E-Learning in Vascular Surgery in the Post-COVID-19 Era: The EL-POCO International Survey.

Wireless communication based on the IEEE 802.11 standard is widely implemented in Internet of Things (IoT) and wireless network systems. The performance of such systems is strongly affected by signal quality, which directly influences connection stability, data transmission reliability, and latency. This study presents the design and implementation of a portable device for monitoring IEEE 802.11 signal quality using an OLED display. The proposed system is built on an ESP8266/ESP32 microcontroller with an integrated Wi-Fi module, enabling real-time measurement of the Received Signal Strength Indicator (RSSI) from the connected access point. The measured RSSI values are processed and converted into signal quality percentages and classified into qualitative levels, namely excellent, good, fair, and poor. The monitoring results, including SSID, RSSI value, signal quality level, connection status, and IP address, are displayed on a 0.96-inch OLED screen. Experimental testing was conducted under various conditions, including different distances from the access point and the presence of physical obstacles. The results demonstrate that the device is capable of providing accurate and stable signal quality information in real time. The developed system offers a low-cost, portable, and practical solution for wireless network performance evaluation and is suitable for educational purposes, network diagnostics, and IoT deployment analysis.

The increasing reliance on networked systems for communication, commerce, and critical infrastructure has significantly amplified the risk of Denial of Service (DoS) attacks, one of the most prevalent and damaging forms of cyberattacks. These attacks aim to overwhelm a system’s resources, rendering services unavailable to legitimate users, which can have severe consequences for organizations and critical infrastructure. This study focuses on the design, development, and implementation of a machine learning-based classification model capable of detecting and mitigating various types of DoS attacks, including Ping of Death, TCP SYN Flood, and Distributed Denial of Service (DDoS) attacks. To achieve this, simulated network traffic is analyzed to extract critical features such as packet size, protocol type, packet count, source IP, and other behavioral patterns that serve as key indicators of malicious activity. The extracted features are used to train a Random Forest Classifier, a robust machine learning model known for its accuracy and reliability in classification tasks. The proposed system operates in real-time, dynamically analyzing incoming traffic, identifying anomalous patterns associated with DoS attacks, and automatically mitigating them by blocking malicious source IP addresses. This approach not only enhances detection accuracy but also minimizes response time, offering a proactive defense mechanism against evolving cyber threats. A comprehensive evaluation of the system is conducted using key performance metrics, including accuracy, precision, recall, and F1-score, which collectively demonstrate the effectiveness of the model in distinguishing legitimate traffic from malicious traffic. The results reveal that the system achieves a high detection accuracy of 95%, with strong precision and recall values, confirming its capability to identify DoS attacks while minimizing false positives and negatives. The findings of this research contribute to the advancement of machine learning applications in the field of cybersecurity, particularly in the domain of intrusion detection and prevention systems. The integration of machine learning algorithms such as the Random Forest Classifier enables the system to adapt to diverse attack scenarios and high-traffic environments, making it scalable for practical deployment in real-world systems. Furthermore, the system’s ability to operate in real time ensures that critical services remain available to legitimate users, mitigating the economic and operational damage caused by DoS attacks. However, the study also highlights challenges related to resource consumption and scalability, particularly in large-scale networks with significant traffic volumes. These limitations underscore the need for further research to optimize resource usage, improve the scalability of the detection model, and explore additional machine learning techniques to enhance performance further. In conclusion, this study demonstrates the feasibility and effectiveness of a machine learning-based approach to detecting and mitigating DoS attacks, providing a scalable, real-time solution that addresses the growing cybersecurity threats faced by modern networked systems. By offering a high level of accuracy and dynamic response capabilities, the system represents a significant step toward strengthening the resilience of critical infrastructure and organizational networks against cyberattacks. Future research will focus on refining the model for large-scale networks, integrating it with existing cybersecurity frameworks, and exploring hybrid detection methods to address emerging attack patterns and techniques. the study emphasizes the importance of leveraging feature engineering techniques to enhance the performance of the classification model by incorporating temporal and spatial analysis of network traffic. By analyzing traffic flow rates, session durations, and inter-packet intervals, the system can better differentiate between legitimate high-traffic activities and malicious attack patterns. Furthermore, the integration of threat intelligence feeds and real-time network monitoring tools enhances the system’s adaptability to emerging attack vectors and zero-day threats. The model's architecture allows for modular updates, enabling seamless incorporation of new features and machine learning algorithms as attack strategies evolve. To further improve system resilience, the study explores combining traditional signature-based detection with anomaly-based methods to create a hybrid intrusion detection system (HIDS) capable of detecting both known and unknown attack types. This hybrid approach ensures a comprehensive defense mechanism while reducing the likelihood of false positives and negatives. In addition, the study proposes incorporating cloud-based deployment models to enable distributed detection across geographically dispersed networks, offering scalability and robust protection for enterprises operating in diverse environments. Finally, the inclusion of real-time visualizations and alert mechanisms provides administrators with actionable insights into network performance, enabling rapid response and effective resource allocation during attack scenarios.

