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
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Computers computers	5.2	9.1	2012	15.4 Days	CHF 1800	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Entropy entropy	2.1	4.9	1999	20.9 Days	CHF 2600	Submit
Future Internet futureinternet	4.6	10.0	2009	15 Days	CHF 1800	Submit
Information information	4.3	8.2	2010	18.7 Days	CHF 1800	Submit
Mathematics mathematics	2.3	5.4	2013	17.4 Days	CHF 2600	Submit
Sci sci	4.1	5.4	2019	28.2 Days	CHF 1400	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (9 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

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

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

17 pages, 1712 KB  

Open AccessArticle

JDQuery: Query-Driven Defect Localization for Java Source Code Based on Code Knowledge Graphs

by Tianyuan Hu and Tong Wang

Electronics 2026, 15(17), 3827; https://doi.org/10.3390/electronics15173827 - 26 Aug 2026

Viewed by 216

Abstract

Java is one of the most widely used object-oriented programming languages, making accurate and efficient defect localization essential for improving software quality and reliability. Conventional static analysis techniques primarily rely on predefined rules and localized syntactic matching, which may limit their ability to [...] Read more.

Java is one of the most widely used object-oriented programming languages, making accurate and efficient defect localization essential for improving software quality and reliability. Conventional static analysis techniques primarily rely on predefined rules and localized syntactic matching, which may limit their ability to capture complex structural and semantic relationships among program entities. To address these limitations, this paper proposes JDQuery, a query-driven defect localization framework for Java source code based on a code knowledge graph. The framework parses Java source code into abstract syntax trees (ASTs), extracts software entities and their semantic relationships according to a formalized domain ontology, and constructs a unified code knowledge graph that integrates syntactic and semantic information. Based on the structural characteristics of Java defects, defect patterns are translated into Cypher queries, enabling flexible defect localization through graph pattern matching. Experiments on multiple open-source Java projects, including both injected defects and native real-world defects, demonstrate that JDQuery achieves precision values of 97.20% and 92.87% on two projects of different code sizes. A comparative evaluation with PMD further shows that JDQuery achieves substantially higher recall while maintaining comparable precision for the evaluated defects. Efficiency experiments demonstrate that JDQuery maintains millisecond-level query latency even when processing large-scale Java projects. Full article

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

►▼ Show Figures

Figure 1

26 pages, 3518 KB  

Open AccessArticle

Cost-Aware Android Malware Detection Using an Early-Warning Behaviour Score

by Ali Fenjan, Mohammed Almulla and Jalil Md. Desa

Computers 2026, 15(8), 504; https://doi.org/10.3390/computers15080504 - 5 Aug 2026

Viewed by 319

Abstract

Android malware detection systems commonly emphasize predictive accuracy while paying less attention to feature-acquisition cost, deployment efficiency, and early decision making. This paper presents a staged model-input budget framework for cost-aware Android malware screening and evaluates classification performance under progressively expanded static feature [...] Read more.

Android malware detection systems commonly emphasize predictive accuracy while paying less attention to feature-acquisition cost, deployment efficiency, and early decision making. This paper presents a staged model-input budget framework for cost-aware Android malware screening and evaluates classification performance under progressively expanded static feature representations. The proposed framework uses a lightweight Behaviour Score as an early-warning model input derived from multiple static behavioural indicators, including permission risk, encryption evidence, network activity, and suspicious keyword evidence. Rather than treating the score as a cost-free feature, the framework distinguishes between the derived model input and the underlying static indicators required to construct it. Uncertain samples are progressively escalated from the early-warning stage to richer feature budgets using a confidence-based decision rule, while confident samples can be resolved before full-feature analysis. The experimental evaluation reports hyperparameter-tuned model performance, empirical inference-time profiling, confidence-based escalation behaviour, Matthews correlation coefficient, false positive rate analysis, low false-positive-rate operating points, cross-validation, statistical testing, and external proxy-budget validation using the Drebin benchmark. On the main Android application dataset, the Behaviour-Score stage achieved an F1-score of 0.8750. When low-cost static indicators were added, the framework achieved an F1-score of 0.9654 and a Matthews correlation coefficient of 0.9267. The full feature set achieved the highest F1-score of 0.9878 and Matthews correlation coefficient of 0.9741. The confidence-based escalation experiment showed that, at a predefined 0.95 confidence operating point, 90.32% of samples were resolved before full-feature analysis, reducing the average number of classifier model inputs used from 9 to 3.50 while maintaining an F1-score of 0.9785. These findings indicate that the proposed framework provides an incremental model-input budget approach for deployment-oriented Android malware screening, while preserving full analysis for uncertain samples. Full article

