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114
A First Step towards Automated Detection of Buffer Overrun Vulnerabilities
 IN NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM
, 2000
"... We describe a new technique for finding potential buffer overrun vulnerabilities in securitycritical C code. The key to success is to use static analysis: we formulate detection of buffer overruns as an integer range analysis problem. One major advantage of static analysis is that security bugs can ..."
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Cited by 396 (9 self)
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We describe a new technique for finding potential buffer overrun vulnerabilities in securitycritical C code. The key to success is to use static analysis: we formulate detection of buffer overruns as an integer range analysis problem. One major advantage of static analysis is that security bugs can be eliminated before code is deployed. We have implemented our design and used our prototype to find new remotelyexploitable vulnerabilities in a large, widely deployed software package. An earlier hand audit missed these bugs.
Consistency techniques for numeric csps
, 1993
"... Many problems can be expressed in terms of a numeric constraint satisfaction problem over finite or continuous domains (numeric CSP). The purpose of this paper is to show that the consistency techniques that have been developed for CSPs can be adapted to numeric CSPs. Since the numeric domains are o ..."
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Cited by 244 (9 self)
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Many problems can be expressed in terms of a numeric constraint satisfaction problem over finite or continuous domains (numeric CSP). The purpose of this paper is to show that the consistency techniques that have been developed for CSPs can be adapted to numeric CSPs. Since the numeric domains are ordered the underlying idea is to handle domains only by their bounds. The semantics that have been elaborated, plus the complexity analysis and good experimental results, confirm that these techniques can be used in real applications. 1
CLP(Intervals) Revisited
, 1994
"... The design and implementation of constraint logic programming (CLP) languages over intervals is revisited. Instead of decomposing complex constraints in terms of simple primitive constraints as in CLP(BNR), complex constraints are manipulated as a whole, enabling more sophisticated narrowing procedu ..."
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Cited by 137 (19 self)
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The design and implementation of constraint logic programming (CLP) languages over intervals is revisited. Instead of decomposing complex constraints in terms of simple primitive constraints as in CLP(BNR), complex constraints are manipulated as a whole, enabling more sophisticated narrowing procedures to be applied in the solver. This idea is embodied in a new CLP language Newton whose operational semantics is based on the notion of boxconsistency, an approximation of arcconsistency, and whose implementation uses Newton interval method. Experimental results indicate that Newton outperforms existing languages by an order of magnitude and is competitive with some stateoftheart tools on some standard benchmarks. Limitations of our current implementation and directions for further work are also identified.
Constraint Arithmetic on Real Intervals
, 1993
"... Constraint interval arithmetic is a sublanguage of BNR Prolog which offers a new approach to the old problem of deriving numerical consequences from algebraic models. Since it is simultaneously a numerical computation technique and a proof technique, it bypasses the traditional dichotomy between (nu ..."
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Cited by 72 (3 self)
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Constraint interval arithmetic is a sublanguage of BNR Prolog which offers a new approach to the old problem of deriving numerical consequences from algebraic models. Since it is simultaneously a numerical computation technique and a proof technique, it bypasses the traditional dichotomy between (numeric) calculation and (symbolic) proofs. This interplay between proof and calculation can be used effectively to handle practical problems which neither can handle alone. The underlying semantic model is based on the properties of monotone contraction operators on a lattice, an algebraic setting in which fixed point semantics take an especially elegant form.
Extending Prolog with Constraint Arithmetic on Real Intervals
, 1990
"... Prolog can be extended by a system of constraints on closed intervals to perform declarative relational arithmetic. Imposing constraints on an interval can narrow its range and propagate the narrowing to other intervals related to it by constraint equations or inequalities. Relational interval ar ..."
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Cited by 56 (7 self)
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Prolog can be extended by a system of constraints on closed intervals to perform declarative relational arithmetic. Imposing constraints on an interval can narrow its range and propagate the narrowing to other intervals related to it by constraint equations or inequalities. Relational interval arithmetic can be used to contain floating point errors and, when combined with Prolog backtracking, to obtain numeric solutions to linear and nonlinear rational constraint satisfaction problems over the reals (e.g. ndegree polynomial equations). This technique differs from other constraint logic programming (CLP) systems like CLP(R) or PrologIII in that it does not do any symbolic processing.
Qualitative and Quantitative Simulation: Bridging the Gap
 Artificial Intelligence
, 1997
"... Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to semiquantitative simulation is to use numeric intervals to represe ..."
