12108467
Tracking Anomalous Behaviors of Name Servers by Mining DNS Traffic
This paper seeks to quantitatively understand the nature of the current threat towards the common name servers. A new tracking technique based on statistical model is proposed to locate the anomalous name servers by analyzing the real-world DNS traffic. After summarizing the attacks towards DNS, the detection method based on associative feature analysis is presented. Experiments are conducted which highlighting both the payload anomaly and the data flow anomaly, and the experimental results reveal the efficiency of our method in detecting the anomalous behaviors of name servers.
10918777
Online Signature Verification by Combining Shape Contexts and Local Features
Most parameter-based online signature verification methods achieve correspondence between the points of two signatures by minimizing the accumulation of their local feature distances. The matching based on minimizing the local feature distances alone is not adequate since the point contains not only local features but the distribution of the remaining points relative to it. One useful way to get correspondences between points on two shapes and measure the similarity of the two shapes is to use the shape context, since this descriptor can be used to describe the distributive relationship between a reference point and the remaining points on a shape. In this paper, we introduce a shape context descriptor for describing an online signature point which contains both 2D spatial information and a time stamp. A common algorithm, dynamic time warp (DTW), is used for the elastic matching between two signatures. When combining shape contexts and local features, we achieve better results than when using only the local features. We evaluate the proposed method on a signature database from the First International Signature Verification Competition (SVC2004). Experimental results demonstrate that the shape context is a good feature and has available complementarity for describing the signature point. The best result by combining the shape contexts and the local features yields an Equal Error Rate (EER) of 6.77% for five references.
9741896
Authoritative Server's Impact on Domain Name System's Performance and Security
40597607
Towards an Analysis of Source-Rewriting Anonymous Systems in a Lossy Environment
Anonymity that protects the identity of participants is a basic requirement for many network-based applications. For practical anonymous communication systems, strong anonymity, reliability and efficiency are three critical properties, but they may degrade in a lossy environment (nodes failure or links crash). This paper takes Crowds system as an example and statistically analyzes the degradation of these properties in lossy environment. Theoretical deduction and numerical analysis illustrate that Crowds is reliable enough to tolerate nodes failure, but both efficiency and anonymity will degrade. With the theoretical analysis results in this paper, a Crowds system operator can adjust the parameter pf to be tolerant of different node failure rate and node corruption rate.
35117974
Online signature verification based on null component analysis and principal component analysis
This paper describes a method for stroke-based online signature verification using null component analysis (NCA) and principal component analysis (PCA). After the segmentation and flexible matching of the signature, we extract stable segments from each reference signature in order that the segment sequences have the same length. The reference set of feature vectors are transformed and separated into null components (NCs) and principal components (PCs) by K-L transform. Online signature verification is a special two-category classification problem and there is not a single available forgery set in an actual system. Therefore, it is different from the typical application of PCA in pattern recognition that both NCA and PCA are used to respectively analyze stable and unstable components of genuine reference set. Experiments on a data set containing a total 1,410 signatures of 94 signers show that the NCA/PCA-based online signature verification method can achieve better results. The best result yields an equal error rate of 1.9%.
7181717
A Biased Support Vector Machine Approach to Web Filtering
Web filtering is an inductive process which automatically builds a filter by learning the description of user interest from a set of pre-assigned web pages, and uses the filter to assign unprocessed web pages. In web filtering, content similarity analysis is the core problem, the automatic-learning and relativity-analysis abilities of machine learning algorithms help solve the above problems and make ML useful in web filtering. While in practical applications, different filtering task implies different userinterest and thus implies different filtering result. This work studies how to adjust the web filtering results to be more fit for the user interest. The web filtering result are divided into three categories: relative pages, similar pages and homologous pages according to different user interest. A Biased Support Vector Machine (BSVM) algorithm, which imports a stimulant function, uses training examples distribution n+/n−− and a user-adaptable parameter k to deal imbalancedly different classes of the pre-assigned pages, is introduced to adjust the filtering result to be best fit for the user interest. Experiments show that BSVM can greatly improve the web filtering performance.
