Linux Platform Research Articles

Edge-Driven Linux Platform for Power Quality Solutions

In the modern landscape of data centers, ensuring optimal power quality and efficiency is paramount. This paper describes designing and implementing an edge-driven Linux platform for power quality solutions for real-time measurement, analysis, and conformity with IEC 61000-4-30, IEEE 519, and EN 50160 standards. It employs an AM6442xx processor and has a web interface with the backing of an efficient embedded web server, a NodeJS, and a Nginx server. It supports industrial communication protocols like Modbus TCP to simplify integration into existing systems. In addition, it involves risk assessment, threat modeling, and enhanced ability of the platform to withstand tests that threaten the confidentiality and integrity of data. Thus, the proposed solution increases the efficiency of data center operations and provides reliable power quality metering that meets current international standards.

Fine-Grained Hardware Profiling - Are You Using the Right Tools?

We consider the problem of fine-grained hardware profiling, i.e., profiling the hardware while the desired section of the program is executing. Although this requirement is frequently encountered in practice, its importance has not been emphasized in literature so far. In this work, we compare and validate three tools for performing fine-grained profiling on Linux platforms - perf, PAPI, and a homegrown tool PMU-metrics. perf has been used in the past for fine-grained profiling in an erroneous manner, producing inaccurate metrics as a result. On the other hand, PAPI and PMU-metrics produce accurate metrics for profiling at thems-scale, while PMUmetrics enables profiling even at the µs-scale. Thus, we hope that our analysis will help systems practitioners choose the right tool for performing fine-grained profiling at different time scales.

A Novel Static Analysis Approach Using System Calls for Linux IoT Malware Detection

The proliferation of Internet of Things (IoT) devices on Linux platforms has heightened concerns regarding vulnerability to malware attacks. This paper introduces a novel approach to investigating the behavior of Linux IoT malware by examining syscalls and library syscall wrappers extracted through static analysis of binaries, as opposed to the conventional method of using dynamic analysis for syscall extraction. We rank and categorize Linux system calls based on their security significance, focusing on understanding malware intent without execution. Feature analysis of the assigned syscall categories and risk ranking is conducted with statistical tests to validate their effectiveness and reliability in differentiating between malware and benign binaries. Our findings demonstrate that potential threats can be reliably identified with an F1 score of 96.86%, solely by analyzing syscalls and library syscall wrappers. This method can augment traditional static analysis, providing an effective preemptive measure to enhance Linux malware analysis. This research highlights the importance of static analysis in strengthening IoT systems against emerging malware threats.

Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform

This paper presents our innovative approach in the field of Internet of Things (IoT). Our focus was on enhancing the research environment at Cheng Shiu University's laboratory through the creation of a dynamic network monitoring setup and lighting control system. Our aim was to simplify equipment management and promote energy efficiency. The core of our development is the Raspberry Pi 4B embedded Linux platform, which serves as a host for both a Message Queuing Telemetry Transport (MQTT) broker and a Node-RED server. Python programs, developed in PyCharm IDE, utilize multiple threads for efficient data exchange via Modbus TCP and MQTT protocols, facilitating seamless interaction between PLCs and MQTT broker servers. The Node-RED IoT development tool was instrumental in crafting a cross-platform Human-Machine Interface (HMI) web server with MQTT Publish/Subscribe capabilities, ensuring smooth communication with the MQTT broker server. By configuring the Apache HTTP server to direct to the Node-RED web directory, we can effectively monitor laboratory conditions and manage the lighting system. The experimental results validate the cost-effectiveness of our approach in transforming traditional control systems into a web-based supervisory control system, thereby enhancing the overall functionality of existing equipment. This project underscores our commitment to advancing IoT solutions in practical settings.

Fabrication and Development of the Embedded Linux Based on the ARM System

Abstract With the development of the embedded system and improvement of the requirements from customers, embedded GUI which was used to communicate between users and embedded system became to be a key in researching the embedded system. This article introduced what is embedded system and what is embedded GUI. It described the porting of an embedded GUI on ARM Linux platform, including the establishment of compile environment, configuring and modifying of compile options and setting of runtime parameters. Above all, this article introduces the development of Embedded Linux GUI, describes the GUI development environment of Qt /Embedded and Qt, one procedure of the app location development based on Qt/Embedded is described in detail. This system adopts the B/S mode and uses the ARM11 development board with S3C6410 as the core. Sensors are used to obtain information, and the obtained data is judged and analyzed. When the preset conditions are not met, an alarm function is triggered. At the same time, data transmission is carried out through the Zigbee wireless module, and the receiving end analyzes and displays the received data. Each device is connected to the Zigbee module, which distinguishes it by assigning different network addresses and constantly transmits the status of the monitoring area to the receiving end. The receiving end distinguishes the monitoring status of different locations based on the different network addresses, increasing mobility.