The Domain Name System (DNS) plays a critical role in the functioning of the Internet, providing essential resolution services for nearly all user activities. In this work, we examine the hypothesis that individual users exhibit recurrent and distinctive patterns in their DNS query behavior, which can be leveraged to create unique and robust user fingerprints. Building on a publicly available dataset of real DNS traffic collected from a large-scale network, we evaluate the feasibility of user identification solely based on these behavioral DNS traces, independent of IP address stability. We conducted a comparative study of several machine learning models - including Naive Bayes, Random Forests, XGBoost, Multilayer Perceptrons, and Convolutional Neural Networks - on their ability to classify users based on domain category frequencies and derived statistical features. After extensive data preprocessing, dimensionality reduction, and feature selection, our best-performing model (CNN) achieves a classification accuracy of 86.7% across 1727 classes (unique IP addresses). The results confirm the viability of DNS-based user fingerprinting, even in the presence of dynamic IP addresses. Our approach opens new avenues for applications in network forensics and anomaly detection, while also raising important questions about privacy and ethical use of passive traffic analysis.

ABSTRACT Survey research online offers advantages but is vulnerable to fraudulent responding. Although studies using niche samples, survey panels, or social media recruitment show that fraud is common, few have examined it in online surveys targeting broad populations through community-based recruitment. This study investigated fraudulent responding in an online survey on barriers to job-related training recruited solely through community organizations with minimal eligibility restrictions. A points-based algorithm (30 detection variables) was applied iteratively to assess response legitimacy, and relationships between demographics and validity status were examined using binary logistic regression. Of 1952 surveys, 56.1% (n = 1,096) were deemed fraudulent. Duplicate IP and suspicious e-mail addresses were the most frequent violations. All demographic variables were significantly associated with validity status. Findings show that fraudulent responding is pervasive in community-based online surveys. Strengthening data integrity requires the implementation of flexible, quasi-standardized fraud detection methods and transparent reporting of fraud screening in published online research.

In the article, the authors delve into the critical area of ​​network security, focusing on spoofing attacks that exploit vulnerabilities in computer networks, starting with the access layer network. Spoofing attacks involve impersonating legitimate individuals or devices to gain unauthorized access, intercept data, or disrupt services, creating significant threats to network integrity, confidentiality, and availability. This paper systematically examines four prevalent types of spoofing attacks: MAC spoofing, DHCP spoofing, ARP spoofing, and IP spoofing, detailing their mechanisms, potential impacts, and corresponding defensive strategies. MAC spoofing is one of the main attacks discussed. With this technique, an attacker changes the MAC address of their network interface card (NIC) to mimic the MAC address of an authorized device. Moving to DHCP spoofing, the paper explores how attackers impersonate legitimate Dynamic Host Configuration Protocol (DHCP) servers to distribute malicious IP configurations. In a typical network, DHCP servers automatically assign IP addresses, subnet masks, gateways, and DNS servers to clients. A rogue DHCP server can respond faster to client requests or provide false information, redirecting traffic to attacker-controlled gateways for man-in-the-middle (MITM) interception. ARP spoofing, or Address Resolution Protocol spoofing, is another key focus, where attackers poison the ARP cache of devices to associate their MAC-address with the IP-address of a legitimate host, such as a gateway. By sending gratuitous ARP replies, attackers can redirect traffic intended for the victim through their machine. Finally, IP-spoofing is analyzed as a technique where attackers forge the source IP address in packet headers to disguise their origin. This occurs at the network layer (Layer 3) and is commonly used in DDoS attacks, where spoofed packets amplify traffic to overwhelm targets. The study evaluates effective countermeasures, including port security, 802.1X authentication protocols, DHCP snooping, dynamic ARP inspection, IP Source Guard on switches, ingress/egress filtering and Unicast Reverse Path Forwarding (uRPF)verification on routers, advocating for a layered defense approach. This comprehensive analysis serves as a valuable resource for network engineers, security professionals, and researchers aiming to fortify access networks against evolving spoofing threats, emphasizing proactive measures to safeguard digital infrastructures in an increasingly hostile cyber landscape.

Internet network is a primary need in educational environment especially at SMP Negeri 3 of Lebong Regency of Bengkulu Province. SMP Negeri 3 of Lebong Regency already has internet network using Telkom Indihome provider with 30 Mbps Bandwidth. School does not have a system that can help monitor user internet access, where this can allow better control over user behavior on the network and can ensure that access to the website is in accordance with school policy. The internet access monitoring application is built using PHP programming language with MySQL database. The database is used to store the results of taking user internet access logs and blocked websites. The internet access monitoring application is accessed on the local network by activating the apache web server on the admin laptop and opening a browser to open a web-based application with the localhost/monitoring link. Based on the testing that has been conducted at SMP Negeri 3 of Lebong Regency, the results obtained are that the internet access monitoring application at SMP Negeri 3 of Lebong Regency based on the web can provide information related to users (IP Address) who are active on the internet network and information on sites (IP Address) that have been accessed by each user. In addition, this application can help block websites that are not desired to be accessed by users.

This paper focuses on an innovative detection technique of device-aware phishing attacks on the cloud platform based on FNN and RNN. Over time, the phishing attack, especially targeting the cloud, has developed sophisticated characteristics based on specific characteristics such as IP address, geolocation and configurations of devices. The new threat model associated with cloud attacks will challenge its security. Current traditional phishing detection mechanisms, based on signature and heuristic techniques, do not learn with dynamic device-aware threats. In this sense, the developed model combines FNN and RNN capabilities. It can manage both complex data and sequential patterns of the model. The system is tested with multiple success indicators: the F1-score, knowledge, efficiency and exactness, it was evaluated with a significantly higher detection accuracy for phishing attacks. The accuracy achieved was 99%, and the precision achieved was 97% with a recall of 96%. On comparison with some of the familiar old AI models, such as RF, KNN & LR, the proposed model FNN–RNN achieved a higher level of accuracy, making it an efficient solution to the problem. The results indicate that the system enhances phishing detection in dynamic cloud environments while also improving on security, scalability and resilience against evolving threats. The approach given here presents a powerful solution for advanced phishing attacks in the cloud environment as an existing potential of deep learning models in cybersecurity.

As enterprises migrate workloads to cloud environments, enforcing comprehensive security across multi-tenant architectures is increasingly complex, with challenges that traditional perimeter-based defenses cannot handle effectively. This article proposes a complete zero-trust security architecture specifically designed for multi-tenant Software-as-a-Service systems built on ASP.NET Core microservices, OAuth 2.0 identity frameworks, and Kubernetes orchestration platforms. Traditional implicit trust assumptions are removed by continuously verifying users and devices and application programming interfaces via a sophisticated dynamic trust-scoring engine that assesses contextual parameters such as IP address reputation, session entropy characteristics, behavioral analytics patterns, device posture attributes, and real-time threat intelligence feeds. A metadata-driven security policy engine integrates seamlessly with Kubernetes-based service mesh technologies that allow fine-grained microsegmentation and adaptive access control responding dynamically to changing risk profiles. The proposed architecture was rigorously evaluated across three distinct enterprise-grade workloads: customer relationship management systems, e-commerce platforms, and healthcare data management applications. It demonstrated substantial improvement in security effectiveness through significant reductions in unauthorized access attempts and a mean time to detection of security incidents when compared to traditional perimeter-based models and semi-modern security implementations. This framework enhances real-time visibility into security events, aligns compliance more easily with regulatory requirements, and accomplishes meaningful attack surface reduction through comprehensive defense-in-depth strategies. This article further advances the practical adoption of zero-trust principles by introducing a scalable metadata-driven architectural approach that maintains compatibility with modern continuous integration and continuous deployment pipelines and DevSecOps automation practices, thus letting organizations realize robust security controls without sacrificing development velocity or operational agility.

