Linux Operating System Research Articles

Wazuh SIEM for Cyber Security and Threat Mitigation in Apparel Industries

Security of IT infrastructure is critical in the modern digital environment, particularly for industries that manufacture clothing. The application of the Wazuh Security Information and Event Management (SIEM) system to improve security monitoring and compliance for an IT system in the apparel industry is the main topic of this study. To improve the apparel industry cyber threat monitoring system in real-time monitoring, detecting the threat vectors that support the centralized management system, to meet these objectives of the company, an SIEM security management system has been installed and configured with the components of Wazuh manager, indexer, Wazuh agent, and Wazuh dashboard as well. An Oracle virtual lab environment was created to install and configure the Wazuh system a robust Linux operating system was installed at the server level and Windows 10 was installed on the client end to the authenticity failure of the security system. To implement the Wazuh system, the project management guideline was followed which ensured the planning and implementation of the project, testing phases, and along with review and maintenance. Wazuh security management system successfully identified the authentical failure reports and vulnerabilities and generated automated reports from the system that showed the result of 1 critical, 19 high, and 5 medium levels of vulnerabilities shown in Figure 6, and the necessary patch recommended to solve the critical issues. On the other hand, the system also generated security events as well as integrity monitoring system reports. The deployment of the Wazuh security management system ensured the robust cost-effective security management system with high quality for detecting cyber threats which supports the regulatory and compliance requirements and is a viable tool for the apparel industry to protect the stakeholders’ interest.

Causal Inconsistencies Are Normal in Windows Memory Dumps (Too)

Main memory contains valuable information for criminal investigations, e.g., process information or keys for disk encryption. Taking snapshots of memory is therefore common practice during a digital forensic examination. Inconsistencies in such memory dumps can, however, hamper their analysis. In this article, we perform a systematic assessment of causal inconsistencies in memory dumps taken on a Windows 10 machine using the kernel-level acquisition tool WinPmem. We use two approaches to measure the quantity of inconsistencies in Windows 10: (1) causal inconsistencies within self-injected memory data structures using a known methodology transferred from the Linux operating system, and (2) inconsistencies in the memory management data structures of the Windows kernel using a novel measurement technique based on properties of the virtual address descriptor (VAD) tree. Our evaluation is based on a dataset of more than 180 memory dumps. As a central result, both types of inconsistency measurement reveal that a high number of inconsistencies is the norm rather than the exception. We also correlate workload and execution time of the memory acquisition tool to the number of inconsistencies in the respective memory snapshot. By controlling these factors it is possible to (somewhat) control the level of inconsistencies in Windows memory dumps.

Computer Attacks and Their Impact on the Security of Servers with Linux Operating System of Local Government Entities

This research aims to determine the incidence of computer attacks on servers with the Linux operating system of local government entities. The study is limited to the decentralized autonomous government (GAD) of the Ecuadorian Amazon. Initially, the most common computer attacks that have affected organizations in recent years were determined using statistical reports from important computer security companies positioned as leaders in Gartner’s magic quadrant. Phishing and distributed denial of service (DDoS) attacks are established as computer attacks under study. Computer attacks are carried out before and after mitigation measures are established. With the help of the information systems risk analysis and management methodology (MAGERIT), the vulnerability, level of impact, and risk computer attacks cause on servers with the Linux operating system are determined. This research aims to serve as a guide to the information technology departments of local governments in implementing mechanisms that safeguard the most important asset of an organization, such as information. Keywords: computer attack, phishing, DDoS, MAGERIT, Linux. Resumen La presente investigación tiene como finalidad determinar la incidencia de los ataques informáticos en los servidores con sistema operativo Linux de entidades de gobierno local. El estudio está delimitado a un gobierno autónomo descentralizado (GAD) de la Amazonía ecuatoriana. Inicialmente se determina los ataques informáticos más comunes que han afectado a las organizaciones en los últimos años haciendo uso de reportes estadísticos de importantes empresas de seguridad informática posicionadas como líderes en el cuadrante mágico de Gartner. Se establece como ataques informáticos objeto de estudio los ataques de phishing y de denegación de servicio distribuido (DDoS). Se realizan ataques informáticos antes y después de establecer las medidas de mitigación y con la ayuda de la metodología de análisis y gestión de riesgos de los sistemas de información (MAGERIT) se determina la vulnerabilidad, el nivel de impacto y el riesgo que los ataques informáticos provocaban en los servidores con sistema operativo Linux. El presente trabajo de investigación pretende ser de gran utilidad y servir de guía a los departamentos de tecnologías de la información de gobiernos locales en la implementación de mecanismos que salvaguarden el activo más importante de una organización como lo es la información. Palabras Clave: ataque informático, phishing, ddos, magerit, linux.

