Search Results (26)

Wi-Fi router security is a real concern for universities and research centers that rely on strong, dependable networks for everything they do. In this study, we took a close look at four popular Wi-Fi router firmwares using open-source tools such as Binwalk, CVE-Bin-Tool, and Semgrep. We carefully examined the file systems, cross-referenced them with the National Vulnerability Database (NVD), and searched for outdated software like BusyBox and OpenSSL. What we found was clear: proprietary firmwares had more Critical and High vulnerabilities, while OpenWrt stood out for being more secure, easier to update, and openly maintained by its community. Our reproducible process automates how we gather evidence and map vulnerabilities, making firmware auditing more practical and trustworthy. These results make a strong case for using open-source firmware as a safer, more manageable choice for institutional networks. Full article

OpenStack is a popular open-source cloud platform that orchestrates virtualized compute, storage, and networking resources. In such virtualized environments, packet traceability refers to the ability to track the path and transformations of network packets as they traverse virtual switches, routers, and interfaces. This paper presents a comprehensive overview of packet traceability in OpenStack cloud environments. We provide an introduction to the OpenStack architecture with a focus on the Networking component (Neutron) and discuss how packets flow through virtual networking elements. We examine the routing and interface mechanisms that enable communication within and across nodes, and compare single-node versus multi-node OpenStack deployments from a packet tracing perspective. Furthermore, we survey tools and techniques for packet tracing (such as monitoring interfaces, using tcpdump, and Open vSwitch tracing), and highlight the challenges faced (multi-tenancy, overlay networks, etc.) in tracing packets. We offer recommendations for improving traceability, including leveraging built-in OpenStack features and advanced kernel-level tracing technologies. Our goal is to aid cloud administrators and researchers in understanding and improving network packet observability in OpenStack clouds, thereby enhancing troubleshooting and security analysis capabilities. Full article

Probabilistic Boolean Networks can capture the dynamics of complex biological systems as well as other non-biological systems, such as manufacturing systems and smart grids. In this proof-of-concept manuscript, we propose a Probabilistic Boolean Network architecture with a learning process that significantly improves the prediction of the occurrence of faults and failures in smart-grid systems. This idea was tested in a Probabilistic Boolean Network model of the WSCC nine-bus system that incorporates Intelligent Power Routers on every bus. The model learned the equality and negation functions in the different experiments performed. We take advantage of the complex properties of Probabilistic Boolean Networks to use them as a positive feedback adaptive learning tool and to illustrate that these networks could have a more general use than previously thought. This multi-layered PBN architecture provides a significant improvement in terms of performance for fault detection, within a positive-feedback network structure that is more tolerant of noise than other techniques. Full article

Field Programmable Gate Arrays (FPGAs) have witnessed an increase in space applications in the last years, mainly due to their cost-effective high-performances and flexibility. However, the susceptibility of these devices to radiation-induced effects when working in such an environment is well known. When common mitigation techniques are not sufficient to ensure the correct completion of a task, radiation-hardened FPGAs represent one of the most effective solutions. NanoXplore, in this context, is the first European developer of rad-hard FPGAs, which embed intrinsic high complexity in their architectures preventing the user from using or developing custom placement and routing algorithms. In this paper, we overcame these issues by proposing the first tool tailored to NanoXplore devices which allows the exploration of NanoXplore device architectures and routing of points through a Python interface. We developed a model that reflects the one used by the vendor, allowing the user to extract info about routes, nets and additional logic, otherwise unavailable. The tool also performs routing of points in the programmable logic, computing the optimal path. An implementation of the router on Graphic Processing Unit (GPU) is proposed to exploit the highly parallelizable nature of the problem. Finally, routing timing analyses on different benchmarks have been performed, improving the routing routine time. Full article

