From e91748d46316a539f509c9418ecc49a2d35910a2 Mon Sep 17 00:00:00 2001 From: Nadia DAOUDI Date: Tue, 5 Dec 2023 10:27:41 +0100 Subject: [PATCH] move seminar:5 to past --- index.html | 63 +++++++++++++++++++++++++++++++----------------------- 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/index.html b/index.html index 63de55a..b476842 100644 --- a/index.html +++ b/index.html @@ -173,31 +173,7 @@

Upcoming Seminars

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Benjamin Steenhoek
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Iowa State University

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Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection

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Deep learning-based vulnerability detection has shown great performance and, in some studies, outperformed static analysis tools. However, the highest-performing - approaches use token-based transformer models, which are not the most efficient to capture code semantics required for vulnerability detection. In this paper, we propose - to combine such causal-based vulnerability detection algorithms with deep learning, aiming to achieve more efficient and effective vulnerability detection. Specifically, - we designed DeepDFA, a dataflow analysis-inspired graph learning framework and an embedding technique that enables graph learning to simulate dataflow computation. We show - that DeepDFA is both performant and efficient. DeepDFA outperformed all non-transformer baselines. It was trained in 9 minutes, 75x faster than the highest-performing baseline - model. When using only 50+ vulnerable and several hundreds of total examples as training data, the model retained the same performance as 100% of the dataset. DeepDFA also - generalized to real-world vulnerabilities in DbgBench; it detected 8.7 out of 17 vulnerabilities on average across folds and was able to distinguish between patched and buggy versions. - By combining DeepDFA with a large language model, we surpassed the state-of-the-art vulnerability detection performance on the Big-Vul dataset - with 96.46 F1 score, 97.82 precision, and 95.14 recall.

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Presentation Date: Monday, December 4, 2023 at 3:00 PM CET

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Robust Learning of Deep Predictive Models from Noisy and Imbalanced Software and Software Defect Prediction. Evaluation results show that RobustTrainer effectively tackles the mislabelling and class imbalance issues and produces significantly better deep predictive models compared to the other six comparison approaches.

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Presentation Date: Monday, December 18, 2023 at 10:30 PM CET

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Presentation Date: Monday, December 18, 2023 at 10:30 AM CET

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Past Seminars

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    + Dataflow Analysis-Inspired DL for Efficient Vulnerability Detection, Monday, December 4, 2023, by Benjamin Steenhoek from ISU + +
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    Benjamin Steenhoek
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    Iowa State University

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    Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection

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    Deep learning-based vulnerability detection has shown great performance and, in some studies, outperformed static analysis tools. However, the highest-performing + approaches use token-based transformer models, which are not the most efficient to capture code semantics required for vulnerability detection. In this paper, we propose + to combine such causal-based vulnerability detection algorithms with deep learning, aiming to achieve more efficient and effective vulnerability detection. Specifically, + we designed DeepDFA, a dataflow analysis-inspired graph learning framework and an embedding technique that enables graph learning to simulate dataflow computation. We show + that DeepDFA is both performant and efficient. DeepDFA outperformed all non-transformer baselines. It was trained in 9 minutes, 75x faster than the highest-performing baseline + model. When using only 50+ vulnerable and several hundreds of total examples as training data, the model retained the same performance as 100% of the dataset. DeepDFA also + generalized to real-world vulnerabilities in DbgBench; it detected 8.7 out of 17 vulnerabilities on average across folds and was able to distinguish between patched and buggy versions. + By combining DeepDFA with a large language model, we surpassed the state-of-the-art vulnerability detection performance on the Big-Vul dataset + with 96.46 F1 score, 97.82 precision, and 95.14 recall.

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    Presentation Date: Monday, December 4, 2023 at 3:00 PM CET

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    Towards Understanding Fairness and its Composition in Ensemble Machine Learn ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design.

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    Presentation Date: Monday, November 20, 2023 at 4:00 PM CET

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    Presentation Date: Monday, November 20, 2023 at 4:00 PM CET