A large number of fake online accounts creates difficulties for cybersecurity, since the owners of these accounts can share lies, plan assaults and boost cyber problems. In our research, we apply a strictly bounded approach that makes use of machine learning to identify fake accounts using their associated noncontent features. We built a database with 10,000 accounts (half of which were fake, half genuine) taken from (Twitter/X, Instagram) and then looked at nine main features such as account age, the relationship between followers and people the account follows, how often posts are made, how long each post’s gap is, average session duration, the spread of IP addresses, the number of different devices used and fast switching of IP addresses. After the data was cleaned, normalized and data imbalances were corrected with SMOTE if the ratio was higher than 1.5 to 1, a hyperparameters optimized Random Forest classifier with 100 trees were tuned using 5-fold cross validation. For the fake account class, the model got 91.0 % accuracy, 93.4 % precision, 89.2 % recall and a 91.3 % F₁ score using the 3,000 hold out test set. Performing learning curves and permutation tests, we confirmed that the highlights of the project were reliable and significant. Testing on 1,000 new account profiles revealed that it took less than ten milliseconds to infer connections for each account (performing as expected for 94 % of cases). Using public data restriction, approval from an ethics board, keeping logs for a short period and being transparent help to use AI responsibly. Based on our findings, Metadata classifiers can effectively and fast stop attacks caused by fake accounts.

The development of information technology has increased the demand for content management systems that are easy to use and flexible for website management. WordPress is widely used as a platform for building informational websites. This study aims to implement WordPress on a Linux operating system based on a Virtual Machine as a medium for website development and testing. The research methods include system design, virtual network configuration, installation of the web server and database server, and system functionality testing. The implementation is carried out using a Linux server environment with Apache as the web server and MySQL as the database server. The results show that WordPress can be successfully installed and run in a virtual environment and accessed through a local network using an IP address and a local domain. This implementation demonstrates that Virtual Machine technology is an effective and efficient solution for WordPress-based website development without requiring a physical server.

This internship (Kerja Praktik) was conducted at PT Pembangunan Jaya Ancol in the IT and Project Management Office Division with the objective of providing practical work experience for students in the field of computer networking and information technology. The main focus of this internship was the basic configuration of MikroTik devices and their implementation in supporting the company’s IT operational activities. The methods applied during the internship included workplace observation, hands-on practice in network device configuration, and documentation of IT operational activities. The basic configurations performed consisted of network topology design, IP address configuration on ether and bridge-hotspot interfaces, as well as network connectivity testing to ensure proper system operation. In addition, the student was involved in supporting IT operational tasks, such as preparing computer equipment and network systems at the Ancol ticketing area ahead of the New Year celebration. The results of this internship indicate that basic MikroTik configuration plays a crucial role in maintaining network stability and ensuring smooth IT operations within the company. Through this internship, the student gained technical knowledge and practical experience that are highly relevant as preparation for entering the professional world in the field of information technology.

The purpose of this work was to present an artificial intelligence model based on XGBoost algorithms, designed for the detection of intrusions in computer networks by analyzing information on characteristics, techniques and user behaviors. This with the use of data that includes packet size variables, the reputation of IP addresses, failed authentication attempts and access at different times. The methodology used under a focus on Boosting algorithms consolidating as an alternative to improve the growing challenges of cybersecurity. As a result, it can be seen that the research not only empirically validates the proposed model, but also improves the reinforcement of the implementation of automatic solutions based on artificial intelligence, thereby mitigating the risks that arise from daily practice in networks and databases, and thus improving vulnerable network contexts.

