Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning

<div>Comparison between our method and BOMiner. (a) Comparison of average confusion matrix. (b) Comparison of average recall, TNR, precision, and <span class="nowrap"><svg height="11.8174pt" id="M185" style="vertical-align:-3.1815pt" version="1.1" viewbox="-0.0498162 -8.6359 11.8575 11.8174" width="11.8575pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M584 650H137L131 622C214 614 217 612 200 521L125 127C109 41 101 35 23 28L17 0H288L294 28C201 35 193 42 209 128L242 309H348C440 309 442 300 443 226H471L510 422H482C452 354 449 348 357 348H251L295 575C302 609 304 615 338 615H426C502 615 517 604 526 581C534 560 536 524 537 492L565 494C574 554 583 631 584 650Z" id="g113-71"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.786,3.132)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z" id="g50-50"></path></g></svg>.</span></div>

Mathematical Problems in Engineering

fig7

Figure 7

Figure 7: Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning 