Linux System Research Articles

A3C System: One-Stop Automated Encrypted Traffic Labeled Sample Collection, Construction and Correlation in Multi-Systems

Encrypted traffic classification can essentially support network QoS (Quality of Service) and user QoE (Quality of Experience). However, as a typical supervised learning problem, it requires sufficiently labeled samples, which should be frequently updated. The current gateway-based labeled sample acquisition methods can only be carried out under TLS traffic. It relies on the Server Name Indication, a confused optional field that can be tampered with. The current end-based methods carried out manually or automatically have low efficiency and lack sample integrity, category purity, and label authenticity. In addition, they may have colossal packet loss and violate device security and user privacy. To solve these problems, we propose a one-stop automated encrypted traffic labeled sample collection, construction, and correlation system, A3C. First, we carry out the automated process-isolated traffic collection and labeled sample construction in the mixed application scenario, which can be used on Windows, Linux, and Android systems. Then, we propose the Segmented Entropy Distribution Capsule Neural Network (SED-CapsNet) to validate the encryption of the collected samples. We also propose optional authenticity validation and context flow correlation methods. Experimental results show that the system can effectively achieve one-stop encrypted traffic labeled dataset acquisition. It is superior to the existing methods.

ERDAC: Efficient and Reliable Remote Direct Access and Control for Embedded Systems

Emerging embedded systems, such as autonomous vehicles, demand highly efficient remote data transfer, whereas existing networking hardware and protocols cause high communication latency and CPU consumption. In this article, we propose embedded RDAC (eRDAC), an efficient and reliable remote direct access and control solution for embedded systems. The proposed remote access controller in eRDAC has a two-layer protocol offload engine that employs the command/response protocol on UDP to ensure the data reliability and security, and a multichannel DMA controller with configurable priority to improve the efficiency. Besides, a reusable hardware Ethernet MAC is implemented to support not only remote access commands but also standard Ethernet communication. We implement eRDAC on FPGA and the corresponding software in the Linux system. Experimental results show that eRDAC can reduce the latency of remote I/O reading/writing by 74.3%/74.9% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.76\times /3.98\times $ </tex-math></inline-formula> performance improvement) and reduce the latency of remote memory reading and writing with 1024B by 54.2% compared to the socket-based communication. Meanwhile, eRDAC can cut off the consumption of the remote processor and achieve 0.250mJ/Mb energy consumption with only 25-mW power.

SpotFuzzer: Static Instrument and Fuzzing Windows COTs

The security research on Windows has received little attention in the academic circle. Most of the new methods are usually designed for the Linux system and are difficult to transplant to Windows. Fuzzing for Windows programs always suffers from its closed source. Therefore, we need to find an appropriate way to achieve feedback from Windows programs. To our knowledge, there are no stable and scalable static instrumentation tools for Windows yet, and dynamic tools, such as DynamoRIO, have been criticized for their performance. To make matters worse, dynamic instrumentation tools have very limited usage scenarios and are impotent for many system services or large commercial software. In this paper, we proposed SpotInstr, a novel static tool for instrumenting Windows binaries. It is lightweight and can instrument most Windows PE programs in a very short time. At the same time, SpotInstr provides a set of filters, which can be used to select instrumentation points or restrict the target regions. Based on these filters, we propose a novel selective instrumentation method which can speed up both instrumentation and fuzzing. After that, we design a system called SpotFuzzer, which leverages the ability of SpotInstr and can fuzz most Windows binaries. We tested SpotInstr and SpotFuzzer in multiple dimensions to show their superior performance and stability.

Analysis and optimization of Dual Parallel Partition Sorting with OpenMP

PurposeSorting is a very important algorithm to solve problems in computer science. The most well-known divide and conquer sorting algorithm is quicksort. It starts with dividing the data into subarrays and finally sorting them.Design/methodology/approachIn this paper, the algorithm named Dual Parallel Partition Sorting (DPPSort) is analyzed and optimized. It consists of a partitioning algorithm named Dual Parallel Partition (DPPartition). The DPPartition is analyzed and optimized in this paper and sorted with standard sorting functions named qsort and STLSort which are quicksort, and introsort algorithms, respectively. This algorithm is run on any shared memory/multicore systems. OpenMP library which supports multiprocessing programming is developed to be compatible with C/C++ standard library function. The authors’ algorithm recursively divides an unsorted array into two halves equally in parallel with Lomuto's partitioning and merge without compare-and-swap instructions. Then, qsort/STLSort is executed in parallel while the subarray is smaller than the sorting cutoff.FindingsIn the authors’ experiments, the 4-core Intel i7-6770 with Ubuntu Linux system is implemented. DPPSort is faster than qsort and STLSort up to 6.82× and 5.88× on Uint64 random distributions, respectively.Originality/valueThe authors can improve the performance of the parallel sorting algorithm by reducing the compare-and-swap instructions in the algorithm. This concept can be used to develop related problems to increase speedup of algorithms.

