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As control-flow protection methods get widely deployed it is difficult forattackers to corrupt control data to build attacks. Instead, data-orientedexploits, which modify non-control data for malicious goals, have beendemonstrated to be possible and powerful. To defend against data-orientedexploits, the first fundamental step is to identify non-control,security-critical data. However, previous works mainly rely on tedious humanefforts to identify critical data, which cannot handle large applications noreasily port to new programs.  In this work, we investigate the application of deep learning to criticaldata identification. This work provides non-intuitive understanding about (a)why straightforward ways of applying deep learning would fail, and (b) how deeplearning should be applied in identifying critical data. Based on our insights,we have discovered a non-intuitive method which combines Tree-LSTM models and anovel structure of data-flow tree to effectively identify critical data fromexecution traces. The evaluation results show that our method can achieve87.47% accuracy and a F1 score of 0.9123, which significantly outperforms thebaselines. To the best of our knowledge, this is the first work using a deepneural model to identify critical data in program binaries.

Identifying Non-Control Security-Critical Data in Program Binaries with a Deep Neural Model