In silico identification of approved drugs for potential prophylactic uses against SARS-CoV-2 infection

The outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) responsible for Coronavirus disease 2019 (COVID-19) constitute a major public health threat and economic burden. Despite the multifold research all over the world. COVID-19 has remained without a treatment. Hence, the urgent need to repurpose the FDA-approved drugs through in silico computational technique to identify inhibitors of SARS-CoV-2. Bioinformatics techniques were used to screen 2015 FDA- approved drugs against SARS-CoV-2 proteins and associated human protein, angiotensin-converting enzyme 2 (ACE2) to identify drug for new indication. The ligands were downloaded from Selleckchem drug data bank in SDF format. Open Babel was used to separate the ligands into their compounds and prepared for docking simulation using shell script and saved as PDBQT. Experimental crystal structures of the receptors (ACE2 and Spike proteins) were retrieved from Protein Data Bank (PDB), Autodock tools was used to prepare the proteins and saved as PDBQT files. Docking protocols were validated, molecular docking simulations were performed on a Linux platform using AutoDock Vina in quadruplicate. The binding energies (BEs) kcal/mol were calculated and expressed as mean ± standard deviation. Discovery studio was used for visualization. Density Functional Theory (DFT) was used to determine the predictive maximal inhibitory concentration (pIC50) of best hits. Ninety-one (91) new hits were identified for prophylaxis. Four (4) compounds were subjected to DFT based IC50 calculation with molnupiravir as a reference compound. Significantly, calculated IC50 of molnupiravir in Vero cells and Calu-3 were 0.299 and 0.008µM respectively while the experimental IC50 in Vero cells and Calu-3 as reported in literature were 0.3 and 0.08 µM respectively. The newly identified hits are promising therapeutic agents and there is no significant difference between the experimental IC50 of molnupiravir and the calculated IC50.

RnaCrosslinkOO: an object-oriented R package for the analysis of RNA structural data generated by RNA crosslinking experiments.

RNA (ribonucleic acid) molecules have secondary and tertiary structures in vivo which play a crucial role in cellular processes such as the regulation of gene expression, RNA processing and localization. The ability to investigate these structures will enhance our understanding of their function and contribute to the diagnosis and treatment of diseases caused by RNA dysregulation. However, there are no mature pipelines or packages for processing and analyzing complex in vivo RNA structural data. Here, we present rnaCrosslinkOO (RNA Crosslink Object-Oriented), a novel software package for the comprehensive analysis of data derived from the COMRADES (Crosslinking of Matched RNA and Deep Sequencing) method. rnaCrosslinkOO offers a comprehensive pipeline from raw sequencing reads to the identification and comparison of RNA structural features. It includes read processing and alignment, clustering of duplexes, data exploration, folding and comparisons of RNA structures. rnaCrosslinkOO also enables comparisons between conditions, the identification of inter-RNA interactions, and the incorporation of reactivity data to improve structure prediction. rnaCrosslinkOO is freely available to noncommercial users and implemented in R, with the source code and documentation accessible at https://CRAN.R-project.org/package=rnaCrosslinkOO. The software is supported on Linux, macOS, and Windows platforms.

IOT BASED ATTENDANCE SYSTEM USING FINGERPRINT SENSOR

To provide reliable, time-saving and automatic class attendance system, the concept of Internet of Things (IoT) based class attendance monitoring system using embedded Linux platform is presented in this project . The study is focused on the design and implementation of fingerprint detection and recognition system using Arduino Uno. The system takes fingerprint of students, and analyzes, detects and recognizes fingerprint using sensor .After collecting processing data, the system generates a final attendance record and uploads it in a cloud server. The cloud server has been implemented using python based web framework. The record can be accessed remotely from a user-friendly, web application using the Internet. Finally, the system is also capable of sending an email notification with the final record to the teachers and students in a specific time. Tests and performance analysis were done to verify the efficiency of this system

