history of hypothesized measurements. The reference image is updated adaptively to deal with appearance changes of the objects. The SHM model is generally applicable to various dynamic processes with multiple alternative measurement methods.
8367818
Segment Hough Transform -- a Novel Hough-based Algorithm for Curve Detection
A new Hough-based algorithm termed segment Hough transform (SHT) is proposed to detect the curve in the binary image. The segment-transform, which is the main idea of the novel approach, makes the curve detection very efficient. Thanks to the chain code termed angle chain code (ACC) as well as the modified direction measurement, the segmentation operation is also considerably fast and precise. Compared with the conventional Hough transform (CHT), our algorithm is dramatically faster (by over 20 times) And when the background is complex, the SHT is about four times faster than the random Hough transform (RHT), Furthermore, the proposed algorithm inherits the high robustness from CHT. In some situations, it is even more accurate. We implement the novel algorithm in an embedded system to estimate passengers flow on the bus. The detection rate is over 95% which indicates it is quite suitable for real-time application.
121024775
Arbitrarily smooth orthogonal nonseparable wavelets in R 2
For each $r\in\N$, we construct a family of bivariate orthogonal wavelets with compact support that are nonseparable and have vanishing moments of order r or less. The starting point of the construction is a scaling function that satisfies a dilation equation with special coefficients and a special dilation matrix M: the coefficients are aligned along two adjacent rows, and $\detwo$. We prove that if $\M^2=\pm 2I$, e.\,g., %%$\M=\ourmtx$ or $\M=\qsymtx$, $M=({0\,\,2 \atop 1\,\,0})$ or $M=({1\,\,\phantom{-}1 \atop 1\,\,-1})$, then the smoothness of the wavelets improves asymptotically by $1-\frac{1}{2}\log_23 \approx 0.2075$ when r is incremented by 1. Hence they can be made arbitrarily smooth by choosing r large enough.