The integration of WireGuard and RouterOs mikrotik allows mikrotik users to easily create a secure and fast virtual private network using Virtual Private Network (VPN) for international networks that can provide many benefits, including security, privacy, unlimited access, and more efficient network management. The government has the right to monitor and take action if there is an indication of VPN misuse for illegal activities. WireGuard uses a combination of modern cryptography and network techniques to achieve high performance and strong security. In this study, researchers used the SDLC (Software / System Development life cycle) method where the method is quite detailed covering six stages, namely: planning (Planning), analysis (analysis), design (design), implementation (implementation), testing (testing), maintenance (maintenance). At the testing stage, there needs to be control and monitoring of the local network and packet los that will see changes in the IP address of the user, the aim is to maintain stability when using a Virtual Private Network (VPN).

The Lightning Network (LN) is the most widely adopted second-layer solution for Bitcoin, enabling fast, low-cost transactions through a decentralized payment channel network. Despite its growing importance and the increasing interest from researchers across disciplines, progress in LN research is often impeded by limited access to structured, validated, and reproducible network data. In this paper, we present a curated collection of LN network snapshots spanning from January 2019 to July 2023, reconstructed from publicly available gossip message archives. We apply rigorous consistency checks, and enrich node metadata with city-level geolocation data derived from public IP addresses. The resulting dataset captures the temporal and spatial evolution of the LN, addresses a critical research gap, and provides a reproducible foundation for future empirical studies on network structure and dynamics – accessible not only to the computer science community but also to researchers in cryptocurrency and economics.

Abstract Remotely delivered interventions offer advantages like broader geographic reach and increased accessibility. However, they also introduce challenges, including scams in the recruitment and implementation as well as technological barriers. This paper reports challenges encountered and lessons learned through a feasibility study of EMPOWER (Engage your Mind and Body to Promote your Own Wellness, Energy and Relaxation), a self-care health intervention incorporating complementary and integrative techniques. In Fall 2024, we conducted four EMPOWER sessions (three via Zoom, one in-person). In the first two Zoom sessions, to ensure participants’ privacy and comfort, we did not require verbal or camera engagement, nor did we have a moderator present in the breakout room discussions. After the first two Zoom sessions, we discovered suspicious activity, including similar email formats, inconsistent demographics (i.e., higher-than-expected male and younger participants), and IP address anomalies (survey responses originating from outside the research area and multiple responses from single IP addresses). As a result, data collected from those first two Zoom sessions had to be discarded. Based on these experiences, we recommend several strategies for future virtual caregiving interventions. These include recruiting participants through established in-person groups; implementing rigorous participant vetting processes to verify authenticity; limiting responses to one per IP address; requiring verbal or camera engagement during the intervention sessions; and moderating Zoom breakout room discussions. While remotely delivered interventions present challenges, creative solutions can help maintain research integrity and enhance participant experience. Further work is needed to evaluate the efficacy of these strategies.

Abstract Remotely delivered interventions offer advantages like broader geographic reach and increased accessibility. However, they also introduce challenges, including scams in the recruitment and implementation as well as technological barriers. This paper reports challenges encountered and lessons learned through a feasibility study of EMPOWER (Engage your Mind and Body to Promote your Own Wellness, Energy and Relaxation), a self-care health intervention incorporating complementary and integrative techniques. In Fall 2024, we conducted four EMPOWER sessions (three via Zoom, one in-person). In the first two Zoom sessions, to ensure participants’ privacy and comfort, we did not require verbal or camera engagement, nor did we have a moderator present in the breakout room discussions. After the first two Zoom sessions, we discovered suspicious activity, including similar email formats, inconsistent demographics (i.e., higher-than-expected male and younger participants), and IP address anomalies (survey responses originating from outside the research area and multiple responses from single IP addresses). As a result, data collected from those first two Zoom sessions had to be discarded. Based on these experiences, we recommend several strategies for future virtual caregiving interventions. These include recruiting participants through established in-person groups; implementing rigorous participant vetting processes to verify authenticity; limiting responses to one per IP address; requiring verbal or camera engagement during the intervention sessions; and moderating Zoom breakout room discussions. While remotely delivered interventions present challenges, creative solutions can help maintain research integrity and enhance participant experience. Further work is needed to evaluate the efficacy of these strategies.