A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms

Malware is one of the most common and severe cyber threats today. Malware infects millions of devices and can perform several malicious activities including compromising sensitive data, encrypting data, crippling system performance, and many more. Hence, malware detection is crucial to protect our computers and mobile devices from malware attacks. Recently, Deep Learning (DL) has emerged as one of the promising technologies for detecting malware. The recent high production of malware variants against desktop and mobile platforms makes DL algorithms powerful approaches for building scalable and advanced malware detection models as they can handle big datasets. This work explores current deep learning technologies for detecting malware attacks on Windows, Linux, and Android platforms. Specifically, we present different categories of DL algorithms, network optimizers, and regularization methods. Different loss functions, activation functions, and frameworks for implementing DL models are discussed. We also present feature extraction approaches and a review of DL-based models for detecting malware attacks on the above platforms. Furthermore, this work presents major research issues on DL-based malware detection including future research directions to further advance knowledge and research in this field.

Sensitivity of Different Physical Schemes in WRF Model of a Rainfall Event in Baghdad Station

The Weather Research and Forecasting model (WRF) offers a number of physical options that let users modify it to different scales, regions, and applications. The aim of this study is to test the sensitivity of different physics schemes in the WRF model for rainfall events over Iraq. In this study, six different physics configurations of the climate version of WRF were evaluated for simulation of a rainfall event in Iraq. Possible combinations among two Planetary Boundary Layers (PBL), three Cumulus (CUM) and two Microphysics (MIC) schemes were tested. The study area is the region surrounded by the longitudes 35o E-55o E and latitudes 290o N–38o N, which typically includes the Iraq region. The WRF model is installed on a Linux platform with a 10 km grid size in the zonal and meridional directions. For the six different simulations and the process of choosing the best performing configuration for the Iraq region, the model outputs tested for a single grid point (Baghdad station) of the atmospheric parameters (temperature, pressure and total precipitation) with modeled data and ECMWF. Model outputs using statistical methods: Bias Error (BE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results show All the simulations predict rainfall with values close to the actual but it was discovered that the cloud microphysics setup had the greatest impact on temperature biases, whereas the cumulus parameterization setup has the greatest impact on precipitation.

Musical Projects in Guitar Pro 7.5 Sequencer

The purpose of the article is to study the process of creating musical projects in the GUITAR PRO 7.5 sequencer. Research methodology. The article uses a systematic approach, as well as methods of analysis and synthesis, comparison and generalisation. The scientific novelty lies in the identification and development of innovative methods of working in the GUITAR PRO 7.5 sequencer, which allows creating musical compositions from scratch using modern software tools, as well as the possibilities of using music information technologies in the further artistic practice of musicians, sound producers, sound engineers, producers and in professional music education. Conclusions. This software product is a powerful music editor that allows one to create original scores at a professional level for subsequent editing. The programme presents many necessary tools with which the user can work with a variety of musical notation symbols and a wide range of sound dynamics and tempo control, which allows you to create samples of sounding musical pieces and their phonograms. It has a powerful built-in MIDI editor, chord builder, player, metronome, and other useful tools for musicians. Guitar Pro 7.5 can be run on Windows, Linux, and Mac OS platforms. It is widely used by musicians, composers, arrangers, sound engineers, sound designers, and producers. In addition, Guitar Pro 7.5 methods can be used for studying by students who are mastering the relevant specialties in higher educational institutions. Keywords: music notation editor, score, arrangement, sound, musician, composer, sound producer, sound engineer, producer, professional music education.

High Resolution Dynamical Downscaling of Air Temperature and Precipitation using WRF-ARW Model Over Iraq

The ability of the Weather Research and Forecasting (WRF-ARW) model to simulate precipitation variability throughout the Middle East, especially Iraq, is evaluated in this study. The Weather Research and Forecasting model (WRF v 4.2.1) was used. Using a two-way nesting technique and Mercator projection, the WRF model was set up with two nested grids with high regional resolution at 30 km (WRF30km) and 10 km (WRF10km) horizontal grid spacing. From the surface to 50 hPa, both domains have 41 vertical levels. The outer domain covers the Middle East region and the smaller one covers the Iraq region. The ARW supports horizontal nesting, allowing higher resolution over a region of interest, by introducing additional grids, which is available only for horizontal refinement. The nested grids are rectangular and are aligned with the parent (coarser) grid within which they are nested. To predict many atmospheric parameters WRF-ARW model was used based on the initial conditions taken from National Centre for Atmospheric Research (NCAR) records for the period from 23 to 25 of march 2019. The study area is essentially an area bounded by longitudes 35°-55° and latitudes 29°-38° typically including the area of Iraq. The WRF-ARW model was set up on a Linux platform with a 10 km grid size in the zonal and meridional directions. The aim of this study is to evaluate the two WRF tools dynamical downscaling and nesting) at high resolution (30 and 10 km), for simulating rainfall and temperature over Iraq.

