Remote Desktop Research Articles

An investigation of ransomware incidents in the maritime industry: Exploring the key risk factors

Ransomware is a subset of malicious cyberattacks that aim to hold an organization’s data or critical infrastructure at ransom, compromising or blocking access. If the attack is public or made public after the initial attack, it can also severely jeopardize an organization’s reputation. Given the direct and immediate impact ransomware attacks can have and the lack of in-depth sharing, additional research is needed to analyze ransomware incidents in order to understand the underlying causes of incidents in addition to the detection and prevention methods. In this paper, 22 public ransomware incidents within the marine industry have been investigated to determine their causal factors and commonalities. To investigate causal factors, DEMATEL (Decision Making Trial and Evaluation Laboratory) and a fuzzy set are used in order to enable an organization to better adhere to operational requirements and cyber risk management strategies to increase cyber resilience against ransomware incidents. The study’s findings highlight the fact that network layer cyber security mitigations, strategies for securely utilizing RDP (Remote Desktop Protocol) protocols, and investments in operating systems (OS) and software security are essential components of preventing future ransomware incidents. This study concludes by suggesting several suitable control and preventative measures to improve system safety.

Development of the architecture of the remote access system to educational laboratory equipment using automated solutions

The subject of the study is remote access to Raspberry Pi-based training equipment and remote desktop software. The purpose of the work is to analyze the means of remote control of equipment and develop the architecture of the system of remote access of students to laboratory equipment for the implementation of distance learning. The following tasks are solved in the article: the branches in which application of means of remote access is the most critical are analyzed, among which the special place is occupied by the educational branch; analysis of existing methods of organization of remote access to training equipment was carried out, advantages and disadvantages of each method were allocated; justification is being made for choosing a Raspberry Pi minicomputer as a hardware platform for building remote access systems; describes the architecture of a complex of technical solutions based on Raspberry Pi for remote control of educational laboratory equipment; implementation of applied system of temperature and humidity monitoring using developed approach is described. Research methods include comparative analysis of existing remote control technologies, prototyping and experimental testing of the developed system. The following results were obtained: the feasibility of using the remote desktop approach and AnyDesk software for organizing access was substantiated, a system based on the Raspberry Pi minicomputer for managing equipment and visualizing data was developed, an applied system for monitoring temperature and humidity using Raspberry Pi and AnyDesk was implemented. Conclusions: The proposed approach of using Raspberry Pi and remote desktop software allows students to effectively organize remote access to laboratory equipment from engineering disciplines during distance learning. This solution combines ease of implementation, interaction with real physical equipment and low cost compared to the use of industrial PLCs.

Implementation of File Sharing and Remote Desktop Connection (RDC) by Utilizing Local Area Network (LAN)

Implementation of File Sharing and Remote Desktop Connection (RDC) by Utilizing Local Area Network (LAN)

Optimalisasi Monitoring Traffic Komunikasi Data dan Penggunaan Resource Network Equipment Dalam Jaringan Lokal di IBI-K 1957

Monitoring a network and its device resources is very necessary in a data communications network, because based on the monitoring results, we can find out how much network resources are used, especially firewall and server devices. The monitoring carried out previously used an application that was accessed via remote desktop, where the application could only monitor data communication traffic used by users on the network. The use of remote desktops is very vulnerable from a network security perspective because it is often the target of exploration by cybercriminals or so-called black hackers. For this reason, monitoring of network devices is optimized using an open-source based application that can visualize the results of monitoring these devices on a web basis. By implementing this application, it can be concluded that it is able to fulfill the need for monitoring the use of resources and data communication traffic in the network so that if there is something unusual it can be immediately addressed by a System Administrator.

Beyond built density: From coarse to fine-grained analyses of emotional experiences in urban environments

The migration of individuals to urban centers in the last century has coincided with a rise in stress-related mental health issues among city dwellers compared to their rural counterparts. Neurourbanism, a burgeoning field, seeks to comprehend the determinants of individual well-being within the urban context. This study investigated the impact of urban density on subjective emotional experiences with a focus on how urban density factors are operationalized in this research field. Using a remote desktop protocol coupled with eye tracking technology, we presented participants with realistic urban stimuli from Berlin, Germany, to assess gaze patterns and subjective responses. Following a two-step analytical approach, we first used a two-factorial design based on low/high built density and absent/present greenery, reflecting the common approach to operationalizing urban density. Subsequently, semantic segmentation of the stimuli was performed, providing a more fine-grained, continuous quantification of density factors allowing for a comparison of this approach with the first categorical analysis. Lastly, individual gaze patterns were exploratively analyzed to predict the impact of directed attention to different classes of urban density on subjective experiences. Dichotomous classification replicated previous findings indicating that high built density was associated with more negative subjective ratings compared to low density, and images lacking greenery received more negative ratings than those with green spaces. Using a continuous quantification of urban density factors and adding additional object classes (cars, people, sky) led to different results compared to the dichotomous approach. Gaze patterns only partially echoed subjective ratings, suggesting urban density factors influence ratings via a general urban scene impression without the need for directed attention towards them. These findings underscore the multifaceted influence of density factors on emotional appraisal in urban spaces, emphasizing the importance of chosen methods. The study demonstrates that fine-grained analyses of urban density factors enhance our understanding of how the urban environment affects the well-being of city dwellers.

Desktop Stream

Abstract: The "Desktop Stream" approach is all about breaking barriers between Android devices and desktop computers. It lets you easily access your desktop from your Android, wherever you are. By combining Python for the desktop side and Java for Android, it brings mobility, accessibility, and multi-device collaboration to a whole new level. It uses screen sharing, allowing multiple Android devices to peek at your desktop at the same time. Thanks to a middleware server powered by Firebase and Ngrok, you can work together in real-time, share stuff, and get things done. This approach is perfect for today's world, where remote work and teamwork are crucial. It's built to be user-friendly, secure, and scalable. It turns your desktop into a super-portable resource that you can access from anywhere. "Desktop Stream" is not just a cool idea; it's a practical solution that makes remote desktop access a breeze. In this report, we'll dive into how it works, how we made it happen, and why it's a game-changer for remote collaboration.

Forensic Analysis of File Exfiltrations Using AnyDesk, TeamViewer and Chrome Remote Desktop

The use of remote desktop applications has increased greatly in recent years, mainly because of the generalization of telecommuting due to the COVID-19 pandemic. This process has been carried out in a very controlled manner in some companies, but in other organizations it has been introduced in a more anarchic way. The direct use of on-premises company computers and resources from the internet without the necessary protection mechanisms, including VPNs, has increased the risk of data exfiltration. Apart from other types of data exfiltration, there are cases in which employees transfer files using encrypted communications, consciously or unconsciously, producing a leak of information undetected by data loss prevention systems. In this paper we analyse the question of whether a forensic investigation may answer questions about data exfiltrations; questions such as those regarding the when, what and who (or to whom) and the use of application logs and other available tools. The answers to these questions may form the basis of solid digital evidence for legal purposes, though they may only deliver a partial response to said questions. Other complementary sources are necessary to build a complete answer and accurate digital evidence. Nevertheless, we have identified and analysed several use cases that may help to raise an early alarm that can offer warning about certain behaviours in encrypted traffic that may be detected via network monitoring.

Developing canonical ‘safe researcher’ training materials for trusted research environments

Social science and humanities research infrastructures allow the sharing and safe use of confidential, sensitive data for research via physical safe havens. In recent years there has been a shift towards virtual data enclaves or Remote Desktop systems that offer fewer physical controls. These controls need to be replaced with other safeguards, including mandatory ‘Safe Researcher’ training. This training aims to ensure that researchers are equipped with the knowledge required to use secure data safely. Developing training is resource intensive so canonical training materials are an economical approach to providing standardized, high-quality training. The Social Sciences and Humanities Open Cloud project deliverable ‘Training materials of workshop for secure data facility professionals ́ had two objectives. The first was the development of a set of canonical training materials that Trusted Research Environments (TREs) could use as a framework on which to build their own training course. The second objective was to hold a virtual workshop where the training materials could be demonstrated to a credible audience to gather feedback to inform the future development of the materials. We have now developed the canonical materials, building on the wealth of expertise and experience of UK-based TREs. These training materials were then demonstrated at a virtual, two-hour Stakeholder Workshop that we organized in September 2021. Following our demonstration of the materials, we facilitated small group discussions to gather vital feedback. The discussion groups formed a consensus that the materials were both comprehensive and clearly structured and would be a valuable resource to the TRE community.

Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications

A cloud-based interactive application (CIA) is an application running in the cloud with stringent interactivity requirements, such as remote desktop and cloud gaming. These services have experienced a surge in usage, primarily due to the adoption of new remote work practices during the pandemic and the emergence of entertainment schemes similar to cloud gaming platforms. Evaluating the quality of experience (QoE) in these applications requires specific metrics, including interactivity time, responsiveness, and the assessment of video- and audio-quality degradation. Despite existing studies that evaluate QoE and compare features of general cloud applications, systematic research into QoE for CIAs is lacking. Previous surveys often narrow their focus, overlooking a comprehensive assessment. They touch on QoE in broader contexts but fall short in detailed metric analysis. Some emphasise areas like mobile cloud computing, omitting CIA-specific nuances. This paper offers a comprehensive survey of QoE measurement techniques in CIAs, providing a taxonomy of input metrics, strategies, and evaluation architectures. State-of-the-art proposals are assessed, enabling a comparative analysis of their strengths and weaknesses and identifying future research directions.

Optimization of Internet of Things Remote Desktop Protocol for Low-Bandwidth Environments Using Convolutional Neural Networks.

This paper discusses optimizing desktop image quality and bandwidth consumption in remote IoT GUI desktop scenarios. Remote desktop tools, which are crucial for work efficiency, typically employ image compression techniques to manage bandwidth. Although JPEG is widely used for its efficiency in eliminating redundancy, it can introduce quality loss with increased compression. Recently, deep learning-based compression techniques have emerged, challenging traditional methods like JPEG. This study introduces an optimized RFB (Remote Frame Buffer) protocol based on a convolutional neural network (CNN) image compression algorithm, focusing on human visual perception in desktop image processing. The improved RFB protocol proposed in this paper, compared to the unoptimized RFB protocol, can save 30-80% of bandwidth consumption and enhances remote desktop image quality, as evidenced by improved PSNR and MS-SSIM values between the remote desktop image and the original image, thus providing superior desktop image transmission quality.

Comparative Quality of Service Analysis of VPN Protocols on IPv6

The increase in internet users has led to the depletion of IPv4 supplies and the increase in cyber attacks, one of which is data leakage incidents. Several innovations were created such as switching to IPv6 which provides more addresses and implementing VPN technology to secure internet communications. The solution using VPN is believed to secure the network from various types of attacks from outside the network by creating a tunnel with a certain encryption algorithm as a data exchange path. Some studies reveal that using VPN can cause delay which will affect QoS performance. Therefore, this research will be conducted to provide evidence as well as a comparison between the WireGuard and L2TP / IPSec VPN protocols on IPv6 based on Quality of Service parameters which include delay, jitter, packet loss, throughput, and MOS with three tests namely iPerf3, FTP, and remote desktop . The measurement is done by making a sample model with mikrotik and 6to4 tunnel. From the series of tests, it is known that the L2TP/IPSec protocol is better than the WireGuard protocol judging by the performance generated in tests with FTP and remote desktop. This discovery can be used by end users or other researchers to use VPN more objectively based on the technology.

Diagnostic system of the facility for studying superconducting quadrupoles

For the HED@FAIR project, NRC “Kurchatov Institute” — IHEP creates four quadrupole magnets with a unique combination of a large internal diameter of the superconducting winding and a high magnetic field gradient in the magnet aperture. To evaluate these superconducting magnets in different operation modes (cooling, maintaining operating parameters, heating, and transitioning magnet to the normal state), a cryogenic facility equipped with a diagnostic system was created. The main measured parameters of the unit were temperature, pressure, and vacuum in the equipment and pipelines, levels of liquid helium and liquid nitrogen, level of the gas holder, and helium flow rate. The control devices were represented by valves with electric and valves. For processing incoming signals from sensors and generation of control actions, the equipment of the domestic company OVEN was used, particularly analog input modules MV110, discrete output modules MU110, and interface converters alternating current four and direct current power supplies. The critical part of the diagnostics system, namely, the control of cryogenic valves, was provided through analog output modules NI 9219 and input modules NI 9212 (feedback) of the CompactRIO chassis. The program part of the installation for the study of superconducting quadruples is a mnemonic diagram with interactive graphic elements, physically located on the computer (operator console), and available in the read mode via the remote desktop protocol to users outside the installation. The mnemonic scheme allows the manual control of the plant’s valves and the automatic regulation of some indicators, depending on the settings assigned by the operator. The dynamics of the processes can be monitored with graphs on the right side of the interface. These graphs are displayed on a separate display at the operator’s console, whereas they are displayed on a unified screen for remote users.

Arbeitsmarktdaten: Gesetzesänderungen gefordert

Abstract In recent years, access to German research data on labour markets and social insurances has improved substantially. Nevertheless, reforms are urgently needed to catch up with other countries. Among the most important measures, the German government should legalise the merging of information from heterogeneous data sources for research, make more register available for research, harmonise the handling of data protection, and make remote desktop data accessible more broadly. Research data availability is essential for a functioning and informed democracy that aims to use its resources wisely and to learn from experience.

