Improving Network Applications Security: a New Heuristic to Generate Stress Testing Data Presented by Conrad Pack Del Grosso et al.

Improving Network Applications Security: a New Heuristic

to Generate Stress Testing Data

Presented by Conrad Pack

Del Grosso et al

Overview

• Buffer Overflow problem– Network security– Critical systems

• Testing to identify/remove vulnerabilities– Combined static and dynamic approach– Static slicing– Genetic algorithms (GAs) in dynamic search

• New heuristic

Buffer Overflow

• Incorrect handling of input

• Data overwritten

Impact of Buffer Overflow

• Scope– Language variations (C++ vs. Java)– Prevalence of unaudited code

• Over 50% of vulnerabilities (CERT)• Potential harm

– Unauthorized access in network/security applications

– Serious accidents in critical embedded systems

Overview of Approach

Static Analysis

• Tools– RatScan (front end to RATS)– Splint

• Extracted Information– Potentially vulnerable source statements– Call to potentially unsafe functions/libraries– Estimated buffer sizes

Static Slicing

• Software maintenance technique– “all program code that can in anyway affect

the value of a given variable”

• Inputs and source code relationship– Data dependency– Some inputs not tied to vulnerable statements

• Tool: CodeSurfer (GrammaTech)

• Purpose: Search space reduction

Test Case Generation Using GA

• GA aspects– Chromosome (2 dimensional array)– Crossover/mutation operators (whole/creep)– Fitness function (to follow)– Parameters

• Number of generations (500)• Population size (70)• Propagation rules (2 best)

• Probabilities (pcross = 0.7, pmut = 0.01)

Fitness

• GA is an optimization problem

• Three Approaches– Vulnerable coverage fitness– Nesting fitness– Buffer boundary fitness

• Correlation to crashes alone not enough– Flat landscape– Random search

Vulnerable Coverage Fitness

• Statement coverage

• Vulnerable statement coverage

• Number of vulnerable statement executions

• Function

F(g) = w1 • scov + w2 • log(k) • vcov + w3 • crash

Nesting Fitness

• Unconstrained nodes (graph theory)– Control flow graphs– Do not dominate any node– Do not postdominate any node

• Often correspond with maximum nesting

• Function

F(g) = w1 • scov + w2 • log(k) • vcov + w3 • nesting

Buffer Boundary Fitness

• Buffer boundaries in fitness calculation– Often difficult to precisely determine– Intended for future implementation

• Distance from boundary by size estimate– Compile time (can’t always be determined)

• Function

F(g) = w1 • scov + w2 • log(k) • vcov + w3 • nesting

+ w4 • maxi{minj(Li,j – SBi)}

Empirical Results

• Two test programs– White noise generator (scientific application)– FTP client (network application)

• Random search as a control– Pure random search– GA search with no fitness

• White noise: fixed initial population

• FTP: random initial populations

White Noise Generator Results

FTP Client Results

Personal Conclusions

• Use of Genetic Algorithms in testing is compelling

• Fitness Heuristic using source code is a valuable concept

• Useful in large projects

• Buffer overflow will likely have less importance over time

• GA assumptions