Software Distribution Research Articles

Decentralized Integrity: Blockchain-Based Frameworks for Enhancing Software Update Security

This article examines the application of blockchain technology as a solution for secure and transparent software update distribution. As cyber threats evolve, traditional centralized update mechanisms face increasing vulnerabilities to tampering and unauthorized modifications. We propose a blockchain-based framework that leverages distributed ledger technology to create an immutable, decentralized environment for software updates. Our article demonstrates how this approach can eliminate single points of failure, ensure update integrity through cryptographic signing and network-wide verification, and enhance transparency in the update process. We discuss the system architecture, including update creation, distribution, and verification mechanisms, and evaluate its integration with existing software ecosystems. While acknowledging challenges such as scalability and implementation costs, we argue that blockchain-based solutions significantly advance securing software distribution. Our findings suggest that this approach has the potential to become a standard practice in the industry, substantially improving trust and reliability in software update systems. This article contributes to the growing body of research on blockchain applications in cybersecurity and provides insights for future developments in secure software distribution methods.

Comprehensive Analysis of Security Requirements Engineering Approaches with Assurance Perspective

Given the wide distribution of software and its current growth, security is inevitable, and software systems have become more complex. To avoid the complexity and protect valuable assets of software systems, researchers, and practitioners in the fields of security requirement engineering (SRE) and security assurance advise incorporating security requirements early in software development life cycle (SDLC). Nowadays, security requirements specification and identification are an active research area for ensuring the effectiveness of software systems. However, security requirement identification is a challenging task due to sheer numbers of threats and attacks to a software system. Previous researchers have identified different SRE techniques, each looking at the same problem from a different perspective. Therefore, a literature review conducted on SRE techniques. We compared and analyzed different SRE techniques to find the most suitable SRE techniques for researchers and practitioners. Selected SRE techniques compared based on various parameters, such as different attributes, requirement elicitation, requirement analysis, project size, and integration of standards. The literature also indicates that among SRE approaches, security quality requirements engineering (SQAURE), secure tropos, security requirements engineering process (SREP), and comprehensive lightweight application security process (CLASP) are the most used SRE approaches that focus on activities such as threats, security requirements identification, and security objectives identification. Unfortunately, several SRE approaches fail to explicitly integrate the security standard and security assurance perspective. Three questions developed for this study: Question 1 is based on comparison of existing SRE approaches, Question 2 is based on SRE with security requirements elicitation and analysis, and Question 3 highlights the incorporation of security requirements assurance by SRE approaches implicitly or explicitly. The researcher must work with the developer to easily adopt SRE approaches to elicit and ensure security requirements for the software system.

MolPlay: Democratizing Interactive Molecular Simulations and Analyses with a Portable, Turnkey Platform.

Computer-based tools for visualizing and manipulating molecular structures in real-time hold immense potential for accelerating research and improving education, but are only used to a limited extent. This paper explores the possibilities of these powerful techniques and presents a classification of common interactive modeling tasks, such as assembly, deformation, sampling of rare events, along with relevant use cases, especially for the study of membranes and membrane proteins. I introduce MolPlay─a platform that provides a ready-to-use software environment with a curated set of hands-on examples to democratize access to interactive molecular simulations and analyses (IMSA). These research tools allow researchers to visualize, manipulate, and study digital models of molecules in real-time and can be started directly from a small, portable storage device. The portable, self-contained nature of MolPlay enables versatile deployment scenarios ranging from outreach to teaching in computer laboratories to facilitating the distribution of research software. Initial tests at an international workshop have shown that MolPlay appeals to different target groups and lowers the access barriers to IMSA. By consolidating more than a decade of expertise into one accessible platform, MolPlay represents a significant step toward broader adoption of these underutilized but powerful computational techniques in education and research.

Software supply chain security: a systematic literature review

ABSTRACT In recent years, software supply chain security has attracted significant research attention. This research subject is concerned both with the security of infrastructures used to build software and deliver it to end users and with the security of software that contains external dependencies such as third-party packages and libraries, and its chief goal is to ensure that no vulnerabilities are introduced in the path between developers and users. This paper presents a Systematic Literature Review to identify knowledge gaps in software supply chain security. For this, we considered studies published between 2012 and 2023 in the search engines of IEEE Xplore, ACM Digital Library, Engineering Village, Scopus, and arXiv. Of the 2051 studies obtained in the primary survey, only 85 are relevant for this research. Analyzing the studies, we observed gaps such as little discussion of software supply chains that involve cloud components, few proposals focused on the software distribution process, and a lack of use of threat modeling frameworks to help identify threats to the supply chain and validate the results.

