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Found 7 papers, 2 papers with code
Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?
We recognize that the current advances in machine learning can be used to detect vulnerable code patterns on syntactically incomplete code snippets as the developer is writing the code at EditTime.
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
To achieve this, prior attacks explicitly inject the insecure code payload into the training data, making the poison data detectable by static analysis tools that can remove such malicious data from the training set.
Learning to Reduce False Positives in Analytic Bug Detectors
Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software.
Privileged Zero-Shot AutoML
This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minutes to milliseconds by using a zero-shot approach.
Real-Time AutoML
We train a graph neural network in which each node represents a dataset to predict the best machine learning pipeline for a new test dataset.
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time
Combinatorial optimization algorithms for graph problems are usually designed afresh for each new problem with careful attention by an expert to the problem structure.
Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning
We show in experiments that our method can attack a gradient-boosted machine learning model with evasion rates that are substantial and appear to be strongly dependent on the dataset.
Cryptography and Security