An Unobtrusive, Wireless and Wearable Single-Site Blood Pressure Monitor Based on an Armband Using Electrocardiography (ECG) and Reflectance Photoplethysmography (PPG) Signal Processing

Wearable medical devices (WMDs) for healthcare applications have become ubiquitous, allowing remote, at-home, and real-time chronic monitoring that have significantly decongested clinics. These WMDs permitted the monitoring of several physiological parameters, such as heart and respiration rates, SPO2, temperature, and energy expenditure during activities of daily living (ADLs) or fitness activities. While the measurement of these parameters has become common, full noninvasive, unobtrusive, and real-time blood pressure (BP) monitoring remains elusive owing to BP’s complex dynamics. To bring this into fruition, several works have been conducted combining different biosignals to indirectly extract BP by using PTT. Unlike previous works, we considered PTT variability by averaging it over discrete durations to account for BP variability for a more accurate estimation. PTTs were obtained using electrocardiograph (ECG) and reflective photoplethysmograph (rPPG) signals extracted by a wearable device attached to a single site on the upper arm. Our results show a significant correlation between average PTT and the BP measured using auscultation in a trial study. The developed system has potential for chronic, noninvasive, and cuff-less blood pressure monitors (BPMs) for localized and single-site implementations. Meanwhile, real-time data from the wearable device may be accessed via a remote desktop or a mobile phone application.

Design and implementation monitoring robotic system based on you only look once model using deep learning technique

<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in undesirable places to transmit live video with a moving camera and process it by the YOLOv5 model. Also, the robot system can receive images, videos, or YouTube links and process them with YOLOv5. Raspberry Pi is controlled remotely by connecting to the network through Wi-Fi locally or publicly using the internet with a remote desktop connection application. The results were very satisfactory and proved the high-performance efficiency of the robot.</span>

Organizational Cybersecurity Post The Pandemic: An Exploration of Remote Working Risks and Mitigation Strategies

The Covid-19 pandemic has forced organisations to embrace the largest remote workforce in history, yet this upheaval also brought an increasing number of cyber vulnerabilities to the fore. Organisations must remain committed to not leaving business processes, personal data, or vital infrastructure at risk, which has proved challenging for most. As remote working establishes itself as the new normal, criminals are seeking to capitalize on the widespread cybersecurity uncertainty, and succeeding. Private organisations and cybersecurity professionals must come together to establish robust solutions for home working cybersecurity. 
 This investigation explores several prevalent cyber risks (private networks, public hotspots, remote desktop protocol, authentication policies, virtual private network configuration and phishing attacks) across three key threat classifications of management, technical and human factors when remote working from the perspective of twenty industry experts. These findings offer key insights to emerging vulnerabilities, while also revealing defined strategies for organisations to help mitigate these challenges.

Transmitter Gelombang Frekuensi Radio (RF) FM Berbasis Raspberry Pi

In recent years, technology in the telecommunications sector has developed rapidly, especially with the global situation encouraging the use of long-distance communications. One of these developments is the use of the Raspberry Pi, abbreviated as Raspi, as a substitute for conventional computers for receiving and transmitting FM radio signals. Raspberry Pi is a mini and portable computer that can be used as an alternative to conventional computers in general, such as the use of teleconference media, offices, Big Data, and as a video player with high resolution. The problem in using a computer as an FM radio signal transceiver system is its complex and expensive infrastructure. When compared to the Raspberry Pi 3 with a price range of IDR 500,000 - IDR 1,000,000, a transceiver system using conventional computers requires a computer in general at a price far exceeding the said price. Using Raspi makes it easy to transmit FM radio signals by simply coding through the Raspi terminal. In this study, the authors propose a transmitter system using a Raspberry Pi 3B+ which is connected to a dipole antenna at a working frequency of 100 – 101 MHz. Raspi is operated using the SSH protocol for remote desktops. The operating system used is Raspberry Pi OS with the use of a MAC computer (MAC OS) as a remote desktop. The results show that the Raspberry Pi device can be used to transmit FM radio signals at a frequency of 100 - 101 MHz. The dipole antenna tested using VNA (Vector Network Analyzer) with a length of 71 cm and a linear form can transmit a signal with a VSWR of 1.08 - 1.95:1 at a working frequency of 94.9 - 103.06 MHz and a return loss of -28.1 dBm at a frequency of 98.98 MHz.

