31 papers with code • 0 benchmarks • 2 datasets
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Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection.
Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database.
We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions.
AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis Approach
The security of mobile applications has become a major research field which is associated with a lot of challenges.
We thus train the model to learn execution semantics from the functions' micro-traces, without any manual labeling effort.
Moreover, we subscribe to the hypothesis that code may be treated as natural language, and thus we process assembly code using standard architectures commonly employed in natural language processing.
Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts
Therefore, Eth2Vec can detect vulnerabilities in smart contracts by comparing the code similarity between target EVM bytecodes and the EVM bytecodes it already learned.
We test the hypothesis laid by Galke and Scherp , that feedforward networks suffice for many analytics tasks (which we call, the "Old but Gold" hypothesis) for these two tasks.