CARE 2.0: reducing false-positive sequencing error corrections using machine learning

BackgroundNext-generation sequencing pipelines often perform error correction as a preprocessing step to obtain cleaned input data. State-of-the-art error correction programs are able to reliably detect and correct the majority of sequencing errors. However, they also introduce new errors by making false-positive corrections. These correction mistakes can have negative impact on downstream analysis, such as k-mer statistics, de-novo assembly, and variant calling. This motivates the need for more precise error correction tools.ResultsWe present CARE 2.0, a context-aware read error correction tool based on multiple sequence alignment targeting Illumina datasets. In addition to a number of newly introduced optimizations its most significant change is the replacement of CARE 1.0’s hand-crafted correction conditions with a novel classifier based on random decision forests trained on Illumina data. This results in up to two orders-of-magnitude fewer false-positive corrections compared to other state-of-the-art error correction software. At the same time, CARE 2.0 is able to achieve high numbers of true-positive corrections comparable to its competitors. On a simulated full human dataset with 914M reads CARE 2.0 generates only 1.2M false positives (FPs) (and 801.4M true positives (TPs)) at a highly competitive runtime while the best corrections achieved by other state-of-the-art tools contain at least 3.9M FPs and at most 814.5M TPs. Better de-novo assembly and improved k-mer analysis show the applicability of CARE 2.0 to real-world data.ConclusionFalse-positive corrections can negatively influence down-stream analysis. The precision of CARE 2.0 greatly reduces the number of those corrections compared to other state-of-the-art programs including BFC, Karect, Musket, Bcool, SGA, and Lighter. Thus, higher-quality datasets are produced which improve k-mer analysis and de-novo assembly in real-world datasets which demonstrates the applicability of machine learning techniques in the context of sequencing read error correction. CARE 2.0 is written in C++/CUDA for Linux systems and can be run on the CPU as well as on CUDA-enabled GPUs. It is available at https://github.com/fkallen/CARE.

System design and research of multi-channel electroacupuncture instrument

This paper introduces the development history, working principle, curative effect under different output waveforms and the hardware and software design of the system. The proportional constant current output circuit is analyzed in detail. A system structure of electroacupuncture instrument based on modular design is proposed. The system structure mainly includes multi-channel electroacupuncture control module and display control module, which are connected through CAN bus. Multi-channel electroneedle control module adopts embedded processor and real-time operating system design, display control module is application level processor, running embedded Linux system design. The system has reasonable structure design and high expansibility, which is convenient for further research.

A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots

To proactively defend computer systems against cyber-attacks, a honeypot system—purposely designed to be prone to attacks—is commonly used to detect attacks, discover new vulnerabilities, exploits or malware before they actually do real damage to real systems. Its usefulness lies in being able to operate without being identified as a trap by adversaries; otherwise, its values are significantly reduced. A honeypot is commonly classified by the degree of interactions that they provide to the attacker: low, medium and high-interaction honeypots. However, these systems have some shortcomings of their own. First, the low and medium-interaction honeypots can be easily detected due to their limited and simulated functions of a system. Second, the usage of real systems in high-interaction honeypots has a high risk of security being compromised due to its unlimited functions. To address these problems, we developed Asgard an adaptive self-guarded honeypot, which leverages reinforcement learning to learn and record attacker’s tools and behaviour while protecting itself from being deeply compromised. In this paper, we compare Asgard and its variant Midgard with two conventional SSH honeypots: Cowrie and a real Linux system. The goal of the paper is (1) to demonstrate the effectiveness of the adaptive honeypot that can learn to compromise between collecting attack data and keeping the honeypot safe, and (2) the benefit of coupling of the environment state and the action in reinforcement learning to define the reward function to effectively learn its objectives. The experimental results show that Asgard could collect higher-quality attacker data compared to Cowrie while evading the detection and could also protect the system for as long as it can through blocking or substituting the malicious programs and some other commands, which is the major problem of the high-interaction honeypot.

