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.
14446380
OP-cluster: clustering by tendency in high dimensional space
Clustering is the process of grouping a set of objects into classes of similar objects. Because of unknownness of the hidden patterns in the data sets, the definition of similarity is very subtle. Until recently, similarity measures are typically based on distances, e.g Euclidean distance and cosine distance. We propose a flexible yet powerful clustering model, namely OP-cluster (Order Preserving Cluster). Under this new model, two objects are similar on a subset of dimensions if the values of these two objects induce the same relative order of those dimensions. Such a cluster might arise when the expression levels of (coregulated) genes can rise or fall synchronously in response to a sequence of environment stimuli. Hence, discovery of OP-Cluster is essential in revealing significant gene regulatory networks. A deterministic algorithm is designed and implemented to discover all the significant OP-Clusters. A set of extensive experiments has been done on several real biological data sets to demonstrate its effectiveness and efficiency in detecting coregulated patterns.
8162300
ANNE: An Efficient Framework on View Selection Problem
ROLAP is used to answer queries for analysis support on the data stored in the data warehouse. To fulfill this purpose rapidly and correctly, ROLAP always precompute some views in the data warehouse. However, selecting views to materialize is an NP-hard problem. Some previous works, such as the Greedy Algorithm, BPUS and PBS, have focused on it with high time complexity, which is exponential with the number of dimensions. It leads poor performance even for mid-size datasets. This paper introduces a framework ANNE in which any above algorithm can be applied to solve the problem several orders of magnitude faster than the algorithm itself, while maintaining almost the same effect.
3069611
A General Framework for Fast Co-clustering on Large Datasets Using Matrix Decomposition
Simultaneously clustering columns and rows (co- clustering) of large data matrix is an important problem with wide applications, such as document mining, microarray analysis, and recommendation systems. Several co-clustering algorithms have been shown effective in discovering hidden clustering structures in the data matrix. For a data matrix of m rows and n columns, the time complexity of these methods is usually in the order of mtimesn (if not higher). This limits their applicability to data matrices involving a large number of columns and rows. Moreover, an implicit assumption made by existing co-clustering methods is that the whole data matrix needs to be held in the main memory. In this paper, we propose a general framework, CRD, for co-clustering large datasets utilizing recently developed sampling- based matrix decomposition methods. The time complexity of our approach is linear in m and n. And it does not require the whole data matrix be in the main memory. Experimental results show that CRD achieves competitive accuracy to existing co-clustering methods but with much less computational cost.
14352341
Energy-Distortion-Authentication Optimized Resource Allocation for Secure Wireless Image Streaming
Joint consideration of energy efficiency, multimedia quality and media authenticity has largely been overlooked in the studies of secure wireless multimedia streaming. In this paper, we propose an authentication - resource allocation framework for secure transmission of multimedia streaming over energy constrained wireless networks. The contributions of this research can be summarized in three aspects. Firstly, a hash chain based stream level authentication scheme compatible for JPEG2000 compression standard is proposed. Secondly, link layer energy- distortion is modeled, and the energy-distortion convex hull is efficiently identified, which significantly reduces optimization complexity. Finally, an authentication oriented resource allocation scheme is proposed based on the proposed authentication scheme and link layer energy-distortion convex hull, where the authenticated image quality is optimized according to energy budget constraints.
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.