Cyber Security and Applications最新文献

MedicalFuzzySec: A novel steganography technique using fuzzy logic to secure electronic patient data (EPD) concealment in medical images MedicalFuzzySec：一种新的隐写技术，利用模糊逻辑来确保医学图像中电子患者数据（EPD）的隐藏

Cyber Security and Applications Pub Date : 2025-08-14 DOI: 10.1016/j.csa.2025.100113

Moh Rosy Haqqy Aminy , Ntivuguruzwa Jean De La Croix , Tohari Ahmad , Emmanuel Bugingo , François Xavier Rugema

{"title":"MedicalFuzzySec: A novel steganography technique using fuzzy logic to secure electronic patient data (EPD) concealment in medical images","authors":"Moh Rosy Haqqy Aminy , Ntivuguruzwa Jean De La Croix , Tohari Ahmad , Emmanuel Bugingo , François Xavier Rugema","doi":"10.1016/j.csa.2025.100113","DOIUrl":"10.1016/j.csa.2025.100113","url":null,"abstract":"<div><div>Medical diagnostic systems generate sensitive patient information that requires optimal protection during transmission and storage. Image steganography provides a secure method for embedding secret data, making it imperceptible to the naked eye as part of the original image. However, applying general image steganography directly to medical images can compromise the quality of the transmitted data, and the distortions make the image hosting the secret information appear suspicious and inaccurate for medical interpretation. Steganography in medical images is in its early stages, focusing primarily on basic data-hiding techniques with limited security enhancements. This study introduces MedicalFuzzySec, a dedicated steganographic framework for concealing Electronic Patient Data (EPD) in medical images through fuzzy logic-guided difference expansion. The originality of MedicalFuzzySec lies in its adaptive embedding mechanism, which selectively identifies optimal pixel regions using fuzzy inference rules to ensure high data security with minimal impact on diagnostic image quality. MedicalFuzzySec addresses the limitations of existing approaches, including image degradation and insufficient payload handling, by offering a secure, high-fidelity solution tailored to clinical image standards. Experimental results confirm that MedicalFuzzySec consistently achieves high imperceptibility and robustness, with PSNR values ranging from 56.06 dB to 76.29 dB and SSIM values from 0.989 to 0.999, positioning it as a state-of-the-art solution for secure EPD transmission in medical systems.</div></div>","PeriodicalId":100351,"journal":{"name":"Cyber Security and Applications","volume":"3 ","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PASTAD: A Context-Aware Threat Modeling Methodology for Unmanned Aerial Systems 无人机系统环境感知威胁建模方法

Cyber Security and Applications Pub Date : 2025-08-14 DOI: 10.1016/j.csa.2025.100111

Fahd DEHBI, Mohamed ZRAIB, Ahmed CHEBAK

{"title":"PASTAD: A Context-Aware Threat Modeling Methodology for Unmanned Aerial Systems","authors":"Fahd DEHBI, Mohamed ZRAIB, Ahmed CHEBAK","doi":"10.1016/j.csa.2025.100111","DOIUrl":"10.1016/j.csa.2025.100111","url":null,"abstract":"<div><div>Unmanned Aerial Systems (UAS) have emerged as a transformative technology across multiple domains, including military, logistics, agriculture, and topography. However, limited attention to security during system design, combined with evolving threats and expanding attack surfaces, continues to expose UAS to significant cyber risks. Ensuring the secure operation of these systems is critical, as vulnerabilities can compromise mission success, system integrity, and public safety. To address these challenges, threat modeling is widely regarded as a fundamental practice in system security engineering. Nevertheless, the absence of methodologies explicitly developed for UAS has led practitioners to adopt approaches originally designed for IT systems. These methodologies often fail to account for the distinctive characteristics of UAS as Cyber-Physical Systems (CPS), thereby limiting their effectiveness. To bridge this gap, this study introduces the Process for Attack Simulation and Threat Analysis for Drones (PASTAD), a threat modeling methodology adapted from the PASTA framework and tailored to UAS. Built upon a layered architectural model and robust risk assessment metrics, PASTAD provides a structured, systematic, and context-aware process for identifying threats, evaluating risks, and guiding risk mitigation strategies. The methodology is demonstrated through an illustrative case study involving a fixed-wing UAS deployed in airport surveillance. This study further evaluates PASTAD, discusses its scalability and potential generalization across other UAS platforms, and outlines future directions to advance threat modeling practices and strengthen the resilience of these systems in critical applications.</div></div>","PeriodicalId":100351,"journal":{"name":"Cyber Security and Applications","volume":"3 ","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sysmon event logs for machine learning-based malware detection 用于基于机器学习的恶意软件检测的系统事件日志