Enhancement of cyber security in IoT based on ant colony optimized artificial neural adaptive Tensor flow

ABSTRACT The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.

Mining software insights: uncovering the frequently occurring issues in low-rating software applications.

In today's digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data

AbstractBackground and purposeMultiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS.MethodsFor this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects.ResultsThe projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model‐based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, “peer‐to‐peer,” and marketing distribution.ConclusionsThis review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

Implementation and testing of a KNS topology in an InfiniBand cluster

The InfiniBand (IB) interconnection technology is widely used in the networks of modern supercomputers and data centers. Among other advantages, the IB-based network devices allow for building multiple network topologies, and the IB control software (subnet manager) supports several routing engines suitable for the most common topologies. However, the implementation of some novel topologies in IB-based networks may be difficult if suitable routing algorithms are not supported, or if the IB switch or NIC architectures are not directly applicable for that topology. This work describes the implementation of the network topology known as KNS in a real HPC cluster using an IB network. As far as we know, this is the first implementation of this topology in an IB-based system. In more detail, we have implemented the KNS routing algorithm in the OpenSM software distribution of the subnet manager, and we have adapted the available IB-based switches to the particular structure of this topology. We have evaluated the correctness of our implementation through experiments in the real cluster, using well-known benchmarks. The obtained results, which match the expected performance for the KNS topology, show that this topology can be implemented in IB-based clusters as an alternative to other interconnection patterns.

Introduction of the Capsules environment to support further growth of the SBGrid structural biology software collection.

The expansive scientific software ecosystem, characterized by millions of titles across various platforms and formats, poses significant challenges in maintaining reproducibility and provenance in scientific research. The diversity of independently developed applications, evolving versions and heterogeneous components highlights the need for rigorous methodologies to navigate these complexities. In response to these challenges, the SBGrid team builds, installs and configures over 530 specialized software applications for use in the on-premises and cloud-based computing environments of SBGrid Consortium members. To address the intricacies of supporting this diverse application collection, the team has developed the Capsule Software Execution Environment, generally referred to as Capsules. Capsules rely on a collection of programmatically generated bash scripts that work together to isolate the runtime environment of one application from all other applications, thereby providing a transparent cross-platform solution without requiring specialized tools or elevated account privileges for researchers. Capsules facilitate modular, secure software distribution while maintaining a centralized, conflict-free environment. The SBGrid platform, which combines Capsules with the SBGrid collection of structural biology applications, aligns with FAIR goals by enhancing the findability, accessibility, interoperability and reusability of scientific software, ensuring seamless functionality across diverse computing environments. Its adaptability enables application beyond structural biology into other scientific fields.

Just-in-time Software Distribution in (A)IoT Environments

Just-in-time Software Distribution in (A)IoT Environments

Packaging and containerization of computational methods.

Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.

丘陵山地果园单轨式防霜机的仿真与试验

In order to solve the problems of artificial operation, high labor intensity and low efficiency of frost prevention in hilly orchards, this study takes hilly orchards as the research object. Based on the agronomic requirements of frost prevention in hilly orchards, a smoke anti-frost machine based on the transport track of hilly orchards is designed. With the help of Fluent software, the spatial and temporal distribution characteristics of smoke mass concentration and temperature in the process of smoke diffusion are simulated and analyzed based on the discrete phase model. The results show that the smoke continues to erupt at a speed of 6 m / s, and the plume volume gradually increases. After 9 s, the smoke reaches a stable diffusion state. Based on the established smoke diffusion simulation model, the orthogonal test analysis of the working parameters at the smoke outlet of the frost-proof machine was carried out with the temperature increase near the canopy of the fruit tree as the index. The results showed that the best combination of working parameters at the smoke outlet of the frost-proof machine was the smoke outlet speed of 10 m / s, the smoke outlet angle of 60 °, and the smoke outlet diameter of 140 mm. The field test results show that the frost-proof machine can continuously operate for 0.5 h at a running speed of 0.6 m / s, and the temperature in the height range of 1.5 m to 4 m in the working area can be increased by about 1.7 ° C. This study is of great significance to reduce the frost disaster loss of hilly orchards and improve the economic benefits of hilly orchards.

Research on the tool influence function characteristics of magnetorheological precession finishing (MRPF).