A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture

This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz–4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels.

Computer Network Design and Applications for West Java Legislative Elections

Legislative elections or Pileg are an annual schedule that is routinely held in Indonesia for five years. The concept of technology 4.0 has a huge opportunity to be applied in the legislative election process in this modern era. Legislative elections that use digital devices connected in such a way have more advantages than legislative elections that use conventional methods. The application of technology 4.0 in legislative elections has advantages in terms of effectiveness, data processing speed, and costs. The design of the legislative election system is made using a network where each polling station will be provided with one server to accommodate votes at the polling station. Then the server synchronizes the data to a central server to calculate the final results obtained. The server is built using the text-based Ubuntu Linux operating system. The application used is web-based, with the programming languages used being HTML, PHP, CSS, and Javascript. The test data shows that the time required for the system to complete each stage of the legislative election process is relatively faster than conventional legislative elections. So it can be concluded that the design of the legislative election system has met the requirements to accelerate the legislative election process. This research uses an SDLC system with a prototype model. Tests are carried out using BlackBox testing to test the suitability of the application with application access.

The Hidden Threat: Analysis of Linux Rootkit Techniques and Limitations of Current Detection Tools

This paper addresses the significant threat posed by rootkits as part of the diverse malware landscape of today. Rootkits enable an attacker to regain access to an already comprised system at root-level making their prompt identification and removal crucial. However, rootkits implement advanced stealth features, enabling them to evade detection by conventional measures during analysis. Consequently, analysts generally rely on customized tools to detect the presence of a rootkit. However, our research highlights significant deficits in the tools available for the detection of rootkits on the Linux operating system, which is frequently encountered in investigations of server environments. Recognizing the need for improved awareness and capabilities among investigators, we conducted an in-depth analysis of 21 distinct Linux rootkits allowing us to dive into their techniques and features. Furthermore, we critically assessed the effectiveness of standard detection tools, revealing their limitations. Based on these insights, we propose best practices for investigators to effectively identify and detect signs of rootkit infections. Additionally, we provide a repository of indicators of compromise we extracted during our analyses to facilitate the detection of the analyzed rootkits on compromised systems along with a utility to detect rootkits via hidden files on a live system.

Design of an image processing system for heterogeneous computer architectures

Embedded real-time image processing systems are widely applied in various fields, enhancing visual experience and making life more convenient. With the development of embedded processors and operating systems, as well as in-depth research into image processing algorithms, the effects and speed of image processing have been significantly improved. This paper proposes an embedded image processing system design based on the current technological developments, centered around the Zybo processor. This includes a collaborative hardware and software design, specific hardware circuit design, and the design of a system software platform. The paper also discusses the establishment of a development environment for the Zybo system, the transplantation of the Linux operating system, the design of the file system, and the application of OpenCV library functions in image processing.