Cyber Security and Applications Pub Date : 2025-07-23 DOI: 10.1016/j.csa.2025.100110

Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan

{"title":"Sysmon event logs for machine learning-based malware detection","authors":"Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan","doi":"10.1016/j.csa.2025.100110","DOIUrl":"10.1016/j.csa.2025.100110","url":null,"abstract":"<div><div>Malware poses a significant threat to modern computing environments, necessitating advanced detection techniques that can adapt to evolving attack methods. This study focuses on dynamic malware analysis using machine learning models to process detailed data from Sysmon Event Logs, a crucial sources of system information that record both running program activities. Sysmon events contain various information on what a program is doing during execution, such as created processes, initiated network connection, DNS queries, modified file and registry keys, and other type of events. Such information can be used to classify malicious or benign software. In this research, we employed various machine learning algorithms, both classification (supervised learning) and outlier detection (unsupervised learning) approaches, such as Naive Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM) for supervised learning, and Isolation Forest, Local Outlier Factor (LOF), and One-Class SVM for unsupervised learning. An extensive set of experiment were conducted to look for the best approach and the most relevant features. Principal Component Analysis (PCA) was applied to select the most relevant features for both supervised and unsupervised learning models. The experiments showed that the Local Outlier Factor (LOF) model with its twenty best features achieved the best performance, with an F1 score of 0.9873.</div></div>","PeriodicalId":100351,"journal":{"name":"Cyber Security and Applications","volume":"3 ","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive steganography based on motion vectors for H.264/AVC H.264/AVC中基于运动矢量的自适应隐写

Cyber Security and Applications Pub Date : 2025-07-11 DOI: 10.1016/j.csa.2025.100109

Rusul Nabeel Al-Mallah , Mohammed Hazim Al-Jammas

引用次数: 0

Lyric-based passwords: Enhancing security and recall with AI 基于歌词的密码：通过人工智能增强安全性和召回性

Cyber Security and Applications Pub Date : 2025-07-10 DOI: 10.1016/j.csa.2025.100108

Jared Wise, Md Tamjidul Hoque

{"title":"Lyric-based passwords: Enhancing security and recall with AI","authors":"Jared Wise, Md Tamjidul Hoque","doi":"10.1016/j.csa.2025.100108","DOIUrl":"10.1016/j.csa.2025.100108","url":null,"abstract":"<div><div>In the digital age, text-based passwords remain the cornerstone of user authentication. However, the balance between security and memorability remains a significant challenge. Users often face a dilemma between creating complex passwords that are difficult to remember and simpler ones that are vulnerable to attacks.</div><div>This research introduces a novel approach to password generation by leveraging linguistic patterns from song lyrics and advanced machine learning models. By processing over 5 million lyrics from the AZ Lyrics and Genius datasets, we identify memorable linguistic constructs, such as verb phrases, to create secure and user-friendly passwords. Transformer architectures are employed for password generation, while LSTM-based models assess their security.</div><div>A web application integrates these features to enhance usability, offering mnemonic aids such as narrative generation and interactive tools for real-time password creation. This system educates users on best practices and simplifies password management through an engaging interface. Comparative studies demonstrate that lyric-based passwords outperform traditional recall and security metrics methods. By balancing usability and robustness, this approach sets a new standard for password management systems and offers a forward-thinking solution to a persistent cybersecurity challenge.</div></div>","PeriodicalId":100351,"journal":{"name":"Cyber Security and Applications","volume":"3 ","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous identity-based threat segmentation for zero trust architecture 基于自主身份的零信任体系结构威胁分割