The task of router alias resolution for IPv4 networks presents a formidable challenge in the realm of router-level topology inference. Despite the considerable potential exhibited by machine-learning-based alias-resolution methods for IPv4 networks, several constraints impede their effectiveness. These constraints include a low discovery rate of aliased IPs, a failure to account for router aggregation, and a dearth of valid features in current schemes. In this study, we introduce a novel alias resolver, AliasClassifier, which is based on the Random Forest model and the alias triangulation algorithm. This innovative model identifies the key six features from a set of four prevalent routing behaviors that are typically employed to distinguish aliased IPs from non-alienated IPs. Subsequently, the AliasClassifier aggregates aliased IP pairs into routers using an alias triangulation algorithm. Experimental results demonstrate that AliasClassifier excels in discovering aliased IPs in IPv4 networks, boasting a resolution accuracy as high as 94.8% and a recall rate of 40.4%. Its comprehensive performance significantly surpasses that of state-of-the-art alias resolvers such as TreeNET, MLAR, and APPLE. Furthermore, as a typical centralized alias parser, AliasClassifier’s deployment cost is remarkably low. Consequently, AliasClassifier emerges as an ideal tool for router alias resolution in large-scale IPv4 networks. Full article

An N-bit priority resolver having N inputs and N outputs functions as polling hardware in an embedded system, enabling access to a resource when multiple devices initiate access requests at its inputs which may be located on-chip or off-chip. Subsystems such as data buses, comparators, fixed- and floating-point arithmetic units, interconnection network routers, etc., utilize the priority resolver function. In the literature, there are many transistor-level designs for the priority resolver based on dynamic CMOS logic, some of which are modular and others are not. This article presents a novel gate-level modular design of priority resolvers that can accommodate any number of inputs and outputs. Based on our modular design architecture, small-size priority resolvers can be conveniently combined to form medium- or large-size priority resolvers along with extra logic. The proposed modular design approach helps to reduce the coding complexity compared to the conventional direct design approach and facilitates scalability. We discuss the gate-level implementation of 4-, 8-, 16-, 32-, 64-, and 128-bit priority resolvers based on the direct and modular approaches and provide a performance comparison between these based on the design metrics. According to the modular approach, different sizes of priority resolver modules were used to implement larger-size priority resolvers. For example, a 4-bit priority resolver module was used to implement 8-, 16-, 32-, 64-, and 128-bit priority resolvers in a modular fashion. We used a 28 nm CMOS standard digital cell library and Synopsys EDA tools to synthesize the priority resolvers. The estimated design metrics show that the modular approach tends to facilitate increasing reductions in delay and power-delay product (PDP) compared to the direct approach, especially as the size of the priority resolver increases. For example, a 32-bit modular priority resolver utilizing 16-bit priority resolver modules had a 39.4% reduced delay and a 23.1% reduced PDP compared to a directly implemented 32-bit priority resolver, and a 128-bit modular priority resolver utilizing 16-bit priority resolver modules had a 71.8% reduced delay and a 61.4% reduced PDP compared to a directly implemented 128-bit priority resolver. Full article

Mechanical vibrations represent an important problem in machining processes performed by machine tools. They affect surface quality, tool life, and productivity. In extreme situations, chattering may appear, which can dramatically reduce the tool life. CNC router machines are particularly sensitive to vibrations, with their structure bearing resemblance to a composition of beams that are uniform in cross-section. These CNC machines are commonly used for different tasks, like engraving, cutting, and 3D printing. This work proposes a modeling methodology for vibration systems that consist of coupled thin beams subjected to vertical vibration. This methodology is used to model vertical vibrations in a CNC router machine. For this, the geometry is decomposed into beams of uniform cross-sections that are coupled at their ends. Each beam is modeled by means of the classical theory of Bernoulli–Euler for thin beams. The boundary conditions are determined by the beam couplings. In the system thus defined, fundamental frequencies are calculated using the bisection method, and then the modes are computed for the corresponding frequencies. The modal amplitudes, being time-dependent, are modeled as a state space system, considering the first m frequencies. In order to provide support to the modeling methodology, simulation experiments are performed for validation, comparing the results provided by models built with the proposed methodology against finite element models and an experimental setting with a real structure. Moreover, an analysis of the vibration model focusing on a specific component of the equipment is presented to illustrate the usefulness and flexibility of the models obtained with the proposed methodology. Full article