Educational Psychology Analysis Method for Extracting Students' Facial Information Based on Image Big Data.

At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students' facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student's academic mood, they can intervene on the students' positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students' positive academic emotions, in order to improve students' positive academic emotions, which will improve students' academic performance and teaching.

Findzx: an automated pipeline for detecting and visualising sex chromosomes using whole-genome sequencing data

BackgroundSex chromosomes have evolved numerous times, as revealed by recent genomic studies. However, large gaps in our knowledge of sex chromosome diversity across the tree of life remain. Filling these gaps, through the study of novel species, is crucial for improved understanding of why and how sex chromosomes evolve. Characterization of sex chromosomes in already well-studied organisms is also important to avoid misinterpretations of population genomic patterns caused by undetected sex chromosome variation.ResultsHere we present findZX, an automated Snakemake-based computational pipeline for detecting and visualizing sex chromosomes through differences in genome coverage and heterozygosity between any number of males and females. A main feature of the pipeline is the option to perform a genome coordinate liftover to a reference genome of another species. This allows users to inspect sex-linked regions over larger contiguous chromosome regions, while also providing important between-species synteny information. To demonstrate its effectiveness, we applied findZX to publicly available genomic data from species belonging to widely different taxonomic groups (mammals, birds, reptiles, and fish), with sex chromosome systems of different ages, sizes, and levels of differentiation. We also demonstrate that the liftover method is robust over large phylogenetic distances (> 80 million years of evolution).ConclusionsWith findZX we provide an easy-to-use and highly effective tool for identification of sex chromosomes. The pipeline is compatible with both Linux and MacOS systems, and scalable to suit different computational platforms.

Banking System Using Blockchain

Blockchain is a distributed ledger technology which aids in recording transaction and tracking capital in any business network. The main feature of blockchain is transparency. Our project is going to help these banks because we will be implementing blockchain ledger for creating a banking system in Linux. Blockchain will boost trust, security, translucence, and the traceability of data shared across a banking system and will be cost saving with new efficiencies. Because it provides rapid, shareable, and entirely clear information recorded on an immutable ledger that can only be viewed by authorised network users, blockchain is the ideal option for distributing data. This network can locate orders, capital, users, creations, and much more, and because participants share only one view of transparency, you can see all the details of a capital transaction from beginning to end, providing you with a greater sense of trust as well as new efficiencies and opportunities. Banking systems are transferring from their older methods to more recent ledger methods. Banking sector has started experimenting with blockchain ledger by copying current asset transactions on the blockchain. This enables the banking sector to have some space for the effectiveness of the blockchain solution. In infrastructure terms, Blockchain is software that is open source designed to support the transfer of digital capital among market participants in real time. Using any chosen blockchain APIs, one may exhibit big decrease in asset transfer costs and timelines. Today's banks are developing and operating their business models with the help of a trusted third-party service provider. As a result, Blockchain technology will be able to reconstruct the banking model since it can handle transaction records via a peer-to-peer network between trading partners without the need for a trusted third party.

Research And Design of Intelligent Home Potting System Technology By Wireless Sensor Networks

People often put potted plants at home in life, but they neglect to cultivate them due to busy work and study, or the lack of expertise related to potted plant cultivation leads to rotten and withered plants. An Exynos 4412 development board with a Linux system and multiple information acquisition sensors is designed using time series for this life background. The system is an intelligent home potted plant system with a Linux-based Exynos 4412 development board and multiple information collection sensors, using a time-series prediction model, deep convolutional neural network model, message queue telemetry transmission protocol, cloud storage and other technologies. The system realizes automatic scientific irrigation, soil fertility monitoring, and pest and disease warning for potted plants, thus ensuring healthy and robust growth of potted plants, and has a broad market space.