Magnetorheological finishing (MRF) technology is characterized by its high convergence rate and minimal subsurface damage as advantages. However, the non-Gaussian type tool influence function (TIF) it generates may cause mid-frequency errors and oriented surface texture issues. Magnetorheological precession finishing (MRPF) technology capable of generating Gaussian-like removal functions, lacks a clearly defined removal function model. This study acquired polishing spots in tilted polishing, discrete precession, and continuous precession modes via fixed-point polishing experiments. Using Multiphysics simulation software, stress and velocity distribution in the contact area were simulated. A TIF model, incorporating the synergistic effects of pressure and shear force and multiple influence coefficients, was proposed based on velocity characteristics across the three modes. To accurately predict the TIF, surface topographies with varying coefficients were constructed using this model, analyzing the coefficients' impact on the TIF profile. Optimal coefficients were identified using a least fit error algorithm. Further analysis of the TIF's internal textures revealed that the precession mode of MRPF yields superior surface quality, thereby elucidating the material removal mechanism of MRPF and laying a theoretical groundwork for advancing processing technologies.

Factors Associated With P. knowlesi Clusters in Cleared Foci Areas in Sabah

Introduction: Plasmodium knowlesi cases is increasing in trend despite integrated vector management leading to morbidity and death. This study determines the association between P. knowlesi cluster with vector control, risk factors and its spatiotemporal distribution in cleared foci area in Sabah. Methods: This is a cross sectional study between 2017 and 2019 involving P. knowlesi cases in cleared foci registered in Vekpro Online. Simple logistic regression tests were performed using Statistics Package for the Social Sciences (SPSS) software and spatiotemporal distribution mapped using Quantum Geographic Information System (QGIS) software. Results: A total of 4,739 cases were analysed and high P. knowlesi cases reported in Ranau (17.1%), Keningau (12.8), Tenom (10.8%), Kudat (8.1%) and Kota Marudu (7.2%). Younger age groups (0 – 9 years), the Rungus ethnicity, and the use of vector control measures were positively associated with P. knowlesi clusters with the odds ratio of 2.421 (95% CI [1.596, 3.674]), 2.654 (95% CI [1.899, 3.708]), and 2.559 (95% CI [2.175, 3.011]) respectively. However, age group of 50-59 years, male gender, occupations related to agriculture, and employment in the private sector or self-employed were negatively associated with P. knowlesi clusters in cleared foci in Sabah with odds ratio between 0.478 and 0.771. The prevalence of P. knowlesi in cleared foci in Sabah were 36.2%, 44.09% per and 41.59% per 100,000 population in 2017, 2018 and 2019 respectively. The distribution differs in districts. Conclusion: Tailored measures and spatiotemporal distribution of P. knowlesi cluster is useful to reduce transmission and aids focus-based planning.

Comparison Between the Properties of Three New Designs of Pinhole Magnetic Lens

Three different new designs of an objective pinhole lens were compared. Their magnetic and optical properties were studied using Finite Element Method for Calculating Magnetic Field Distribution (FEM-CMFD) and Magnetic Electron Lens Optical Properties (MELOP) software. The focal length (fo), spherical aberration (Cs), chromatic aberration (Cc), and the resolving power (δ) of each lens were compared at a range of relatively corrected acceleration voltages (Vr = 100 V-100 kV) and a constant excitation of (NI = 5280 A-t (where A represents Ampere and t represents turn)) and current density (2 A/mm2) to find the best design. The L1 lens is similar to the Gemini lens. L2 and L3 are the traditional pinhole lens shape. The L1 lens gave the best results in terms of optical and magnetic properties as its optical properties at medium and low acceleration voltages. L2 and L3 Lenses had almost identical magnetic properties, but the difference was apparent in optical properties.

Investigative Situations Arising at the Initial and Subsequent Stages of Investigating the Creation, Distribution and Use of Malicious Software

The paper is devoted to the analysis of investigative situations that may arise at different stages of the investigation of a crime under Article 273 of the Criminal Code of the Russian Federation: creation, distribution and use of malicious software. Based on the data presented, the opinions of scientists and scientific literature, the need for a more precise study of the circumstances under which this or that investigative situation is considered at the initial and subsequent stages of the investigation was identified, since there are a huge number of combinations of circumstances. In this regard, we propose to highlight certain types of circumstances of investigative situations that arise at the initial and subsequent stages of the investigation, describe possible options for their forms and then, based on the tasks that arise for the investigator and law enforcement agencies in connection with them, describe recommended actions that could allow you to solve problems during the preliminary investigation and achieve a legal decision within the framework of the preliminary investigation.