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

►▼ Show Figures

Graphical abstract

22 pages, 13845 KB  

Open AccessArticle

NAPO-SCVD: Noise-Aware Preference Reinforcement Large Language Model for Smart Contract Vulnerability Detection

by Dianjun Xie, Wenai Song, Biaokai Zhu, Ruize Guo and Yiran Li

Computers 2026, 15(7), 413; https://doi.org/10.3390/computers15070413 - 27 Jun 2026

Viewed by 446

Abstract

As the core automated execution components of blockchain technology, smart contracts enable programmatic control over digital assets; however, their immutable characteristics and inherent logical vulnerabilities give rise to substantial security risks. Although smart contract vulnerability detection methods based on large language models (LLMs) [...] Read more.

As the core automated execution components of blockchain technology, smart contracts enable programmatic control over digital assets; however, their immutable characteristics and inherent logical vulnerabilities give rise to substantial security risks. Although smart contract vulnerability detection methods based on large language models (LLMs) have exhibited certain potential in vulnerability detection and explanation, the coarse-grained modeling of traditional binary preference optimization paradigms hinders the model ability to learn the priority of domain-specific requirements, frequently leading to extreme optimization at the cost of detection accuracy. Furthermore, existing approaches fail to consider non-ideal factors in real-world application scenarios and overlook noise interference induced by missing prompts, which results in inadequate detection stability and reliability, making them challenging to adapt to complex practical scenarios. To address these critical issues, this study proposes a Noise-Aware Preference Reinforcement Large Language Model for Smart Contract Vulnerability Detection (NAPO-SCVD). This method adopts a four-stage framework consisting of data construction, continuous pre-training, supervised fine-tuning, and noise-aware preference optimization. Specifically, it enhances the model’s comprehension of contract syntax and semantics through domain-specific pre-training, improves its detection and explanation capabilities using high-quality datasets, constructs deliberately guided biased explanations to simulate noisy samples, refines preference gradients, and strengthens the model’s anti-interference ability. Consequently, this approach achieves high-precision and high-reliability smart contract vulnerability detection, along with fine-grained explanations. Full article

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

►▼ Show Figures

Figure 1

18 pages, 627 KB  

Open AccessArticle

Design of a Multi-Tier Security Model Encompassing Human Factors, Identification Processes, and Secure Networking

by Zhuldyz Tashenova, Askhatov Alim, Gabdullin Abzal, Abdikhaimov Yelnur, Raiskanov Rassul, Oryntay Al-Tarazi, Zhanat Abdugulova and Shirin Amanzholova

Information 2026, 17(6), 537; https://doi.org/10.3390/info17060537 - 1 Jun 2026

Viewed by 721

Abstract

Modern cybersecurity challenges span multiple layers, from human behavior and identity management to network communication and device security. This paper proposes a unified multi-layered security framework that integrates human-centric, identity-centric, and communication-centric defenses into a coherent architecture. Drawing on insights from diverse domains [...] Read more.

Modern cybersecurity challenges span multiple layers, from human behavior and identity management to network communication and device security. This paper proposes a unified multi-layered security framework that integrates human-centric, identity-centric, and communication-centric defenses into a coherent architecture. Drawing on insights from diverse domains (industrial control systems, IoT, healthcare, blockchain, and quantum communications), we identify common defense-in-depth principles and interdependencies across layers. The study highlights the persistent gaps in current research, which often focuses on isolated layers or domain-specific models, and addresses these gaps by synthesizing a cross-domain framework. We develop a mixed-method methodology to compare and integrate multi-layer security mechanisms, and we implement a proof-of-concept risk assessment engine to evaluate the framework’s effectiveness. Preliminary results from this implementation demonstrate that combining layers yields significantly improved detection performance and resilience compared to single-layer baselines. The framework’s contributions include a comprehensive literature-driven model, an operational validation in a simulated environment, and guidelines for deploying multi-layer defenses in complex, interconnected infrastructures. Empirical findings confirm that an integrated multi-layer approach can adapt to varied threat scenarios and reduce vulnerabilities, underscoring the value of coordinated controls across technical and human factors. The proposed framework lays a foundation for future work on scalable, cross-layer cybersecurity architectures that better protect contemporary cyber–physical systems. Full article