1210599
Joint error concealment and error recovery for consecutive frame losses under the unbalanced multiple description coding architecture
Real-time video transmission over error-prone wireless networks often experiences consecutive frame losses due to either temporary link outages or traffic congestion. Although error concealment (EC) techniques have been extensively studied, they usually cannot handle the problem. Thus, we envision using EC under the unbalanced multiple description coding (UMDC) architecture. UMDC has almost no coding delay and can produce two descriptions at any bit-rates adaptive to different path bandwidths. In this paper, we propose an iterative EC algorithm able to adaptively exploit both the high-resolution (HR) and low-resolution (LR) information via multi-hypothesis weights. It is applied to both lost HR frames and following undecoded ones. Considering error propagation, we design an interframe error recovery (ER) algorithm for the undecoded HR frames. It iteratively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR stream with respect to the intermediate information from EC. The joint design of EC and ER can be applied to most UMD approaches. Extensive experiments have been carried out under different conditions. The proposed EC technique exhibits high PSNR gains versus the usual ones under the UMDC architecture and the classical ones without the support of UMDC. The proposed ER technique is efficient in reducing error drift. In conclusion, joint EC and ER can provide satisfactory performance on both PSNR and visual quality.
10605413
Center-configuration selection technique for the reconfigurable modular robot
The reconfigurable modular robot has an enormous amount of configurations to adapt to various environments and tasks. It greatly increases the complexity of configuration research in that the possible configuration number of the reconfigurable modular robot grows exponentially with the increase of module number. Being the initial configuration or the basic configuration of the reconfigurable robot, the center-configuration plays a crucial role in system’s actual applications. In this paper, a novel center-configuration selection technique has been proposed for reconfigurable modular robots. Based on the similarities between configurations’ transformation and graph theory, configuration network has been applied in the modeling and analyzing of these configurations. Configuration adjacency matrix, reconfirmation cost matrix, and center-configuration coefficient have been defined for the configuration network correspondingly. Being similar to the center-location problem, the center configuration has been selected according to the largest center-configuration coefficient. As an example of the reconfigurable robotic system, AMOEBA-I, a three-module reconfigurable robot with nine configurations which was developed in Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), has been introduced briefly. According to the numerical simulation result, the center-configuration coefficients for these nine configurations have been calculated and compared to validate this technique. Lastly, a center-configuration selection example is provided with consideration of the adjacent configurations. The center-configuration selection technique proposed in this paper is also available to other reconfigurable modular robots.
16377263
Personal Name Recognition Based on Categorized Linguistic Knowledge
This paper proposes an integrated approach for personal name recognition (PNR) in Chinese by utilizing both statistical language models and categorized linguistic knowledge. Various formulas are proposed for calculating personal name credibility and context credibility for different types of personal names. Experiment is conducted on large-scale corpus to evaluate the approach and the F-1 scores has reached 98.85% and 92.73% respectively in close and open test.
11982779
A Wireless Intrusion Detection Method Based on Dynamic Growing Neural Network
In this paper an intrusion detection method based on dynamic growing neural network (DGNN) for wireless networking is presented. DGNN is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent overfitting the training data just like decision tree learning. The intrusion detection method is an anomaly detection method and the feature is selected from the packets. In the experiments, we first check the ability of the neural network and then use it to perform detection in a WLAN. The results show that it can detect new intrusion behavior and some improving methods are presented in the conclusions
10784212
Approximate GCDs of polynomials and SOS relaxation
min p,u1,...,us ‖f1−p·u1‖2+‖f2−p·u2‖2+· · ·+‖fs−p·us‖2 (1) where p, u1, . . . , us are polynomials such that deg(p) ≤ k, deg(p · ui) ≤ di = deg fi for 1 ≤ i ≤ s. Here ‖f‖2 denotes the norm of the coefficient vector of polynomial f . The minimization problem has many different formulations and various numeric optimization strategies have been proposed, see [1, 3] and references therein. In particular, Karmarkar and Lakshman [4] proposed an algorithm based on the global minimization of a rational function to compute the approximate GCD of univariate polynomials. The most expensive part of their algorithm is to find all the real solutions of two bivariate polynomials with high degrees. It has been shown in [8] that sum of squares (SOS) relaxation[5, 9] can be used to find the global minimum of the rational function arising from the approximate GCD computation. The SOS relaxation can be solved by reformulating them as semidefinite programs (SDPs), which in turn are solved efficiently by using interior point methods [7]. Motivated by the interesting results obtained in [8], we show how to apply SOS relaxation to solve different optimization problems formulated in [1, 3, 4]. Let fi,ui,p be coefficient vectors corresponding to polynomials fi, ui, p respectively, and Ai = Ai(p) be convolution ∗ This research was partially supported by NKBRPC (2004CB318000) and the Chinese National Natural Science Foundation under Grant 10401035 (Li and Zhi).