Tim Sonnekalb

Orcid: 0000-0002-0067-1790

According to our database1, Tim Sonnekalb authored at least 14 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
User-agent as a Cyber Intrusion Artifact: Detection of APT Activity using minimal Anomalies on the User-agent String Traffic (short paper).
Proceedings of the 16th ZEUS Workshop, Ulm, Germany, February 29-March 1, 2024., 2024

Finding a Needle in a Haystack: Threat Analysis in Open-Source Projects.
Proceedings of the IEEE International Conference on Software Analysis, 2024

2023
ROMEO: A binary vulnerability detection dataset for exploring Juliet through the lens of assembly language.
Comput. Secur., May, 2023

TIPICAL - Type Inference for Python In Critical Accuracy Level.
Proceedings of the 21st IEEE/ACIS International Conference on Software Engineering Research, 2023

Cross-Domain Evaluation of a Deep Learning-Based Type Inference System.
Proceedings of the 20th IEEE/ACM International Conference on Mining Software Repositories, 2023

A Static Analysis Platform for Investigating Security Trends in Repositories.
Proceedings of the 1st IEEE/ACM International Workshop on Software Vulnerability, 2023

2022
Deep security analysis of program code.
Empir. Softw. Eng., 2022

Generalizability of Code Clone Detection on CodeBERT.
Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022

2021
ROMEO: Exploring Juliet through the Lens of Assembly Language.
CoRR, 2021

Towards Visual Analytics Dashboards for Provenance-driven Static Application Security Testing.
Proceedings of the IEEE Symposium on Visualization for Cyber Security, 2021

Provenance-Based Security Audits and Its Application to COVID-19 Contact Tracing Apps.
Proceedings of the Provenance and Annotation of Data and Processes, 2021

2020
Erste Überlegungen zur Erklärbarkeit von Deep-Learning-Modellen für die Analyse von Quellcode.
Softwaretechnik-Trends, 2020

2019
Smart Hot Water Control with Learned Human Behavior for Minimal Energy Consumption.
Proceedings of the 5th IEEE World Forum on Internet of Things, 2019

Machine-learning supported vulnerability detection in source code.
Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2019


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