Geolocation by IP involves identifying an individual based on their IP address. Using these services helps identify the region where an individual accessing a website is located, enabling region-based limitations. The address-to-location identification can be used to narrow coordinates to within a 1-km radius for the entire world. Various IP addresses can also be grouped to cover an IPv4 address space of 740 million and IPv6 address capabilities. Correct IP geolocation detection helps determine the geographic region of the service user. Detection is essential for content delivery, fraud detection, data regulation breaches, and national cybersecurity. Regarding detection accuracy issues, approximate borders would help quantify the difference between reported and ground-truth country boundaries. Borders have branches that further diverge into regions, states, counties, and town municipalities. These units facilitate international transportation and significantly impact transportation analysis and structure. Compare the surfacing results generated by algorithms integrated with various open-source and commercial functional geographical databases, alongside the accuracy claims made by these databases. The research incorporates active latency triangulation and passive topology-inferencing techniques to estimate detection accuracy to around the hundredth mark. Overall, this illustrates how the operational economic advantage of North America and Europe, as opposed to booster regions such as Africa or South Asia, affects study accuracy in light of top political sensitivity. Furthermore, it raises concerns for us, as Ethiopian non-biased third-world development implies peer politics. This research provides a conclusive basis for creating a benchmarking system to assess and improve the efficiency of IP geolocation technology. Further incorporating newer technologies into the system will improve the system by automating recalibration and integrating real-time data validation.

The Domain Name System (DNS) constitutes an essential component of Internet infrastructure serving as a translator of human-readable domain names into machine-readable IP addresses. Although simple in its operation, DNS performance, especially its responsiveness, has a bearing on the user experience, web application performance, and network resilience. This research examines the responsiveness of DNS across several top-level domains (TLDs) including .com, .org, .net, .edu and the newer generic TLDs. tech and. xyz. Using a set of recursive DNS resolvers distributed globally, this research resolves TLDs in parallel over long durations to capture rich datasets. Systematic assessment is performed on response latency due to propounded factors such as TTLs, cache performance, the location of authoritative servers, and the efficiency of anycast routing. The employed techniques of real-time probing, latency aggregation, temporal sampling, and statistical smoothing aid in consistency, mitigating transient network disturbances. A hybrid analytical model of time-series analysis and decision-tree based classification is applied to cluster domains revealing performance profile similarities. The legacy TLDs were observed to outperform newer counterparts, sustaining the argument about the disparity of response times across TLDs, newer TLDs were found to lag due to immature infrastructure and poorly distributed name servers. These findings highlight DNS optimization needs, particularly for emerging TLDs to improve user experience and site responsiveness. As a practical reference for network architects and domain registrars, the paper provides a scoring framework for assessing the performance of a DNS at the TLD level.

Personal computers and small networks have become easy targets for automated cyber attacks in recent years. Port scans, malware distribution, and remote exploits are now commonplace threats that traditional antivirus programs and firewalls struggle to address effectively. These conventional tools rely heavily on known signatures, which means they often miss new or evolving threats, leaving users vulnerable. We developed AURA as a response to this challenge. It's a modular framework designed to provide continuous, real-time protection through adaptive intelligence and automation. The system actively monitors network traffic, evaluates threats based on their behavior, and cross-references suspicious activity against global reputation databases like Spamhaus DROP and AbuseIPDB. When it identifies a genuine threat, AURA doesn't just block it the system can redirect attackers to a local honeypot where their activities are safely logged for analysis. Beyond network monitoring, AURA includes an integrity monitor that watches over critical files and system components. If something changes without authorization, users receive immediate alerts via the Twilio API. Everything comes together in a centralized web dashboard that provides a complete view of monitoring, analysis, and response activities. Our testing demonstrates that AURA successfully identifies high-risk IP addresses, isolates them in real time, and collects valuable forensic evidence—all with minimal system overhead. This work shows how AI and automation can bring enterprise-level security capabilities to personal computing environments, enabling proactive defense at the endpoint level.