Topic Editors

Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
Group of Analysis, Security and Systems (GASS), Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain

Addressing Security Issues Related to Modern Software

Abstract submission deadline
31 August 2026
Manuscript submission deadline
31 October 2026
Viewed by
11

Topic Information

Dear Colleagues,

Unfortunately, the recently introduced area of DevSecOps—in medium to large companies—currently lacks automated security tools. While most existing solutions predominantly target only one narrow step of the software development life cycle (SDLC) process, a much-needed holistic overview of the global security solution is missing. Despite this, in terms of security, DevSecOps is considered the best application development. By integrating security early on in the development process, it is possible to ensure it is a continuous service delivery as opposed to being developed in siloes. In this context, this Topic is requesting the submission of papers that incorporate automated security tools throughout the software development life cycle. Specifically, these works must perform targeted security checks and collect valuable information and intelligence from each step and apply advanced machine learning and artificial intelligence methods to convert this intelligence into actionable insights and recommendations, following an open-source approach for the core functionality, which will be supported by a realistic and viable business model. We invite authors to submit original contributions in all areas of artificial intelligence, cybersecurity, and software security. Topics of interest include but are not limited to the following:

  • Adversarial machine learning techniques applied to DevSecOps;
  • Al techniques for vulnerability prediction;
  • Artificial intelligence for automatic error correction;
  • Artificial intelligence techniques for algorithmic verification;
  • Automatic abstraction techniques applicable to programming code;
  • Automatic modelling of software and hardware attacks and defences using artificial intelligence algorithms;
  • Automatic prediction of security flaws in software and hardware using deep learning algorithms;
  • Deep learning techniques for modelling threats and vulnerabilities in software;
  • Deep learning techniques for symbolic model checking;
  • Deep learning techniques for the detection of programming errors in binary and modern programming languages;

Keywords

  • DevSecOps automation
  • holistic security
  • AI/ML security
  • SDLC security integration
  • vulnerability prediction
  • deep learning threats
  • open-source security
  • continuous security validation

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Computers
computers
2.6 7.5 2012 15.5 Days CHF 1800 Submit
Electronics
electronics
2.6 6.1 2012 16.4 Days CHF 2400 Submit
Entropy
entropy
2.1 5.2 1999 22.3 Days CHF 2600 Submit
Information
information
2.4 6.5 2010 16.4 Days CHF 1600 Submit
Mathematics
mathematics
2.3 4.6 2013 18.3 Days CHF 2600 Submit
Sensors
sensors
3.4 8.2 2001 18.6 Days CHF 2600 Submit
AI
ai
3.1 6.9 2020 18.9 Days CHF 1600 Submit
Applied Sciences
applsci
2.5 5.5 2011 18.4 Days CHF 2400 Submit
Sci
sci
- 5.2 2019 37.1 Days CHF 1200 Submit
Future Internet
futureinternet
2.8 8.3 2009 16.9 Days CHF 1600 Submit

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