LBFextract: Unveiling transcription factor dynamics from liquid biopsy data

MotivationThe analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation, but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns. ResultsWe introduce Liquid Biopsy Feature extract (LBFextract), a comprehensive package designed to streamline feature extraction from cfDNA sequencing data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies. LBFextract facilitates the integration of preprocessing and postprocessing steps through alignment fragment tags and a hook mechanism. It incorporates various methods, including coverage-based and fragment length-based approaches, alongside two novel feature extraction methods: an entropy-based method to infer TF activity from fragmentomics data and a technique to amplify signals from nucleosome dyads. Additionally, it implements a method to extract condition-specific differentially active TFs based on these features for biomarker discovery. We demonstrate the use of LBFextract for the subtype classification of advanced prostate cancer patients using coverage signals at transcription factor binding sites from cfDNA. We show that LBFextract can generate robust and interpretable features that can discriminate between different clinical groups. LBFextract is a versatile and user-friendly package that can facilitate the analysis and interpretation of liquid biopsy data. Data and Code Availability and ImplementationLBFextract is freely accessible at https://github.com/Isy89/LBF. It is implemented in Python and compatible with Linux and Mac operating systems. Code and data to reproduce these analyses have been uploaded to 10.5281/zenodo.10964406.

CNN- based intrusion detection software for network operating system environment

Cybersecurity represents an important challenge specific to digital technology in the modern world, and is of vital importance for reducing or even preventing the impact of cybercrime. The Linux operating system is designed as open-source software that includes some features of software tools intended for network security and cybersecurity systems, such as intruder detection and penetration testing. With these tools in Linux, we need a special system to constantly detect intrusions into connected network devices. This research presents a method for detecting intrusion attacks based on analyzing the natural behavior of the system by building a special convolutional network to achieve this goal. The classification and detection results of the proposed convolutional neural network were compared with the regular machine learning method (SVM), with feature selection by correlation for both methods. Same datasets were used to train and test each of CNN and SVM. Some metrics were determined to evaluate the performance of classification and prediction models for a specific type of regular attacks, DoS and BOT attacks, where both SVM and CNN obtained an accuracy of 85.58% and 95.59%, respectively.

PRP protocol optimization method based on Q-learning under the Linux operating system

Abstract In the rapid development of industrial networks, in the face of complex and changeable network environments, the demand for reliable and efficient communication systems is becoming increasingly prominent. In this paper, a PRP protocol optimization method based on Q-learning under the Linux operating system is proposed. By introducing a Q-learning algorithm, the adaptive learning of a dynamic network environment is realized through Q-value update rules, to adjust PRP protocol parameters. The experimental results show that the optimization of PRP protocol based on Q-learning can improve the performance of PRP protocol under various network conditions and load changes, thus improving communication reliability.

COVID-19 Nursing Staff Sizing Technology.

This study shows the development of a software for calculating the number of nursing team members required for providing care during the coronavirus disease 2019 pandemic. Study about the development of a technology based on the literature about data and indicators. The indicators were systematized in the following dimensions: institutional, professional, and occupational structure, all with a focus on coronavirus disease 2019. The software was created to be used on the Web, client-server, in browsers such as Internet Chrome, Explorer, and/or Mozilla Firefox, accessing via an Internet address and also allowing access by Windows, Android, and Linux operating systems, with MySQL database used for data storage. The data and indicators related to the institutional structure for coronavirus disease 2019 were systematized with 10 dimensions and indicators, and the professional and occupational structure, with 14 dimensions and indicators. The construction of computer requirements followed the precepts of software engineering, with theoretical support from the area. In the evaluation of the software, data simulation revealed points that had to be adjusted to ensure security, data confidentiality, and easy handling. The software provides to calculate the size and quality of the team, nursing sizing required due to the needs generated by the coronavirus disease 2019 pandemic.

Development of an Operating System Supporting Intelligent Predictions and Recommendations

This Study discusses the development of an intelligent operating system feature that supports smart prediction and recommendations using artificial intelligence (AI) capabilities within the Linux operating system. The study aims to integrate AI-driven features into Linux to enhance user productivity and efficiency by providing relevant application recommendations based on user behavior patterns. The implementation involves data collection of application usage, training machine learning models for application recommendations, and integrating these features into the Linux environment. The project utilizes Python for scripting, employing libraries such as psutil, pandas, scikit-learn, and joblib for data handling and machine learning tasks. The results demonstrate successful implementation of the AI-driven recommendation system, enhancing user interaction and productivity within the Linux operating system

