472683
On the effectiveness of DDoS attacks on statistical filtering
Distributed denial of service (DDoS) attacks pose a serious threat to service availability of the victim network by severely degrading its performance. Recently, there has been significant interest in the use of statistical-based filtering to defend against and mitigate the effect of DDoS attacks. Under this approach, packet statistics are monitored to classify normal and abnormal behaviour. Under attack, packets that are classified as abnormal are dropped by the filter that guards the victim network. We study the effectiveness of DDoS attacks on such statistical-based filtering in a general context where the attackers are "smart". We first give an optimal policy for the filter when the statistical behaviours of both the attackers and the filter are static. We next consider cases where both the attacker and the filter can dynamically change their behaviour, possibly depending on the perceived behaviour of the other party. We observe that while an adaptive filter can effectively defend against a static attacker, the filter can perform much worse if the attacker is more dynamic than perceived.
9658544
Effect of Malicious Synchronization
We study the impact of malicious synchronization on computer systems that serve customers periodically. Systems supporting automatic periodic updates are common in web servers providing regular news update, sports scores or stock quotes. Our study focuses on the possibility of launching an effective low rate attack on the server to degrade performance measured in terms of longer processing time and request drops due to timeouts. The attackers are assumed to behave like normal users and send one request per update cycle. The only parameter utilized in the attack is the timing of the requests sent. By exploiting the periodic nature of the updates, a small number of attackers can herd users' update requests to a cluster and arrive in a short period of time. Herding can be used to discourage new users from joining the system and to modify the user arrival distribution, so that the subsequent burst attack will be effective. While the herding based attacks can be launched with a small amount of resource, they can be easily prevented by adding a small random component to the length of the update interval.
1227971
Rotation of foveated image in the wavelet domain
An advantage of wavelet transform is its efficiency in representing natural images, that is, a natural image can be accurately represented by only a small number of retained wavelet coefficients. It is interesting to know whether some common image operations, e.g., rotation, can be performed very fast in wavelet domain. Preferably, the running time should depend only on the number of retained coefficients, not the size of the original image. However, it is not clear how this can be achieved. In this paper, we consider rotation and images with a special structure: foveated images. Wavelet coefficients of a foveated image vanish outside an arrangement of circles. We exploit this structure to derive algorithms that accurately approximate rotation. The running time is /spl Theta/(m) where m is the number of retained coefficients. Experiments show the accuracy of the approximation.
11249256
A unified framework for resolving ambiguity in copy detection
Copy detection is an important component of digital rights management and can be implemented using a retrieval-based approach. Under this approach, a query image, suspected to be a copy, is compared against all the images in the owner database. The comparison is done based on a distance metric in feature space. The performance of such a system depends on the mutual separation of the feature representation of the images in the database. In this paper we propose a framework that increases this mutual separation by literally shifting them away from each other. The idea of modifying the features derives its inspiration from the field of watermarking. It is also important to make sure that the semantics of the images do not change after modification. Thus the focus of this paper is on how to modify the images in the database, so that the mutual separation between the images in feature space is above a certain threshold and the distortion induced is minimized. This problem can be formulated as a non-convex optimization problem which is difficult to solve. We propose a restriction of the problem and solve it using second-order cone programming. We present a practical implementation of our framework, named RAM, which uses AFMT as the feature representation. We conduct experiments to test the performance of RAM.
12953441
A Simultaneous Search Problem
Abstract. We introduce a new search problem motivated by computational metrology. The problem is as follows: we would like to locate two unknown numbers x,y ∈ [0,1] with as little uncertainty as possible, using some given number k of probes. Each probe is specified by a real number r∈ [0,1] . After a probe at r , we are told whether x≤ r or x \geq r , and whether y≤ r or y\geq r . We derive the optimal strategy and prove that the asymptotic behavior of the total uncertainty after k probes is 13/7 2-(k+1)/2 for odd k and 13/10 2-k/2 for even k .
42674644
Shortest Path amidst Disc Obstacles Is Computable
An open question in Exact Geometric Computation is whether there are transcendental computations that can be made "geometrically exact". Perhaps the simplest such problem in computational geometry is that of computing the shortest obstacle-avoiding path between two points p,q in the plane, where the obstacles are a collection of n discs. This problem can be solved in O(n2log n) time in the Real RAM model, but nothing was known about its computability in the standard (Turing) model of computation. We first give a direct proof of the Turing-computability of this problem, provided the radii of the discs are rationally related. We make the usual assumption that the numerical input data are real algebraic numbers. By appealing to effective bounds from transcendental number theory, we further show a single-exponential time upper bound when the input numbers are rational. Our result appears to be the first example of a non-algebraic combinatorial problem which is shown computable. It is also a rare example of transcendental number theory yielding positive computational results.