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Cited by 52 (1 self)
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Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to semiquantitative simulation is to use numeric intervals to represent incomplete quantitative information. In this research we demonstrate semiquantitative simulation using intervals in an implemented semiquantitative simulator called Q3. Q3 progressively refines a qualitative simulation, providing increasingly specific quantitative predictions which can converge to a numerical simulation in the limit while retaining important correctness guarantees from qualitative and interval simulation techniques. Q3's simulations are based on a technique we call step size refinement. While a pure qualitative simulation has a very coarse step size, representing the state of a system trajectory at relatively few qualitatively distinct states, Q3 interpolates newly expl...
Interval Computations: Introduction, Uses, and Resources
 Euromath Bulletin
, 1996
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Interval constraint logic programming
 CONSTRAINT PROGRAMMING: BASICS AND TRENDS, VOLUME 910 OF LNCS
, 1995
"... Abstract. In this paper, we present anoverview on the use of interval arithmetic to process numerical constraints in Constraint Logic Programming. The main principle is to approximate nary relations over IR with Cartesian products of intervals whose bounds are taken in a nite subset of I R.Variabl ..."
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Cited by 48 (5 self)
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Abstract. In this paper, we present anoverview on the use of interval arithmetic to process numerical constraints in Constraint Logic Programming. The main principle is to approximate nary relations over IR with Cartesian products of intervals whose bounds are taken in a nite subset of I R.Variables represent real values whose domains are intervals de ned in the same manner. Narrowing operators are de ned from approximations. These operators compute, from an interval and a relation, aset included in the initial interval. Sets of constraints are then processed thanks to a local consistency algorithm pruning at each stepvalues from initial intervals. This algorithm is shown to be correct and to terminate, on the basis of a certain number of properties of narrowing operators. We focus here on the description of the general framework based on approximations, on its application to interval constraint solving over continuous and discrete quantities, we establish a strong link between approximations and local consistency notions and show that arcconsistency is an instance of the approximation framework. We nally describe recentwork on di erent variants of the initial algorithm proposed by John Cleary and developed by W. Older and A. Vellino which havebeen proposed in this context. These variants address four particular points: generalization of the constraint language, improvement of domain reductions, e ciency of the computation and nally, cooperation with other solvers. Some open questions are also identi ed. 1
Automatically Verified Reasoning with Both Intervals and Probability Density
 Interval Computations
, 1993
"... Information about a value is frequently best expressed with an interval. Frequently also, information is best expressed with a probability density function. We extend automatically verified numerical inference to include combining operands when both are intervals, both are probability density funct ..."
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Cited by 41 (14 self)
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Information about a value is frequently best expressed with an interval. Frequently also, information is best expressed with a probability density function. We extend automatically verified numerical inference to include combining operands when both are intervals, both are probability density functions, or one is an interval and the other a probability density function. This technique, termed the automatically verified histogram method, uses interval techniques and forms a sharp contrast with traditional Monte Carlo methods, in which operands are all intervals or all density functions, and which are not automatically verifying. Автоматически проверяемые рассуждения с использованием интервалов и функций плотности вероятности Д. Берлеант Информация о значении величины часто лучше всего может быть выражена с помощью интервала, а также и с помощью функции плотности вероятности. Мы обобщаем автоматически проверяемый численный вывод таким образом, чтобы включить случай комбинированных операндов, то есть случай, когда оба операнда являются интервалами, или оба функциями плотности вероятности, или когда один является интервалом, а другой — функцией плотности вероятности. Этот метод, называемый методом гистограмм с автоматической проверкой, использует интервальную технику и резко отличается от традиционного метода МонтеКарло, в котором все операнды являются либо интервалами, либо функциями плотности вероятности, и в котором отсутствует автоматическая верификация.
Arc Consistency for Continuous Variables
 Artificial Intelligence
, 1998
"... Davis [1] has investigated the properties of the Waltz propagation algorithm with interval labels in continuous domains. He shows that in most cases, the algorithm does not achieve arc consistency, and furthermore is subject to infinite iterations. ..."
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Cited by 40 (6 self)
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Davis [1] has investigated the properties of the Waltz propagation algorithm with interval labels in continuous domains. He shows that in most cases, the algorithm does not achieve arc consistency, and furthermore is subject to infinite iterations.