Micromobility responds to urban transport challenges by reducing emissions, mitigating traffic, and improving accessibility. Nevertheless, the safety of micromobility users, particularly cyclists, remains a concern in urban environments. This study aims to construct a safety map and a risk-averse routing system for micromobility users in diverse urban environments, as exemplified by a case study in Lisbon. A data-driven methodology uses object detection algorithms and image segmentation techniques to identify potential risk factors on cycling routes from Google Street View images. The ‘Bikeable’ Multilayer Perceptron neural network measures these risks, assigning safety scores to each image. The method analyzed 5321 points across 24 parishes in Lisbon, with an average safety score of 4.5, indicating a generally safe environment for cyclists. Carnide emerged as the safest area, while Alcântara exhibited a higher level of potential risks. Additionally, an equation is proposed to compute route efficiency, enabling comparisons between different routes for identical origin-destination pairs. Preliminary findings suggest that the presented routing solution exhibits higher efficiency than the commercial routing benchmark. Risk-averse routes did not result in a substantial rise in travel distance or time, with increments of 7% on average. The study also contributed to increasing the existing amount of cycle path data in Lisbon by 12%, correcting inaccuracies, and updating the network in OpenStreetMap, providing access to more precise information and, consequently, more routes. The key contributions of this study, such as the safety map and risk-averse router, underscore the potential of data-driven tools for boosting urban micromobility. The solutions proposed demonstrate modularity and adaptability, making them fit for a range of urban scenarios and highlighting their value for cities prioritizing safe, sustainable urban mobility. Full article

The cloud computing networks used in the IoT, and other themes of network architectures, can be investigated and improved by cheminformatics, which is a combination of chemistry, computer science, and mathematics. Cheminformatics involves graph theory and its tools. Any number that can be uniquely calculated by a graph is known as a graph invariant. In graph theory, networks are converted into graphs with workstations or routers or nodes as vertex and paths, or connections as edges. Many topological indices have been developed for the determination of the physical properties of networks involved in cloud computing. The study computed newly prepared topological invariants, K-Banhatti Sombor invariants (KBSO), Dharwad invariants, Quadratic-Contraharmonic invariants (QCI), and their reduced forms with other forms of cloud computing networks. These are used to explore and enhance their characteristics, such as scalability, efficiency, higher throughput, reduced latency, and best-fit topology. These attributes depend on the topology of the cloud, where different nodes, paths, and clouds are to be attached to achieve the best of the attributes mentioned before. The study only deals with a single parameter, which is a topology of the cloud network. The improvement of the topology improves the other characteristics as well, which is the main objective of this study. Its prime objective is to develop formulas so that it can check the topology and performance of certain cloud networks without doing or performing experiments, and also before developing them. The calculated results are valuable and helpful in understanding the deep physical behavior of the cloud’s networks. These results will also be useful for researchers to understand how these networks can be constructed and improved with different physical characteristics for enhanced versions. Full article

The practice of sports has been steadily evolving, taking advantage of different technological tools to improve different aspects such as individual/collective training, support in match development or enhancement of audience experience. In this work, an in-house implemented monitoring system for golf training and competition is developed, composed of a set of distributed end devices, gateways and routers, connected to a web-based platform for data analysis, extraction and visualization. Extensive wireless channel analysis has been performed, by means of deterministic 3D radio channel estimations and radio frequency measurements, to provide coverage/capacity estimations for the specific use case of golf courses. The monitoring system has been fully designed considering communication as well as energy constraints, including wireless power transfer (WPT) capabilities in order to provide flexible node deployment. System validation has been performed in a real golf course, validating end-to-end connectivity and information handling to improve overall user experience. Full article