Cyber Security and Applications Pub Date : 2025-06-22 DOI: 10.1016/j.csa.2025.100106

Sina Ahmadi

引用次数: 0

Public-Key replacement attacks on lightweight authentication schemes for resource-constrained scenarios 针对资源受限场景的轻量级身份验证方案的公钥替换攻击

Cyber Security and Applications Pub Date : 2025-06-18 DOI: 10.1016/j.csa.2025.100102

Fei Zhu , Ying Hu , Yufei Ren , Bingfei Han , Xu Yang

{"title":"Public-Key replacement attacks on lightweight authentication schemes for resource-constrained scenarios","authors":"Fei Zhu , Ying Hu , Yufei Ren , Bingfei Han , Xu Yang","doi":"10.1016/j.csa.2025.100102","DOIUrl":"10.1016/j.csa.2025.100102","url":null,"abstract":"<div><div>Ensuring data integrity and data source trustworthiness during data sharing has always attracted the attention of researchers. Very recently, Zhu et al. designed a lightweight conditional privacy-preserving identity authentication scheme for securing vehicular ad-hoc networks. Feng et al. constructed an authentication transmission mechanism for artificial intelligence generated image content. Zhu et al. and Feng et al. proposed a lightweight certificateless aggregate signature (CLAS) scheme as their respective foundation signature schemes. They claimed that their constructions were provably secure against several types of security attacks. In this work, by analyzing their respective underlying CLAS schemes, we found that their schemes are unable to achieve unforgeability, which is the most critical property that a signature scheme should provide. In particular, for each scheme, we show that a malicious public-key replacement attacker has the ability to forge a valid signature on any false message. Taking Zhu et al.’s scheme as an example, such an attack allows a malicious attacker to impersonate an honest vehicle to broadcast fraudulent information about road conditions, causing traffic congestion or even accidents. We also analyze the reason for such an attack and provide corresponding improvement suggestions.</div></div>","PeriodicalId":100351,"journal":{"name":"Cyber Security and Applications","volume":"3 ","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ensemble learning framework for enhanced anomaly and failure detection in IoT systems 用于增强物联网系统中异常和故障检测的集成学习框架

Cyber Security and Applications Pub Date : 2025-06-17 DOI: 10.1016/j.csa.2025.100105

Ismail Bibers, Mustafa Abdallah

{"title":"An ensemble learning framework for enhanced anomaly and failure detection in IoT systems","authors":"Ismail Bibers, Mustafa Abdallah","doi":"10.1016/j.csa.2025.100105","DOIUrl":"10.1016/j.csa.2025.100105","url":null,"abstract":"<div><div>The rapid proliferation of Internet of Things (IoT) devices has revolutionized modern connectivity but also introduced significant cybersecurity challenges due to heterogeneous architectures, resource limitations, and expanding attack surfaces. In this study, we propose a flexible ensemble-based anomaly detection framework tailored for IoT environments. By integrating diverse machine learning models including decision trees, support vector machines, and neural networks through techniques such as bagging, boosting, blending, and stacking, our approach aims to enhance detection accuracy and robustness against evolving threats. We evaluate the framework on two benchmark datasets: one from a smart manufacturing setting using MEMS sensors, and the other from the N-BaIoT dataset, which targets botnet detection in IoT networks. Evaluation results demonstrate that ensemble methods consistently outperform individual classifiers across key metrics, including accuracy, precision, recall, and F1-score. For the MEMS dataset, advanced ensemble methods deliver an absolute increase of approximately 2.0 % in anomaly detection accuracy over the top-performing single AI method. For the N-BaIoT dataset, the average accuracy of all ensemble approaches is 95.53 % while that for single AI models is 73.82 %. Additionally, we assess runtime performance to gauge their suitability for real-time applications. We also show the confusion matrices and ROC curves of different models used in our framework. To promote reproducibility, we have released our codebase, trained models, and processed datasets. This work offers practical insights into building secure and reliable IoT systems and highlights the potential of ensemble learning in this context.</div></div>","PeriodicalId":100351,"journal":{"name":"Cyber Security and Applications","volume":"3 ","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable AI and machine learning for robust cybersecurity in smart cities 可解释的人工智能和机器学习为智能城市提供强大的网络安全