Adaptive Non-Intrusive Load Monitoring Based on Feature Fusion

Intelligent power management system is an important part of the smart grid, in which the non-intrusive load monitoring technology is one of the key technologies. However, most of the load monitoring methods regard this task as a multi-classification problem, thus it is not effective to identify the unknown loads that did not participate in training. In this paper, an adaptive non-intrusive load monitoring method based on feature fusion has been proposed which utilizes both information of harmonic current feature and voltage-current (V-I) trajectory feature. The harmonic current feature is obtained from the high-frequency load sampling data through Fast Fourier Transform (FFT). At the same time, the V-I trajectory feature is obtained by using the pre-trained convolutional autoencoder neural network that was trained for feature extraction on a public dataset. After the feature extraction, the similarity between these two feature vectors and the available feature vectors in the database is calculated by TOPSIS algorithm. Then the load monitoring can be carried out according to the similarity. When the maximum similarity is greater or equal than the set threshold, the load is considered to be one of the existing loads in the database, otherwise, it will be considered as a new type of load, and the load feature database will be updated. The autoencoder model is trained by using the V-I trajectory from the BLUED dataset and the PLAID dataset is used to verify the identification accuracy of the proposed algorithm by comparison with different algorithms. On the PLAID dataset, the identification accuracy can reach above 97&#x0025;. Finally, on the embedded Linux system with STM32MP1 as the core, some household electrical appliances are used for validation in real house environment. The results show that the proposed method has improved the capability of load identification by using the complementarity of different features. It can be carried out in real time on embedded system and is able to identify the unknown load at the same time.

Research on Embedded Detection System Based on Intelligent Pressure Transmitter

Abstract A new development scheme of check equipment for recoil device is designed. In view of the existing reverse recoil device check there is a long time to prepare, data interpretation and difficult problems, the application of advanced touch technology and sensor technology, completed the embedded test system scheme based on intelligent pressure transmitter design, can quickly test reverse recoil device to check the key parameters, and be able to compare the interpretation of data in real-time, Shorten the inspection time of recoil device and improve the accuracy of test data. The simulation research of the scheme design system is carried out in the computer of LINUX system. Through the simulation, it is found that each system module can work correctly and normally, with higher generality and artificial intelligence.

Comparison of selected configuration management systems in Linux

The purpose of this article is to compare selected configuration management systems on Linux. These systems had to meet the condition of integration with Linux. The two of the most popular systems were chosen which are Puppet and Ansible. The comparison was based on a simple system configuration using these two systems in several aspects: installation, file and folder management, package management, user management, configuration of the Apache server and Firewall. Through this comparison, it was possible to determine which system is more suitable for a novice Linux system administrator.

ASD: ARP Spoofing Detector Using OpenWrt

The address resolution protocol (ARP) is one of the most important communication protocols in a local area network (LAN). However, since there is no authentication procedure, the ARP is vulnerable to cyberattack such as ARP spoofing. Since ARP spoofing can be connected to critical attacks, including a man-in-the-middle (MITM) attack, detecting ARP spoofing initially without returning false-positive alarms is important. In general, however, existing works for ARP spoofing are unable to distinguish between ARP spoofing and connections from virtual machine (VM) guests, which results in false-positive alarms. In this article, we propose an access point-based ARP Spoofing Detector (ASD) that can detect ARP spoofing attacks without returning a false-positive rate. Our proposed system distinguishes between ARP spoofing and connections from VM guests using three information tables, AssocList, ARP cache table, and DHCP table, which are commonly managed by the access point based on a Linux system. We evaluated the performance of ASD on ARP spoofing attack experiments.

MAPPER: Managing Application Performance via Parallel Efficiency Regulation

State-of-the-art systems, whether in servers or desktops, provide ample computational and storage resources to allow multiple simultaneously executing potentially parallel applications. However, performance tends to be unpredictable, being a function of algorithmic design, resource allocation choices, and hardware resource limitations. In this article, we introduce MAPPER, a manager of application performance via parallel efficiency regulation. MAPPER uses a privileged daemon to monitor (using hardware performance counters) and coordinate all participating applications by making two coupled decisions: the degree of parallelism to allow each application to improve system efficiency while guaranteeing quality of service (QoS), and which specific CPU cores to schedule applications on. The QoS metric may be chosen by the application and could be in terms of execution time, throughput, or tail latency, relative to the maximum performance achievable on the machine. We demonstrate that using a normalized parallel efficiency metric allows comparison across and cooperation among applications to guarantee their required QoS. While MAPPER may be used without application or runtime modification, use of a simple interface to communicate application-level knowledge improves MAPPER’s efficacy. Using a QoS guarantee of 85% of the IPC achieved with a fair share of resources on the machine, MAPPER achieves up to 3.3 \( \times \) speedup relative to unmodified Linux and runtime systems, with an average improvement of 17% in our test cases. At the same time, MAPPER violates QoS for only 2% of the applications (compared to 23% for Linux), while placing much tighter bounds on the worst case. MAPPER relieves hardware bottlenecks via task-to-CPU placement and allocates more CPU contexts to applications that exhibit higher parallel efficiency while guaranteeing QoS, resulting in both individual application performance predictability and overall system efficiency.