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

►▼ Show Figures

Figure 1

20 pages, 2376 KB  

Open AccessArticle

ESP32-Based Hardware Key for Software Application Protection

by Alexandru-Ion Popovici and Florin-Daniel Anton

Appl. Sci. 2026, 16(9), 4251; https://doi.org/10.3390/app16094251 - 27 Apr 2026

Cited by 1 | Viewed by 1390

Abstract

In the current context, classic software licensing and protection mechanisms based exclusively on host application checks can be circumvented by patching, emulation and replay attacks in user-controlled environments. This paper presents an adaptive hardware key implemented on the ESP32-S3 platform, which externalizes sensitive [...] Read more.

In the current context, classic software licensing and protection mechanisms based exclusively on host application checks can be circumvented by patching, emulation and replay attacks in user-controlled environments. This paper presents an adaptive hardware key implemented on the ESP32-S3 platform, which externalizes sensitive decisions and cryptographic operations from the host application to a dedicated device. The solution combines a device-anchored root of trust (secure boot and flash memory encryption), a PKI-verifiable identity (Public Key Infrastructure X.509 certificate and digital signatures as proof of ownership), hierarchical key derivation to avoid static secrets and the establishment of an authenticated encrypted session for all essential data exchanges. User access is conditioned by three-factor authentication (PIN—Personal Identification Number, TOTP—Time based One Time Password and USB physical presence) and a “code-in-dongle” mechanism, in which the important logic runs on the device and the application receives tokens with limited duration. Experimental validation demonstrates correct provisioning, secure session establishment, negative brute-force testing, as well as lifecycle support via signed OTA (Over-The-Air) with anti-rollback and encrypted backup/recovery. Build reports indicate a balanced flash distribution and available DIRAM (Data/Instruction RAM) margin, while IRAM (Instruction RAM) saturation (99.99%) reflects a normal architectural behavior of the ESP32-S3 unified memory model rather than a capacity constraint. Full article

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

►▼ Show Figures

Figure 1

27 pages, 1202 KB  

Open AccessReview

A Classification Framework and Research Progress on Adaptation Methods for Concept Drift in Malicious Code Detection Models

by Qi Wang, Longjuan Wang and Weiwei Zhao

Future Internet 2026, 18(5), 231; https://doi.org/10.3390/fi18050231 - 24 Apr 2026

Viewed by 612

Abstract

With the development of artificial intelligence technologies, various models have become mainstream methods in malicious code detection. The application of these models brings significant advantages in automation, intelligence, and proactivity. However, as malicious code continuously evolves and updates, discrepancies emerge between the distribution [...] Read more.

With the development of artificial intelligence technologies, various models have become mainstream methods in malicious code detection. The application of these models brings significant advantages in automation, intelligence, and proactivity. However, as malicious code continuously evolves and updates, discrepancies emerge between the distribution of malicious code characteristics and those in the model’s training dataset. This leads to a decline in the model’s detection performance, a phenomenon known as concept drift. Existing research still lacks a systematic review that comprehensively explains how concept drift impacts malicious software detection models and how to effectively address this issue. Therefore, this paper reviews and analyzes the current research on this topic in five aspects: enhanced machine learning methods, deep neural network models, graph neural network models, continual learning strategies, and meta-learning strategies. By analyzing, comparing, summarizing, and discussing the various methods, this paper aims to provide insights into future improvements for reducing concept drift in malicious code detection models. This paper helps researchers understand the basic principles behind concept drift, current mitigation techniques, existing challenges, and future development directions, providing support for further research and improvement of existing methods. Full article

