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Scalable RADAR for Co-evolutionary Adaptive Environments. Approach: Extend our existing platforms by further examining biological factors. Future: Simulate spread of both attacks and repairs simultaneously.

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scalable radar for co evolutionary adaptive environments
Scalable RADAR for Co-evolutionary Adaptive Environments

Approach: Extend our existing platformsby further examining biological factors

Future: Simulate spread of both attacks and repairs simultaneously

Question: Can our existing approach be adapted to repair specialized embedded devices?

Approach: While individual devices may lack the computing speed to efficiently find repairs, they can cooperate to explore the search space and find repairs more quickly

Biological Principles


Scalable RADAR

Subgoal: Develop models and simulations to understand Scalable RADAR principles and adapt them to computation, specifically to improve our existing techniques.

Question 2: How much does the structure of the lymphatic system speed up repair?


Question: What effect does diversity have on vulnerability?


  • Answer: Diversity decreases with increased connectivity and communication. Diversity decreases network vulnerability, even whenitincreasesindividualvulnerability.
  • Example: Despite larger individual vulnerabilities (in red),The group AB above is less vulnerable than CDE

Answer: There is a trade-off between many small nodes and few large nodes – rate of distribution of repairs vs. speed of recruitment of new repairs.

Therefore, we will study FIXME-X and FIXME-Y.

Question 1: How much do FIXME search signals speed up immune repair?

Answer: Biologically, as the size of the search space increases, the effect of signals improves performance by orders of magnitude.

Research 2: InformationDiversity through Information Flow

Research 1: Evolutionary Program Repair

  • Insight: Attacks and defects have unique information flow signatures. Conversely, bug fixes exhibit information flows that differ in a significant manner from the original program
  • Status: Prototype currently handles 60% of X86 Instruction FIXME: MORE DETAILED RESULTS/EXPLANATION HERE

Systematic Study of Cost and Generality

  • Systems contain more errors and are more prone to attack than ever.
  • The balance of power favors the attacker:
    • Software replicates are all vulnerable to the same attack.
    • System complexity precludes rapid repair.
  • We must rethink the current cybersecurity paradigm.
  • Subgoal: Systematically and precisely measure program diversity by measuring the information flow generated by unique inputs.


  • Subgoal: Apply evolutionary repair to known bugs in real-world programs totaling over 5 million lines of code and 10,000 test cases.

Immune systems are composed of millions of cells.

for(Loop = 0; Input[Loop] != ‘\0’; Loop++){

if ((Input[Loop] >= ‘a’) &&

(Input[Loop] <= ‘z’)) {

else if((Input[Loop] >= ‘a’) &&

(Input[Loop] <= ‘z’)) {

  • Approach: Enhance several fundamental steps throughout the
  • process and attempt to fix 105 indicative bugs found in existing programs.


  • Result: Improvements yielded 68% more patches. Based on Amazon EC2 cloud service rates, 55 bugs were fixed at an average cost of $7.32 per bug.

Redundancy, diversity, “wisdom of the crowd.”

  • Approach:Construct matrices (pictured above) relating input to branch decisions. Judge the diversity of programs by comparing their structure in a way that is robust to small, simple changes


  • Animal immune systems can defeat multiple, adaptable adversaries.

Research 3: Simulation and Modeling

Genes, cells, systems adapt over multiple time scales.

Mutational Robustness and Proactive Diversity

Study of the Immune System

Evaluating Diversity

Distributed Repair

Subgoal 1: Examine whether there is a computational analog for biological mutational robustness and thus quantify the ability of random changes to produce variants that retain specified program behavior.

Software is a complex, evolving system.

Mutational robustness: Independent of programming language, domain, and test suit coverage, the fraction of program variants with identical behavior on all available test cases is 36.75% in 22 programs.

Decentralized Search

Biological systems search complex spaces without a “leader.”

Subgoal 2: Use mutational robustness to proactively fix unknown

bugs while retaining functionality.


  • Adapt Scalable RADAR to a new, clean-slate paradigm for software development/maintenance.
  • Demonstrate large, complex software systems that:
    • automatically detect attacks
    • repair themselves
    • evolve a diversity of solutions.

Results: We select a population of variants based on computational analogs of biological diversity that fixes an average of 40% of unknown bugs.

Automated Response

Cells respond to environmental signals automatically.

Melanie Moses

Jed Crandall

Stephanie Forrest (PI)

Wes Weimer