2256492
Autonomic Intrusion Detection System
We propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection in unlabeled audit data streams. The framework owns ability of self-managing: self-labeling, self-updating and self-adapting. Affinity Propagation (AP) uses the framework to learn a subject's behavior through dynamical clustering of the streaming data. The testing results with a large real HTTP log stream demonstrate the effectiveness and efficiency of the method.
15381526
A general framework for adaptive and online detection of web attacks
Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we propose a novel general framework for adaptive and online detection of web attacks. The general framework can be based on any online clustering methods. A detection model based on the framework is able to learn online and deal with "concept drift" in web audit data streams. Str-DBSCAN that we extended DBSCAN to streaming data as well as StrAP are both used to validate the framework. The detection model based on the framework automatically labels the web audit data and adapts to normal behavior changes while identifies attacks through dynamical clustering of the streaming data. A very large size of real HTTP Log data collected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness.
6226035
Online and adaptive anomaly Detection: detecting intrusions in unlabelled audit data streams
Disclosed is a method of lowering the prolactin level in the blood or serum of a person which method comprises administering a therapeutically effective amount of 2-amino-6-allyl-5,6,7,8-tetrahydro-4H-thiazolo-[5,4-d]azepine or a non-toxic, pharmaceutically acceptable acid addition salt thereof.
6043120
Optimal Image Component Transmissions in Multirate Wireless Sensor Networks
In image transmission applications over wireless sensor networks (WSN), energy efficiency and image quality are both important factors for joint optimization. In this paper, we propose a new position-value (P-V) oriented cross layer optimization approach to minimize energy consumption with distortion bounds for image transmission applications in WSN, by exploring importance level diversity for position-magnitude information. To put more effort on transmitting important position information and relatively less effort on unimportant magnitude information, desirable BER, ARQ retry limit, transmission rate and distortion reduction of image are jointly optimized cross PHY, MAC and APP layers, and image quality is assured while energy consumption is minimized. Simulation results demonstrate the effectiveness of the proposed approach in achieving energy efficiency while maintaining image quality.
16736293
Non-negative Matrix Factorization with Orthogonality Constraints and its Application to Raman Spectroscopy
We introduce non-negative matrix factorization with orthogonality constraints (NMFOC) for detection of a target spectrum in a given set of Raman spectra data. An orthogonality measure is defined and two different orthogonality constraints are imposed on the standard NMF to incorporate prior information into the estimation and hence to facilitate the subsequent detection procedure. Both multiplicative and gradient type update rules have been developed. Experimental results are presented to compare NMFOC with the basic NMF in detection, and to demonstrate its effectiveness in the chemical agent detection problem.
15869205
Detection using correlation bound in a linear mixture model
A detection approach, detection with a correlation bound (DCB), is introduced based on a linear mixture model. We use the upper bound of the correlation between the target and mixing components as the detection index, and derive the expression for this correlation bound using the observed data. The proposed method is an unsupervised approach and provides satisfactory detection performance without imposing strong assumptions. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in Raman spectroscopy for the detection of surface-deposited chemical agents.
16621254
Approximate Clustering on Distributed Data Streams
We investigate the problem of clustering on distributed data streams. In particular, we consider the k-median clustering on stream data arriving at distributed sites which communicate through a routing tree. Distributed clustering on high speed data streams is a challenging task due to limited communication capacity, storage space, and computing power at each site. In this paper, we propose a suite of algorithms for computing (1 + epsiv) -approximate k-median clustering over distributed data streams under three different topology settings: topology-oblivious, height-aware, and path-aware. Our algorithms reduce the maximum per node transmission to polylog N (opposed to Omega(N) for transmitting the raw data). We have simulated our algorithms on a distributed stream system with both real and synthetic datasets composed of millions of data. In practice, our algorithms are able to reduce the data transmission to a small fraction of the original data. Moreover, our results indicate that the algorithms are scalable with respect to the data volume, approximation factor, and the number of sites.
6871628
REDUS: finding reducible subspaces in high dimensional data
Finding latent patterns in high dimensional data is an important research problem with numerous applications. The most well known approaches for high dimensional data analysis are feature selection and dimensionality reduction. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In many emerging applications, however, scientists are interested in the local latent patterns held by feature subspaces, which may be invisible via any global transformation.
 In this paper, we investigate the problem of finding strong linear and nonlinear correlations hidden in feature subspaces of high dimensional data. We formalize this problem as identifying reducible subspaces in the full dimensional space. Intuitively, a reducible subspace is a feature subspace whose intrinsic dimensionality is smaller than the number of features. We present an effective algorithm, REDUS, for finding the reducible subspaces. Two key components of our algorithm are finding the overall reducible subspace, and uncovering the individual reducible subspaces from the overall reducible subspace. A broad experimental evaluation demonstrates the effectiveness of our algorithm.