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

►▼ Show Figures

Figure 1

26 pages, 1005 KB  

Open AccessArticle

A Context-Aware Lightweight Framework for Source Code Vulnerability Detection

by Yousef Sanjalawe, Budoor Allehyani and Salam Al-E’mari

Future Internet 2025, 17(12), 557; https://doi.org/10.3390/fi17120557 - 3 Dec 2025

Viewed by 1518

Abstract

As software systems grow increasingly complex and interconnected, detecting vulnerabilities in source code has become a critical and challenging task. Traditional static analysis methods often fall short in capturing deep, context-dependent vulnerabilities and adapting to rapidly evolving threat landscapes. Recent efforts have explored [...] Read more.

As software systems grow increasingly complex and interconnected, detecting vulnerabilities in source code has become a critical and challenging task. Traditional static analysis methods often fall short in capturing deep, context-dependent vulnerabilities and adapting to rapidly evolving threat landscapes. Recent efforts have explored knowledge graphs and transformer-based models to enhance semantic understanding; however, these solutions frequently rely on static knowledge bases, exhibit high computational overhead, and lack adaptability to emerging threats. To address these limitations, we propose DynaKG-NER++, a novel and lightweight framework for context-aware vulnerability detection in source code. Our approach integrates lexical, syntactic, and semantic features using a transformer-based token encoder, dynamic knowledge graph embeddings, and a Graph Attention Network (GAT). We further introduce contrastive learning on vulnerability–patch pairs to improve discriminative capacity and design an attention-based fusion module to combine token and entity representations adaptively. A key innovation of our method is the dynamic construction and continual update of the knowledge graph, allowing the model to incorporate newly published CVEs and evolving relationships without retraining. We evaluate DynaKG-NER++ on five benchmark datasets, demonstrating superior performance across span-level F1 (89.3%), token-level accuracy (93.2%), and AUC-ROC (0.936), while achieving the lowest false positive rate (5.1%) among state-of-the-art baselines. Sta tistical significance tests confirm that these improvements are robust and meaningful. Overall, DynaKG-NER++ establishes a new standard in vulnerability detection, balancing accuracy, adaptability, and efficiency, making it highly suitable for deployment in real-world static analysis pipelines and resource-constrained environments. Full article

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

►▼ Show Figures

Figure 1

21 pages, 5562 KB  

Open AccessArticle

LSNet: Adaptive Latent Space Networks for Vulnerability Severity Assessment

by Yizhou Wang, Jin Zhang and Mingfeng Huang

Information 2025, 16(9), 779; https://doi.org/10.3390/info16090779 - 8 Sep 2025

Cited by 1 | Viewed by 1275

Abstract

Due to the increasing harmfulness of software vulnerabilities, it is increasingly suggested to propose more efficient vulnerability assessment methods. However, existing methods mainly rely on manual updates and inefficient rule matching, and they struggle to capture potential correlations between vulnerabilities, thus resulting in [...] Read more.

Due to the increasing harmfulness of software vulnerabilities, it is increasingly suggested to propose more efficient vulnerability assessment methods. However, existing methods mainly rely on manual updates and inefficient rule matching, and they struggle to capture potential correlations between vulnerabilities, thus resulting in issues such as strong subjectivity and low efficiency. To this end, a vulnerability severity assessment method named Latent Space Networks (LSNet) is proposed in this paper. Specifically, based on a clustering analysis in Common Vulnerability Scoring System (CVSS) metrics, we first exploit relations for CVSS metrics prediction and propose an adaptive transformer to extract vulnerability from both global semantic and local latent space features. Then, we utilize bidirectional encoding and token masking techniques to enhance the model’s understanding of vulnerability–location relationships, and combine the Transformer method with convolution to significantly improve the model’s ability to identify vulnerable text. Finally, extensive experiments conducted on the open vulnerability dataset and the CCF OSC2024 dataset demonstrate that LSNet is capable of extracting potential correlation features. Compared with baseline methods, including SVM, Transformer, TextCNN, BERT, DeBERTa, ALBERT, and RoBERTa, it exhibits higher accuracy and efficiency. Full article

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

►▼ Show Figures

Figure 1

20 pages, 3148 KB  

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 4199

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)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-9