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Automatic Inference and Enforcement of Kernel Data Structure Invariants

by Arati Baliga, Vinod Ganapathy, Liviu Iftode
"... Kernel-level rootkits affect system security by modifying key kernel data structures to achieve a variety of malicious goals. While early rootkits modified control data structures, such as the system call table and values of function pointers, recent work has demonstrated rootkits that maliciously m ..."
Abstract - Cited by 57 (6 self) - Add to MetaCart
novel rootkit detection technique that automatically detects rootkits that modify both control and non-control data. The key idea is to externally observe the execution of the kernel during a training period and hypothesize invariants on kernel data structures. These invariants are used

Detecting kernel-level rootkits using data structure invariants

by Arati Baliga, Vinod Ganapathy, Liviu Iftode - IEEE TDSC
"... Abstract—Rootkits affect system security by modifying kernel data structures to achieve a variety of malicious goals. While early rootkits modified control data structures, such as the system call table and values of function pointers, recent work has demonstrated rootkits that maliciously modify no ..."
Abstract - Cited by 18 (8 self) - Add to MetaCart
is to externally observe the execution of the kernel during an inference phase and hypothesize invariants on kernel data structures. A rootkit detection phase uses these invariants as specifications of data structure integrity. During this phase, violation of invariants indicates an infection. We have implemented

Abstract DITTO: Automatic Incrementalization of Data Structure Invariant Checks (in Java)

by Ajeet Shankar
"... We present DITTO, an automatic incrementalizer for dynamic, sideeffect-free data structure invariant checks. Incrementalization speeds up the execution of a check by reusing its previous executions, checking the invariant anew only on the changed parts of the data structure. DITTO exploits propertie ..."
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We present DITTO, an automatic incrementalizer for dynamic, sideeffect-free data structure invariant checks. Incrementalization speeds up the execution of a check by reusing its previous executions, checking the invariant anew only on the changed parts of the data structure. DITTO exploits

1 DITTO: Automatic Incrementalization of Data StructureInvariant Checks (in Java)

by Ajeet Shankar
"... Abstract We present DITTO, an automatic incrementalizer for dynamic, side-effect-free data structure invariant checks. Incrementalization speeds up the execution of a check by reusing its previous executions, check-ing the invariant anew only on the changed parts of the data structure. DITTO exploit ..."
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Abstract We present DITTO, an automatic incrementalizer for dynamic, side-effect-free data structure invariant checks. Incrementalization speeds up the execution of a check by reusing its previous executions, check-ing the invariant anew only on the changed parts of the data structure. DITTO

Universal Symbolic Execution and its Application to Likely Data Structure Invariant Generation

by Yamini Kannan, Koushik Sen , 2008
"... We consider the problem of automatically inferring likely program invariants from execution traces. In this paper, we focus on inference of invariants that hold over data structures in the program. Properties of data structures can be specified by means of local axioms asserted over a bounded fragme ..."
Abstract - Cited by 10 (0 self) - Add to MetaCart
We consider the problem of automatically inferring likely program invariants from execution traces. In this paper, we focus on inference of invariants that hold over data structures in the program. Properties of data structures can be specified by means of local axioms asserted over a bounded

An affine invariant interest point detector

by Krystian Mikolajczyk, Cordelia Schmid - In Proceedings of the 7th European Conference on Computer Vision , 2002
"... Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of ..."
Abstract - Cited by 1467 (55 self) - Add to MetaCart
of the neighbourhood of an interest point. Our approach allows to solve for these problems simultaneously. It is based on three key ideas: 1) The second moment matrix computed in a point can be used to normalize a region in an affine invariant way (skew and stretch). 2) The scale of the local structure is indicated

Querying Semi-Structured Data

by Serge Abiteboul , 1997
"... ..."
Abstract - Cited by 530 (18 self) - Add to MetaCart
Abstract not found

Distortion invariant object recognition in the dynamic link architecture

by Martin Lades, Jan C. Vorbrüggen, Joachim Buhmann, Christoph v. d. Malsburg, Rolf P. Würtz, Wolfgang Konen - IEEE TRANSACTIONS ON COMPUTERS , 1993
"... We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture ex-ploits correlations in the fine-scale temporal structure of cellular signals in order to group neurons dynamically into hig ..."
Abstract - Cited by 637 (80 self) - Add to MetaCart
We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture ex-ploits correlations in the fine-scale temporal structure of cellular signals in order to group neurons dynamically

The quadtree and related hierarchical data structures

by Hanan Samet - ACM Computing Surveys , 1984
"... A tutorial survey is presented of the quadtree and related hierarchical data structures. They are based on the principle of recursive decomposition. The emphasis is on the representation of data used in applications in image processing, computer graphics, geographic information systems, and robotics ..."
Abstract - Cited by 541 (12 self) - Add to MetaCart
A tutorial survey is presented of the quadtree and related hierarchical data structures. They are based on the principle of recursive decomposition. The emphasis is on the representation of data used in applications in image processing, computer graphics, geographic information systems

Bigtable: A distributed storage system for structured data

by Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber - IN PROCEEDINGS OF THE 7TH CONFERENCE ON USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION - VOLUME 7 , 2006
"... Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications ..."
Abstract - Cited by 1028 (4 self) - Add to MetaCart
Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications
Results 1 - 10 of 172,980