Vulnerability Disclosure in the Age of Social Media: Exploiting Twitter for Predicting Real-World Exploits

By Carl Sabottke, Octavian Suciu, Tudor Dumitras. 2015

331 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Junyang Qiu
2. Jun Zhang
3. Guanjun Lin
4. Yang Xiang
5. G. Husari
6. Qing-Long Han
7. Sheng Wen
8. Wei Luo

Citations in the corpus

• When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks (2018, background): “In [40], the authors present a system, based on a linear SVM, that predicts”
• A Survey of Android Malware Detection with Deep Neural Models (2020, background): “However, most of the ML-based methods require security experts to manually define the features to characterize Android malware, which heavily relies on experts’ experience, level of expertise, and the depth of domain knowledge [5, 6].”
• Software Vulnerability Detection Using Deep Neural Networks: A Survey (2020, background): “1) Definition: Software security vulnerabilities (short for vulnerabilities), also known as security defects [89], security bugs [108], and software weaknesses [54], are briefly defined as “software bugs that have security impli-cations” [87].”
• TTPDrill: Automatic and Accurate Extraction of Threat Actions from Unstructured Text of CTI Sources (2017, methodology): “[26] built a classier that utilizes a set of features (e.”