#5875. Detecting the impact of software vulnerability on attacks: A case study of network telescope scans

July 2026publication date
Proposal available till 17-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Science Applications;
Computer Networks and Communications;
Hardware and Architecture;
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Abstract:
Network scanning is one of the first steps in gathering information about a target before launching attacks. It is used to scan for vulnerable devices and exposed services in order to exploit them. Such exploits can result in data breaches or network disruption, which can be very costly for organizations. There are many factors, including technical and non-technical, affecting the volume of scanning activities. In this paper, we study the impact of vulnerability disclosure on the volume of scans over time and propose a machine learning-based approach to predict this impact. We conducted a comprehensive data collection of network scans from two network telescopes hosted in different countries, as well as the disclosed vulnerabilities from 20XX to 20XX. We then designed a set of features to characterize the disclosed vulnerabilities and used several classifiers to predict whether a vulnerability will impact the volume of daily scans.
Keywords:
Classification algorithm; CVE; Machine learning; Network scans; NVD; Time series

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