К ВОПРОСУ ОБЕСПЕЧЕНИЯ СЕТЕВОЙ БЕЗОПАСНОСТИ

The following are considered:  principles and means of ensuring the security of a corporate network, as well as features of virtual private networks that provide reliable protection and secure data transmission;  principles and measures to ensure the security of the corporate network, as well as the types of information protection provided by these security measures;  virtual private networks (VPNs) that provide a secure encrypted user connection to the network. Using a VPN provides reliable data protection, masking the user's geolocation, access to regional content and secure data transfer;  systems for detecting and preventing network hacking threats, among which the main focus is on the Kali Linux software package, as well as features of the Windows subsystem for Linux and Windows network monitoring tools;  Windows network monitoring tools, including utilities that work in console command-line mode and allow you to check the availability of remote personal computers and diagnose the connection. The Kali Linux software package is a distribution package for the Linux operating system that has more than 600 preinstalled network penetration testing programs. Among the features of the package, the article discusses the search and exploitation of network vulnerabilities, checking the correct configuration of the SSL certificate and open ports, tracing the data transfer route, checking the availability of servers and searching for network problems.

Implementation of the High-Performance Computing Summer Institute at Jackson State University

Between May 25, 2023 and June 21, 2023, we hosted the inaugural four-week High-Performance Computing Summer Institute at Jackson State University. This endeavor was made possible through the support of a three-year NSF CISE-MSI grant. The primary objective of this Summer Institute revolved around the engagement, education, and empowerment of minority and underrepresented students in the realm of High-Performance Computing (HPC) within the field of engineering. Nine undergraduate students with diverse background were recruited to participate in this program. Throughout the program, we immersed these students in a comprehensive curriculum that covered various critical facets of HPC. This curriculum encompassed hands-on instruction in Linux operating system command-line operations, C programming within the Linux environment, fundamental HPC concepts, parallel computing utilizing the Message Passing Interface (MPI) library, and GPU computing through OpenCL. Additionally, we delved into foundational aspects of fluid mechanics, geometric modeling, mesh generation, flow simulation via our in-house flow solvers, and the visualization of solutions. At the end of the program, every participant was tasked with delivering an oral presentation and submitting a written report encapsulating their acquired knowledge and experiences during the program. We are excited to share a detailed overview of our program's implementation with our audience. This includes insights into our utilization of ChatGPT to enhance C programming learning and our suggestion of the NSF ACCESS resources to gain access to HPC systems. We are proud to announce that the program has achieved remarkable success, as evidenced by the positive feedback we received from the participants.

Forensic analysis of Scientific Linux image using commercial and opensource forensic tools

Depending on their needs and personal preferences, people choose to use different operating systems (OS) such as Windows, Linux, and Mac. The scientific Linux Operating System (SLOS) is designed to provide a stable, secure, and high-performance computing environment for scientific research and education in a steady, scalable, and extensible manner. When criminal activities are committed by suspects involving computers and the internet, it calls for digital forensics which involves the use of scientific procedures and tools to carry out the forensic investigation and analysis of digital evidence for legal and investigative purposes. Forensic investigators use commercial and opensource tools for analysis and gathering inculpatory and exculpatory pieces of evidence. This paper presents a comparative analysis of EnCase, FTK, Autopsy, bulk-extractor, and Scalpel for analyzing the Scientific Linux image. The test scenarios were designed to find out if the selected forensic tools can be appropriately used for investigating crimes committed using the SLOS. The test scenarios include extraction and analysis of operating system details, user accounts, web browsing history, and the recovery of deleted and shredded files and this paper compares and evaluates the capability of the tools in retrieving the evidence designed in the scenarios. This systematic comparison and evaluation results would assist digital forensics practitioners, researchers, and law enforcement agencies in making informed decisions regarding the selection of tools for Scientific Linux image forensics.