Вопросы кибербезопасности в решении глобальных задач ООН по борьбе с терроризмом

Today, cybersecurity threats are one of the main problems faced by all countries. Cyberspace plays an important role in our modern life, uniting people and communities around the world, providing them with the opportunity for socialization and self-organization. This article highlights the main types of cyberattacks and threats, as well as suggests methods to prevent them. Various types of cybercrimes are considered, as well as the necessary actions to combat cyberterrorism. Cybercriminals actively use various methods and techniques to carry out cyber attacks, including unauthorized access to systems, theft of personal information, financial fraud and the distribution of malicious software. Cyberterrorism is also a danger, since cyber attacks can be used to create chaos and disrupt the functioning of critical infrastructures, such as government institutions, healthcare systems, energy, and so on. To ensure cybersecurity, cooperation is necessary both at the state and at the international level. Cooperation at the state level includes joint initiatives to exchange information about threats, joint trainings and exercises to improve the skills of cybersecurity specialists, as well as the development of common standards and policies in the field of cybersecurity. At the international level, it is important to establish a dialogue between various countries and international organizations, such as the UN, the European Union, NATO and others. This will allow us to coordinate strategies, coordinate measures to prevent and respond to cyber attacks.

Commissioning of the ALICE readout software for LHC Run 3

ALICE (A Large Ion Collider Experiment) is a heavy-ion detector studying the physics of strongly interacting matter and the quarkgluon plasma at the CERN LHC (Large Hadron Collider). During the second long shut-down of the LHC, the ALICE detector was upgraded to cope with an interaction rate of 50 kHz in Pb-Pb collisions, producing in the online computing system (O2) a sustained input throughput of 3.5 TB/s. In the past years, the O2/FLP project built the new data-acquisition system capable of handling this load. It consists of 200 readout nodes, collecting the data transferred from over 8000 detector links to PCs memory by dedicated PCI boards. The readout software manages the hardware and software memory buffers used for DMA and inter-process communication. It initiates the data flow, performs on-the-fly consistency checks, formats the data, reports performance, and finally pushes the data to the local processing pipeline. The output is then sent by the data distribution software over 100Gb/s links to a dedicated event processing farm. The readout software modular design allowed to address the manifold needs faced during the prototyping, installation and commissioning phases, which proved essential from the lab tests to physics production, like file replay and recording, or online multi-threaded LZ4 compression. We will describe the hardware and software implementation of the O2 readout system, and review the challenges met during the commissioning and first months of operation with LHC collisions in 2022.

Comparative analysis of package managers Flatpak and Snap used for open-source software distribution

This article presents the result of a research of the Flatpak and Snap package managers used to distribute open-source software on Linux systems. Both package managers are characterised by their versatility and implementation of sandboxing. As part of the research, a test application was prepared, which was built in the Flatpak and Snap formats and published in the official software repositories, where for Flatpak it is Flathub and for Snap it is the Snap Store. The prepared application was first used to test and compare the implementation of sandboxing rules. This was followed by tests of RAM usage and start-up time by the application installed in both formats. The result of the research is an analysis of the measurement results and the drawing of conclusions.

Adversarial Defense: A GAN-IF Based Cyber-security Model for Intrusion Detection in Software Piracy

Software-piracy continues to be most critical distress, posing grave threats to digital-assets and financial stability. Traditional Intrusion Detection systems (IDS) often battles hard to identify latest piracy attempts owing to their dependence on pre-established patterns. To effectively address this we attempt to suggest innovative approach leveraging DL based Generative Adversarial Networks (GANs) and ML based Isolation Forest (IF) for detecting software piracies. Our proposed GAN-IF based cyber-security model performs its functions by training a Generator network to mimic the behavior of genuine software applications. Discriminator network discriminates between legitimate and pirated software. Isolation Forests assists in detecting anomalies in diverse conditions, including unseen attacks. Integrated training based on DL and ML framework enables efficient learning and adaptation with respect to piracy challenges, making it highly-successful against prior known threats. There are several DL models which are utilized in IDS operations having limitations in terms of robustness, interpretability. Utilizing GAN in the context of cyber-security to combat software-piracy can have noteworthy merits since GANs can precisely identify forged software as they are skilled at generating fake content resembling actual. Training a GAN on legitimate software, helps to learn and identify disparities in pirated versions. Isolation-Forest can detect anomalies in software distribution networks or user behavior with respect to software usage by recognizing abnormal patterns indicating software piracy, like illegal access or sharing of software licenses. Our proposed model combines GANs and Isolation Forests, excels at accurately detecting subtle indicators of software piracy, a capability that traditional methods may fail to recognize. ML-DL integrated model continuously learns and updates its detection capabilities in response to evolving piracy tactics, making it resilient against zero-day attacks, polymorphic malware. Through adversarial training, ml-model minimizes false alarms and focuses only on genuine threats. In our evaluation, we demonstrate the effectiveness of GAN-IF based cyber-security model in detecting software piracy attempts across various scenarios. Results indicate that our approach outperforms traditional solutions in terms of detection accuracy and adaptability.