Addressing Security Issues Related to Modern Software

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.

Prof. Dr. Luis Javier García Villalba
Dr. Ana Lucila Sandoval Orozco
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Computers computers	4.2	7.5	2012	16.3 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Entropy entropy	2.0	5.2	1999	21.8 Days	CHF 2600	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit
Mathematics mathematics	2.2	4.6	2013	18.4 Days	CHF 2600	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
AI ai	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Sci sci	-	5.2	2019	36.6 Days	CHF 1200	Submit
Future Internet futureinternet	3.6	8.3	2009	17 Days	CHF 1600	Submit

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Published Papers (1 paper)

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All Computers Electronics Entropy Information Mathematics Sensors AI Applied Sciences Sci Future Internet

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20 pages, 3148 KiB  

Open AccessArticle

Dynamic Ultrasonic Jamming via Time–Frequency Mosaic for Anti-Eavesdropping Systems

by Zichuan Yu, Lu Tang, Kai Wang, Xusheng Tang and Hongyu Ge

Electronics 2025, 14(15), 2960; https://doi.org/10.3390/electronics14152960 - 24 Jul 2025

Viewed by 225

Abstract

To combat microphone eavesdropping on devices like smartphones, ultrasonic-based methods offer promise due to human inaudibility and microphone nonlinearity. However, existing systems suffer from low jamming efficiency, poor energy utilization, and weak robustness. Based on these problems, this paper proposes a novel ultrasonic-based [...] Read more.

To combat microphone eavesdropping on devices like smartphones, ultrasonic-based methods offer promise due to human inaudibility and microphone nonlinearity. However, existing systems suffer from low jamming efficiency, poor energy utilization, and weak robustness. Based on these problems, this paper proposes a novel ultrasonic-based jamming algorithm called the Time–Frequency Mosaic (TFM) technique, which can be used for anti-eavesdropping. The proposed TFM technique can generate short-time, frequency-coded jamming signals according to the voice frequency characteristics of different speakers, thereby achieving targeted and efficient jamming. A jamming prototype using the Time–Frequency Mosaic technique was developed and tested in various scenarios. The test results show that when the signal-to-noise ratio (SNR) is lower than 0 dB, the text Word Error Rate (WER) of the proposed method is basically over 60%; when the SNR is 0 dB, the WER of the algorithm in this paper is on average more than 20% higher than that of current jamming algorithms. In addition, when the jamming system maintains the same distance from the recording device, the algorithm in this paper has higher energy utilization efficiency compared with existing algorithms. Experiments prove that in most cases, the proposed algorithm has a better jamming effect, higher energy utilization efficiency, and stronger robustness. Full article

(This article belongs to the Topic Addressing Security Issues Related to Modern Software)

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