6693066
Nnew: nearest neighbor expansion by weighting in image database retrieval
Various systems have been developed for supporting content-based image retrieval. Most systems make very strong assumptions in modeling users’ query concepts. However, since the information need of users can be very diverse, these assumptions may not always hold and hence can lead to poor search results. For instance, if a system assumes that the query-concept is convex but a user issues a disjunctive query, and vice versa, the search result cannot be satisfactory. In this study, we propose a method that can approximate more complex (non-convex and disjunctive) query concepts. Our method uses intelligent modeling and learning to increase query speed and accuracy. Empirical results show that our method converges consistently faster than some traditional approaches on different datasets.
6932749
Adaptive stream resource management using Kalman Filters
To answer user queries efficiently, a stream management system must handle continuous, high-volume, possibly noisy, and time-varying data streams. One major research area in stream management seeks to allocate resources (such as network bandwidth and memory) to query plans, either to minimize resource usage under a precision requirement, or to maximize precision of results under resource constraints. To date, many solutions have been proposed; however, most solutions are ad hoc with hard-coded heuristics to generate query plans. In contrast, we perceive stream resource management as fundamentally a filtering problem, in which the objective is to filter out as much data as possible to conserve resources, provided that the precision standards can be met. We select the Kalman Filter as a general and adaptive filtering solution for conserving resources. The Kalman Filter has the ability to adapt to various stream characteristics, sensor noise, and time variance. Furthermore, we realize a significant performance boost by switching from traditional methods of caching static data (which can soon become stale) to our method of caching dynamic procedures that can predict data reliably at the server without the clients' involvement. In this work we focus on minimization of communication overhead for both synthetic and real-world streams. Through examples and empirical studies, we demonstrate the flexibility and effectiveness of using the Kalman Filter as a solution for managing trade-offs between precision of results and resources in satisfying stream queries.
35270580
Mining image features for efficient query processing
The number of features required to depict an image can be very large. Using all features simultaneously to measure image similarity and to learn image query-concepts can suffer from the problem of dimensionality curse, which degrades both search accuracy and search speed. Regarding search accuracy, the presence of irrelevant features with respect to a query can contaminate similarity measurement, and hence decrease both the recall and precision of that query. To remedy this problem, we present a mining method that learns online users' query concepts and identifies important features quickly. Regarding search speed, the presence of a large number of features can slow down query-concept learning and indexing performance. We propose a divide-and-conquer method that divides the concept-learning task into G subtasks to achieve speedup. We notice that a task must be divided carefully, or search accuracy may suffer. We thus propose a genetic-based mining algorithm to discover good feature groupings. Through analysis and mining results, we observe that organizing image features in a multi-resolution manner and minimizing intra-group feature correlation, can speed up query-concept learning substantially while maintaining high search accuracy.
14013099
DynDex: a dynamic and non-metric space indexer
To date, almost all research work in the Content-Based Image Retrieval (CBIR) community has used Minkowski-like functions to measure similarity between images. In this paper, we first present a non-metric distance function, dynamic partial function (DPF), which works significantly better than Minkowski-like functions for measuring perceptual similarity; and we explain DPF's link to similarity theories in cognitive science. We then propose DynDex, an indexing method that deals with both the dynamic and non-metric aspects of the distance function. DynDex employs statistical methods including distance-based classification and bagging to enable efficient indexing with DPF. In addition to its efficiency for conducting similarity searches in very high-dimensional spaces, we show that DynDex remains effective when features are weighted dynamically for supporting personalized searches.
10581413
Answering queries with useful bindings
In information-integration systems, sources may have diverse and limited query capabilities. To obtain maximum information from these restrictive sources to answer a query, one can access sources that are not specified in the query (i.e., off-query sources). In this article, we propose a query-planning framework to answer queries in the presence of limited access patterns. In the framework, a query and source descriptions are translated to a recursive datalog program. We then solve optimization problems in this framework, including how to decide whether accessing off-query sources is necessary, how to choose useful sources for a query, and how to test query containment. We develop algorithms to solve these problems, and thus construct an efficient program to answer a query.