Longer Term Time Dynamics of Bit Error Rates

n this paper an analysis is conducted to study the properties of indoor and outdoor wireless channel conditions over long time scales (several minutes). It shows that long term time dynamics exist in many different situations even when transmitters and receivers are stationary and the distance between them is only a few meters. We use software-defined radio (SDR) that implements radio communication functionalities in software. Transmissions have been achieved using the Universal Software Radio Peripheral (USRP), with the signal processing and time synchronization occurring in the GNU Radio environment on the LINUX (Ubuntu 14.04 LTS) platform. The work has been implemented in a typical school environment i.e., indoor conditions including a lab environment along with a hallway and outdoor conditions comprising a quadrangle environment. We used the benchmarking tool in the GNU Radio that defines and modifies modulation schemes, transmitter and receiver gains, operating frequencies, and other channel properties. Per-packet bit error rate is computed, visualized, and compared across multiple scenarios. We find a series of interesting time dynamics of the BER over several minutes that are not commonly studied in the literature.

ILIAD: a suite of automated Snakemake workflows for processing genomic data for downstream applications

BackgroundProcessing raw genomic data for downstream applications such as imputation, association studies, and modeling requires numerous third-party bioinformatics software tools. It is highly time-consuming and resource-intensive with computational demands and storage limitations that pose significant challenges that increase cost. The use of software tools independent of one another, in a disjointed stepwise fashion, increases the difficulty and sets forth higher error rates because of fragmented job executions in alignment, variant calling, and/or build conversion complications. As sequencing data availability grows, the ability for biologists to process it using stable, automated, and reproducible workflows is paramount as it significantly reduces the time to generate clean and reliable data.ResultsThe Iliad suite of genomic data workflows was developed to provide users with seamless file transitions from raw genomic data to a quality-controlled variant call format (VCF) file for downstream applications. Iliad benefits from the efficiency of the Snakemake best practices framework coupled with Singularity and Docker containers for repeatability, portability, and ease of installation. This feat is accomplished from the onset with download acquisitions of any raw data type (FASTQ, CRAM, IDAT) straight through to the generation of a clean merged data file that can combine any user-preferred datasets using robust programs such as BWA, Samtools, and BCFtools. Users can customize and direct their workflow with one straightforward configuration file. Iliad is compatible with Linux, MacOS, and Windows platforms and scalable from a local machine to a high-performance computing cluster.ConclusionIliad offers automated workflows with optimized time and resource management that are comparable to other workflows available but generates analysis-ready VCF files from the most common datatypes using a single command. The storage footprint challenge of genomic data is overcome by utilizing temporary intermediate files before the final VCF is generated. This file is ready for use in imputation, genome-wide association study (GWAS) pipelines, high-throughput population genetics studies, select gene candidate studies, and more. Iliad was developed to be portable, compatible, scalable, robust, and repeatable with a simplistic setup, so biologists that are less familiar with programming can manage their own big data with this open-source suite of workflows.

4th-Order-SENS: A Software Module for Efficient and Exact 4th-Order Sensitivity Analysis of Neutron Transport

This work presents a software module called 4th-Order-SENS, which enables the efficient computation of exactly obtained expressions for all sensitivities, up to and including the 4th order, of a functional of the particle flux (e.g., the leakage of particles out of a body) with respect to nuclear parameters (total, scattering, and fission cross sections; nu, chi, sources; and number densities) for systems modeled by the neutron transport equation. The 4th-Order-SENS module implements the nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Linear Systems (nth-CASAM-L), which is the only practically implementable methodology for obtaining the exact expressions of arbitrarily high-order sensitivities of model responses to model parameters, for response-coupled forward/adjoint large-scale linear systems. In addition to presenting the equations that are solved to obtain the 1st-order through 4th-order sensitivities, this work also describes the components of the module 4th-Order-SENS, including the user interface, input file, output files, and several independent code verification capabilities using symmetries and/or finite-difference formulas. The 4th-Order-SENS module is written in Python and Fortran and runs on Linux platforms. Several illustrative applications involving fixed-source problems in one-dimensional spherical and slab geometries are also presented.