Cyber Security and Applications Pub Date : 2025-06-15 DOI: 10.1016/j.csa.2025.100104

Shruti Gupta , Jyotsna Singh , Rashmi Agrawal , Usha Batra

{"title":"Explainable AI and machine learning for robust cybersecurity in smart cities","authors":"Shruti Gupta , Jyotsna Singh , Rashmi Agrawal , Usha Batra","doi":"10.1016/j.csa.2025.100104","DOIUrl":"10.1016/j.csa.2025.100104","url":null,"abstract":"<div><div>An emerging application of such new technologies is in urban development, with cities increasingly utilizing them to address social, environmental, and urban issues. IoT has paved the way for Smart Cities, while AI-fueled big data has revolutionized progressive urbanization. However, initiatives to promote technology must be balanced by principles of sustainability and livability. As deep learning has advanced rapidly, creating increasingly sophisticated technologies has led to highly complex — and often opaque — models that can be difficult to interpret. It becomes increasingly difficult to establish trust and maintain transparency when decision-making systems are based on such opaque and complex structures. This article explores the urban promise of AI and presents a new framework infusion of AI into cityscapes. The new direction is socially oriented through the inclusion of elements such as values, urban metabolism, and governance. A systematic review of machine-learning applications in cybersecurity also discusses the importance of explainability for overcoming the challenges it entails. The importance of assuring the explainability, interpretability, and intelligibility of autonomous systems will also be part of this discussion, especially in the context of developing smart cities using AI-based technologies.</div></div>","PeriodicalId":100351,"journal":{"name":"Cyber Security and Applications","volume":"3 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure and efficient drug supply chain management system: Leveraging polymorphic encryption, blockchain, and cloud storage integration 安全高效的药品供应链管理系统：利用多态加密、区块链和云存储集成

Cyber Security and Applications Pub Date : 2025-06-06 DOI: 10.1016/j.csa.2025.100103

Muammar Shahrear Famous , Samia Sayed , Rashed Mazumder , Risala T. Khan , M. Shamim Kaiser , Mohammad Shahadat Hossain , Karl Andersson , Rahamatullah Khondoker

{"title":"Secure and efficient drug supply chain management system: Leveraging polymorphic encryption, blockchain, and cloud storage integration","authors":"Muammar Shahrear Famous , Samia Sayed , Rashed Mazumder , Risala T. Khan , M. Shamim Kaiser , Mohammad Shahadat Hossain , Karl Andersson , Rahamatullah Khondoker","doi":"10.1016/j.csa.2025.100103","DOIUrl":"10.1016/j.csa.2025.100103","url":null,"abstract":"<div><div>The counterfeit medication infiltration within global supply chains poses a major public health threat. To address this, a collaborative effort among governments, regulators, and pharmaceutical companies is essential to secure the global/local supply chain. This paper proposes a novel approach that leverages blockchain technology, polymorphic encryption, and cloud storage to tackle security risks and privacy concerns in medication supply chains. The framework integrates a drug supply chain decentralized application (also called SCMapp) within the Ethereum blockchain, enabling functionalities like secure supplier onboarding, encrypted data management, cloud storage integration, and efficient data retrieval. This approach aims to revolutionize drug supply chain management by enhancing security, transparency, and overall efficiency, ensuring adherence to global health regulations. A safe and effective method for managing drug supply chains is provided by the suggested Drug Supply Chain Management System. The proposed model outperformed existing solutions in terms of security, efficiency, and traceability. The combination of encryption, blockchain, and cloud storage provided a comprehensive approach to address the challenges of drug supply chain management. The comparison analysis highlighted the unique advantages of the proposed model over other methods.</div></div>","PeriodicalId":100351,"journal":{"name":"Cyber Security and Applications","volume":"3 ","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0