Online Justification for Tabled Logic Programs

Pemmasani, Giridhar; Guo, Hai-Feng; Dong, Yunbai; Ramakrishnan, C. R.; Ramakrishnan, I. V.

doi:10.1007/978-3-540-24754-8_4

Cited by 28 publications

(21 citation statements)

References 13 publications

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“…XSB, like other Prolog systems, does not provide functionalities for generating proofs of a successful query. The justifier program for XSB [7] can record execution trace for interactively retrieving reasons for a successful Prolog query, but does not provide the capability of constructing derivation graphs that illustrate all possible derivations. We use a similar approach to XSB's justifier program to record derivation steps when evaluating the MulVAL Datalog program.…”

Section: Security Analysis Based On Logic Programmingmentioning

confidence: 99%

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A scalable approach to attack graph generation

Boyer

McQueen

2006

Proceedings of the 13th ACM Conference on Computer and Communications Security

469

304

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Attack graphs are important tools for analyzing security vulnerabilities in enterprise networks. Previous work on attack graphs has not provided an account of the scalability of the graph generating process, and there is often a lack of logical formalism in the representation of attack graphs, which results in the attack graph being difficult to use and understand by human beings. Pioneer work by Sheyner, et al. is the first attack-graph tool based on formal logical techniques, namely model-checking. However, when applied to moderate-sized networks, Sheyner's tool encountered a significant exponential explosion problem. This paper describes a new approach to represent and generate attack graphs. We propose logical attack graphs, which directly illustrate logical dependencies among attack goals and configuration information. A logical attack graph always has size polynomial to the network being analyzed. Our attack graph generation tool builds upon MulVAL, a network security analyzer based on logical programming. We demonstrate how to produce a derivation trace in the Mul-VAL logic-programming engine, and how to use the trace to generate a logical attack graph in quadratic time. We show experimental evidence that our logical attack graph generation algorithm is very efficient. We have generated logical attack graphs for fully connected networks of 1000 machines using a Pentium 4 CPU with 1GB of RAM.

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Section: Security Analysis Based On Logic Programmingmentioning

confidence: 99%

“…When the evaluation of the rule succeeds, this function will record the successful derivation into a trace file. In essence this method for recording execution traces is similar to the one used by the "justifier" program in XSB [7]. The attack simulation trace has the following format.…”

Section: Algorithmsmentioning

confidence: 99%

A scalable approach to attack graph generation

Boyer

McQueen

2006

Proceedings of the 13th ACM Conference on Computer and Communications Security

469

304

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“…Pontelli and Son [6] adopt the concept of justifications [13,14,15] to the context of ASP. Roughly, justifications formalize reasons why an atom is in an answer set.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Debugging ASP Programs by Means of ASP

Brain

Gebser

Pührer

et al. 2007

Logic Programming and Nonmonotonic Reasoning

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Abstract. Answer-set programming (ASP) has become an important paradigm for declarative problem solving in recent years. However, to further improve the usability of answer-set programs, the development of software-engineering tools is vital. In particular, the area of debugging provides a challenge in both theoretical and practical terms. This is due to the purely declarative nature of ASP that, on the one hand, calls for solver-independent methodologies and, on the other hand, does not directly apply to tracing techniques. In this paper, we propose a novel methodology, which rests within ASP itself, to sort out errors on the conceptual level. Our method makes use of tagging, where the program to be analyzed is rewritten using dedicated control atoms. This provides a flexible way to specify different types of debugging requests and a first step towards a dedicated (meta level) debugging language.

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“…e.g. [7]) has been used for debugging tabled programs that cannot be debugged by traditional means. Here, we consider justification in the context of the Silk system, currently under development at Vulcan, Inc.…”

Section: Scalability For Multi-valued and Quantitative Logicsmentioning

confidence: 99%

Tabling with Answer Subsumption: Implementation, Applications and Performance

Swift

Warren

2010

Logics in Artificial Intelligence

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Abstract. Tabled Logic Programming (TLP) is becoming widely available in Prolog systems, but most implementations of TLP implement only answer variance in which an answer A is added to the table for a subgoal S only if A is not a variant of any other answer already in the table for S. While TLP with answer variance is powerful enough to implement the well-founded semantics with good termination and complexity properties, TLP becomes much more powerful if a mechanism called answer subsumption is used. XSB implements two forms of answer subsumption. The first, partial order answer subsumption, adds A to a table only if A is greater than all other answers already in the table according to a user-defined partial order. The second, lattice answer subsumption, may join A to some other answer in the table according to a user-defined upper semi-lattice. Answer subsumption can be used to implement paraconsistent and quantitative logics, abstract analysis domains, and preference logics. This paper discusses the semantics and implementation of answer subsumption in XSB, and discusses performance and scalability of answer subsumption on a variety of problems.

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Online Justification for Tabled Logic Programs

Cited by 28 publications

References 13 publications

A scalable approach to attack graph generation

A scalable approach to attack graph generation

Debugging ASP Programs by Means of ASP

Tabling with Answer Subsumption: Implementation, Applications and Performance

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