Implementation Of Computer Programming Mathematical Algorithm On A Server With The Debian Linux Operating System Via Virtualbox As Supporting The Admission Of New Students In Vocational Schools

Implementation Of Computer Programming Mathematical Algorithm On A Server With The Debian Linux Operating System Via Virtualbox As Supporting The Admission Of New Students In Vocational Schools

SSHAA: A Python Package Index for visualizing features of SSH attacks with text mining in classification

ABSTRACT With the proliferation of the Internet, the number of cyber-attacks has increased worldwide. To prevent cyber-attacks, network administrators need to use analytical tools to disclose six new critical statistics such as Internet Protocol (IP) authenticity and classification, IP ownership, frequency of attacks, time of day, day of the week, and country of attack. There are four SSH attack analysis tools available, but existing tools lack visualization and cannot provide the necessary attack statistics. This paper proposes SSHAA, the first open source PyPI analysis tool that meets the requirement. Text mining technology is used for extracting data from SSH log files, classifying IPs with identified country names and calculating a variety of statistics. PyPI allows SSHAA to run on Windows, MacOS, and Linux operating systems. This paper highlights how to debut a PyPI application using SSHAA which will be the world’s first tutorial in the security journals for maximum software dissemination. According to PePy, SSHAA has been downloaded 7668 times worldwide. The substantial number of downloads signifies the efficacy of the proposed tool. The proposed system offers ten functional statistics for SSH attack analysis. The six new functional statistics to information security are significant.

CShaperApp: Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

AbstractCaenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant development. In this study, we developed a desktop software CShaperApp to segment fluorescence‐labeled images of cell membranes and analyze cellular morphologies interactively during C. elegans embryogenesis. Based on the previously proposed framework CShaper, CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine‐tuned one adapted to their in‐house dataset. Experimental results show that it takes about 30 min to process a three‐dimensional time‐lapse (4D) dataset, which consists of 150 image stacks at a ∼1.5‐min interval and covers C. elegans embryogenesis from the 4‐cell to 350‐cell stages. The robustness of CShaperApp is also validated with the datasets from different laboratories. Furthermore, modularized implementation increases the flexibility in multi‐task applications and promotes its flexibility for future enhancements. As cell morphology over development has emerged as a focus of interest in developmental biology, CShaperApp is anticipated to pave the way for those studies by accelerating the high‐throughput generation of systems‐level quantitative data collection. The software can be freely downloaded from the website of Github (cao13jf/CShaperApp) and is executable on Windows, macOS, and Linux operating systems.

ROSPaCe: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical System and IoT Networks

Most of the intrusion detection datasets to research machine learning-based intrusion detection systems (IDSs) are devoted to cyber-only systems, and they typically collect data from one architectural layer. Often the attacks are generated in dedicated attack sessions, without reproducing the realistic alternation and overlap of normal and attack actions. We present a dataset for intrusion detection by performing penetration testing on an embedded cyber-physical system built over Robot Operating System 2 (ROS2). Features are monitored from three architectural layers: the Linux operating system, the network, and the ROS2 services. The dataset is structured as a time series and describes the expected behavior of the system and its response to ROS2-specific attacks: it repeatedly alternates periods of attack-free operation with periods when a specific attack is being performed. This allows measuring the time to detect an attacker and the number of malicious activities performed before detection. Also, it allows training an intrusion detector to minimize both, by taking advantage of the numerous alternating periods of normal and attack operations.

Rational load balancing in collaborated cloud computing environments *

Abstract Optimum efficiency and fault tolerance are the most demanding and challenging issues in the domain of performance and reliability management in cloud computing environments. Optimized resource utilization is a key aspect for yielding efficiency in cloud platforms. Workload balancing through resource sharing is one of the key solutions for attaining performance in cloud environments. In addition, multiple cloud environments join hands to offer performance and fault tolerance through resource sharing. We provide a better and cloud-instances’ priority-based efficient load balancing method for collaborative cloud platforms. The recommended efficient load balancing method shortens the waiting timespan and overcomes the starvation problem of low priority instances in intercloud environments. A functional prototype of the recommended load balancing method was deployed on a physical cloud infrastructure which was setup with the OpenStack cloud software on the Fedora Linux operating system. The pilot project execution findings exhibit a reduction in the timespan borne by instances for executing load balancing. This technique is useful for attaining fault tolerance and efficient resource utilization in intracloud and intercloud environments.