Journey through a virtual tunnel: Simulated motion and its effects on the experience of time

This paper examines the relationship between time and motion perception in virtual environments. Previous work has shown that the perception of motion can affect the perception of time. We developed a virtual environment that simulates motion in a tunnel and measured its effects on the estimation of the duration of time, the speed at which perceived time passes, and the illusion of self-motion, also known as vection. When large areas of the visual field move in the same direction, vection can occur; observers often perceive this as self-motion rather than motion of the environment. To generate different levels of vection and investigate its effects on time perception, we developed an abstract procedural tunnel generator. The generator can simulate different speeds and densities of tunnel sections (visibly distinguishable sections that form the virtual tunnel), as well as the degree of embodiment of the user avatar (with or without virtual hands). We exposed participants to various tunnel simulations with different durations, speeds, and densities in a remote desktop and a virtual reality (VR) laboratory study. Time passed subjectively faster under high-speed and high-density conditions in both studies. The experience of self-motion was also stronger under high-speed and high-density conditions. Both studies revealed a significant correlation between the perceived passage of time and perceived self-motion. Subjects in the virtual reality study reported a stronger self-motion experience, a faster perceived passage of time, and shorter time estimates than subjects in the desktop study. Our results suggest that a virtual tunnel simulation can manipulate time perception in virtual reality. We will explore these results for the development of virtual reality applications for therapeutic approaches in our future work. This could be particularly useful in treating disorders like depression, autism, and schizophrenia, which are known to be associated with distortions in time perception. For example, the tunnel could be therapeutically applied by resetting patients’ time perceptions by exposing them to the tunnel under different conditions, such as increasing or decreasing perceived time.

DEVELOPMENT AND STUDY OF A MICROSPECTROMETER LAYOUT

The photoelectronic processes occurring in a vertically located double-barrier photode-tector under the influence of an external bias voltage are simulated depending on the char-acteristics of the penetrating waves. Under conditions of vertical illumination, to obtain the spectral dependence of the intensity of absorbed radiation, an algorithm was developed for selecting individual waves from the integral flux of the optical signal under conditions of changing the external bias voltage in steps of 1 mV. The algorithm takes into account the main cause-and-effect relationships between the absorption coefficient and design and technological parameters. It makes it possible to obtain the intensities of individual waves from the integral radiation flux in the optical range, where there is a change in the sign of the spectral characteristics and a linear dependence of the point of sign change on the ap-plied voltage. A general architecture, functional structure and remote control scheme for the monitor-ing system for hazardous substances have been developed, as well as corresponding pro-grams with the help of which the algorithm was operated in the LabVIEW environment. To run the algorithm, a prototype of a spectral analysis device was developed and prepared, in which an Arduino Mega board controlled by a microcontroller was used to analyze the output signal of the photodetector. The layout was tested using multi-colored LEDs. The spectral dependence of the inten-sity and its numerical data are obtained on the screen using a new principle. This method is novel and opens up prospects for further development of the field of micro-sized spectro-photometers. To identify mixtures by spectral analysis and estimate their quantity, the RDP (Remote Desktop) protocol of the remote system and the LABVIEW graphical programming envi-ronment were used. RDP was created by Microsoft to provide remote access to Windows servers and workstations.

Advanced Persistent Threat Detection and Mitigation Using Machine Learning Model

The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s livelihood. Several cyber-attacks lead to the compromise of data security. The proposed system offers complete data protection from Advanced Persistent Threat (APT) attacks with attack detection and defence mechanisms. The modified lateral movement detection algorithm detects the APT attacks, while the defence is achieved by the Dynamic Deception system that makes use of the belief update algorithm. Before termination, every cyber-attack undergoes multiple stages, with the most prominent stage being Lateral Movement (LM). The LM uses a Remote Desktop protocol (RDP) technique to authenticate the unauthorised host leaving footprints on the network and host logs. An anomaly-based approach leveraging the RDP event logs on Windows is used for detecting the evidence of LM. After extracting various feature sets from the logs, the RDP sessions are classified using machine-learning techniques with high recall and precision. It is found that the AdaBoost classifier offers better accuracy, precision, F1 score and recall recording 99.9%, 99.9%, 0.99 and 0.98%. Further, a dynamic deception process is used as a defence mechanism to mitigate APT attacks. A hybrid encryption communication, dynamic (Internet Protocol) IP address generation, timing selection and policy allocation are established based on mathematical models. A belief update algorithm controls the defender’s action. The performance of the proposed system is compared with the state-of-the-art models.