Linux security

Linux is an open source Operating system that is for the most part freely available to the public. Due to its customizability and cost to performance benefits Linux has quickly been adopted by users and companies alike for use in applications such as servers and workstation. As the spread of Linux continues it is important for security specialists to understand the platform and the security issues that affect the platform as well. This paper seeks to first educate the users on what the Linux platforms is and what it offers to the user or company. And it then will expand upon some common or recent vulnerabilities that Linux faces due to the way it functions. After explaining some exploits, the paper will then seek to explain some hardening solutions that are available on the platform.

Hydrogen bond energy estimation (H-BEE) in large molecular clusters: A Python program for quantum chemical investigations.

A procedure, derived from the fragmentation-based molecular tailoring approach (MTA), has been proposed and extensively applied by Deshmukh and Gadre for directly estimating the individual hydrogen bond (HB) energies and cooperativity contributions in molecular clusters. However, the manual fragmentation and high computational cost of correlated quantum chemical methods make the application of this method to large molecular clusters quite formidable. In this article, we report an in-house developed software for automated hydrogen bond energy estimation (H-BEE) in large molecular clusters. This user-friendly software is essentially written in Python and executed on a Linux platform with the Gaussian package at the backend. Two approximations to the MTA-based procedure, viz. the first spherical shell (SS1) and the Fragments-in-Fragments (Frags-in-Frags), enabling cost-effective, automated evaluation of HB energies and cooperativity contributions, are also implemented in this software. The software has been extensively tested on a variety of molecular clusters and is expected to be of immense use, especially in conjunction with correlated methods such as MP2, CCSD(T), and so forth.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html. Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

Real-time anomaly attack detection based on an improved variable length model

This paper uses a real-time anomaly attack detection based on improved variable length sequences and data mining. The method is mainly used for host-based intrusion detection systems on Linux or Unix platforms which use shell commands. The algorithm first generates a stream of command sequences with different lengths and subsumes them into a generic sequence library, de-duplicats and sortes shell command sequences. The shell command sequences are then stratified according to their weighted frequency of occurrence to define the state. Next, the behavioural patterns of normal users are mined to output the state stream and a Markov chain is constructed. Then, the state sequences are calculated based on a primary probability distribution and a transfer probability matrix. The System will check decision values of the short sequence stream. Finally, the decision values of the behavioural sequences are analysed to determine whether the current session user is behaving abnormally. The improved algorithm introduces the concept of multi-order frequencies and proposes a new separation mechanism. The extension module is integrated into the variable length model. By comparing the performance of the old and new separation mechanisms on the SEA dataset and the self-made dataset (SD), it is found that the improved model greatly improves the performance of the model and shortens the running time.

XFilter: An Extension of the Integrity Measurement Architecture Based on Fine-Grained Policies

The Integrity Measurement Architecture subsystem on the Linux platform is a critical security component in the kernel to ensure the integrity of the running system. However, the default Integrity Measurement Architecture policy mechanisms based on options such as file owner and FSMAGIC cannot achieve a file-level configuration. Although Integrity Measurement Architecture supports the Linux Security Module policy rules to be close to the goal of fine-grained configuration, it is not easy to be managed because the Linux Security Module was not originally designed for integrity measurement. Moreover, the Linux Security Module-based policy does not apply in some use cases considering the type of Mandatory Access Control tools chosen by users. This paper presents a new policy configuration option, named XFilter, that achieves a fine-grained policy configuration method. The XFilter includes two policy matching mechanisms, XLabel and XList, which share the same policy token created for XFilter exclusively. XLabel marks the files for measurement using a label in the file’s extended attribute (xattr). By contrast, XList stores the measurement information in a list of file paths. To simplify the deployment, an automatic configuration process is implemented for integrating into the package management system. The evaluation results suggest that both mechanisms satisfy the requirements of file-level IMA policy control and create a performance burden for system operation in the acceptable range. They also reveal a positive correlation between the increment of the system latency and the growth of the length of file paths list for the XList mechanism.