Implementation of a Monitoring System for Pregnancy by Tele-Ultrasound in an African Context (Burkina Faso).

Prenatal ultrasound is a radiological examination that allows optimal follow-up of pregnancies. However, its implementation remains limited in poor countries due to a lack of equipment and trained health workers, such as in Burkina Faso. The aim of this work is to set up an ultrasound tele-expertise system. To achieve this objective, we mobilized human, material and IT resources. The design of the tele-expertise application was based on a generic open source software called "MedShakeEHR" that we have adapted to our context. The application runs in a network on a Linux system. It enables ultrasound data exchange and sharing with a remote expert for interpretation using the DICOM protocol. This device thus offers the possibility to pregnant women to carry out their prenatal ultrasound locally. It also allows the constitution of prenatal ultrasound database according security and confidentiality standards.

Sustainable packaging of quantum chemistry software with the Nix package manager

AbstractThe installation of quantum chemistry software packages is commonly done manually and can be a time‐consuming and complicated process. An update of the underlying Linux system requires a reinstallation in many cases and can quietly break software installed on the system. In this paper, we present an approach that allows for an easy installation of quantum chemistry software packages, which is also independent of operating system updates. The use of the Nix package manager allows building software in a reproducible manner, which allows for a reconstruction of the software for later reproduction of scientific results. The build recipes that are provided can be readily used by anyone to avoid complex installation procedures.

Community Environmental Detection and Community Outdoor Space Health Promotion Based on Cloud Resource Scheduling Mechanism.

In the context of rapid economic growth, people's living standards are improving day by day, and China's urbanization process is also gradually accelerating. Urban renewal is a very complex project. The renewal and expansion of most cities in the country usually involve demolishing and rebuilding. This way of urban renewal only pays attention to the renewal efficiency of cities and towns, and ignores the health needs of residents, which will make community residents lack a sense of security, pleasure, comfort, and belonging to the community, and greatly reduce the quality of life and physical fitness of community residents. Building a healthy city is an important goal of today's society, and community is the basic unit of a city, so it is urgent to build a health-promoting community. We should pay attention to and study the social environment, the external space environment of residential areas, and the interaction between them, so as to make develop them in a healthy direction and create a healthy and positive community. Therefore, it is an urgent task to improve the community residents' attention to the above problems and build community outdoor space, which has very important practical significance. In this paper, we use the cloud resource scheduling mechanism and the Internet of things to study the healthy role of community outdoor space planning. Through the transplantation of embedded Linux system, the development of communication interface and the external acquisition sensing device, we collect the information of working status and running status, and transmit it to the database built in the cloud through the 4G module on the motherboard. Then, HTML + java script + CSS + JSON + Ajax technology is used to write the display interface and logical relationship, so that the data can be displayed on the mobile terminal and PC, which can provide a useful technical exploration for outdoor space planning. The community Internet of Things technology realizes the intelligent community environment detection, and the community cloud resource scheduling mechanism combined with the community Internet of Things can obtain the residents' help information in time and allocate the community outdoor resources in real time, which can not only meet the health needs of community members but also be conducive to the sustainable development of a health-promoting community. In addition, the cloud resource scheduling mechanism and the Internet of things also provide new ideas for the study of the health promotion research of community outdoor space planning.

ThermalBleed: A Practical Thermal Side-Channel Attack

Modern OSs expose an interface for monitoring CPU temperature to unprivileged users for effective user decision-based thermal management. Due to the low sampling rate and resolution, thermal sensors have generally been restricted to the construction of covert channels. However, exposing the thermal interface to unprivileged users may be problematic, because the heat emission inside a CPU core is affected by program execution on the core; an attacker may be able to infer the secret information of the program by exploiting the thermal interface as a side-channel. In this paper, we extensively analyze digital thermal sensors in Intel CPUs and show that it is possible to implement a software-based thermal side-channel attack. Specifically, by analyzing some properties of the thermal sensors, we inferred that the thermal sensor makes it possible to distinguish between a cache hit and a physical memory access in memory load operations. Based on the analysis results, we implement ThermalBleed, a thermal side-channel attack that breaks kernel address space layout randomization (KASLR) in Linux systems. Moreover, by conducting an in-depth analysis, we identify useful hidden properties of the Intel thermal sensors. Our analysis establishes a stepping stone to build a more precise and effective thermal side-channel attack in the future. To the best of our knowledge, this is the first work that extends a thermal covert channel to a practical side-channel attack by exploring the properties of Intel digital thermal sensors.