The evaluation of surface quality is an important criterion to understand the effect of the cutting angle in relation to the grain and of the processing tool on wood. This paper examines, in a comparison, the surface quality of maple cut through by CNC and CO₂ laser, for different angles with regard to the wood grain: 0°, 15°, 30°, 45°, 60°, 75°, and 90° and at different feed speeds of the CNC router: 2; 2.5; 3; 3.5 and 4 m/min. The direction of processing as related to the grain was a more significant factor in comparison with the feed speed when CNC was used, with best options for 0°, 90° and 75° and worst for 15°, where fuzzy grain was predominant, followed in order by 30°, 45°, and 60°, where pull-out material prevailed. The laser smoothed the core roughness, Rk, with no significant differences as related to the wood grain direction and enhanced an anatomical waviness earlywood-latewood, with the earlywood processed deeper. As the cutting advanced from along to across the grain, the laser uncovered more wood anatomical details and with less destruction. No significant differences in Rk between CNC cutting and laser processing were found for angles: 0°, 60°, and 75°, but surfaces processed at 15°, 30°, and 45° were significantly rougher in the case of CNC cutting. Comparative FTIR investigation of surfaces cut by laser and CNC (at 0° and 90°) clearly revealed temperature-induced chemical changes, such as hemicelluloses degradation, possibly demethylation and advanced condensation in the structure of lignin, in the case of laser processing. Full article

Cross channel scripting (XCS) is a common web application vulnerability, which is a variant of a cross-site scripting (XSS) attack. An XCS attack vector can be injected through network protocol and smart devices that have web interfaces such as routers, photo frames, and cameras. In this attack scenario, the network devices allow the web administrator to carry out various functions related to accessing the web content from the server. After the injection of malicious code into web interfaces, XCS attack vectors can be exploited in the client browser. In addition, scripted content can be injected into the networked devices through various protocols, such as network file system, file transfer protocol (FTP), and simple mail transfer protocol. In this paper, various computational techniques deployed at the client and server sides for XCS detection and mitigation are analyzed. Various web application scanners have been discussed along with specific features. Various computational tools and approaches with their respective characteristics are also discussed. Finally, shortcomings and future directions related to the existing computational techniques for XCS are presented. Full article

Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people. There are numerous possibilities to use the Wi-Fi-based HAR solution for human-centric applications in intelligent surveillance, such as human fall detection in the health care sector or for elderly people nursing homes, smart homes for temperature control, a light control application, and motion detection applications. This paper’s focal point is to classify human activities such as EMPTY, LYING, SIT, SIT-DOWN, STAND, STAND-UP, WALK, and FALL with deep neural networks, such as long-term short memory (LSTM) and support vector machines (SVM). Special care was taken to address practical issues such as using available commodity hardware. Therefore, the open-source tool Nexmon was used for the channel state information (CSI) extraction on inexpensive hardware (Raspberry Pi 3B+, Pi 4B, and Asus RT-AC86U routers). We conducted three different types of experiments using different algorithms, which all demonstrated a similar accuracy in prediction for HAR with an accuracy between 97% and 99.7% (Raspberry Pi) and 96.2% and 100% (Asus RT-AC86U), for the best models, which is superior to previously published results. We also provide the acquired datasets and disclose details about the experimental setups. Full article

Ocean in-situ sensors are crucial for measuring oceanic parameters directly from the sea in a spatial and temporal basis. Real-time operation is used in many applications related to decision support tools and early warning services in case of accidents, incidents and/or disasters. The design of the proposed system is described as a rapid-response detection system, which aims to measure natural and artificial radioactive contaminants or other crucial ocean parameters, to replace the traditional method of sampling. The development of an interactive cellular system is undertaken using a commercial router that is programmed according to sensor specifications. A radioactivity sensor is integrated in a communication box enabling self-powered operation with a solar panel. The proposed system operates in (near) real-time mode and provides gamma-ray spectra by integrating the sensor and the appropriate electronic modules in it. Additionally, an on-site experiment was conducted to test the operability of the system in a real environment close to the sea, for monitoring fallout due to rainfall and snowfall events. The main intense radionuclides that were observed by different energy lines, were radon progenies (²¹⁴Bi, ²¹⁴Pb). The continuous operation of the whole system was controlled by operating the system during the winter period. Full article

Fretwork, as a decorating technique, can today be performed with modern means by laser or CNC routing, but the tool influence on the surface quality must be well understood for further optimization of the cutting parameters. The paper presents an experimental work for the determination of the influence of the cutting angle with respect to the grain on the surface quality of larch wood (Larix decidua Mill.) cut through by laser and CNC routing at different feed speeds. The research considered geometric fretwork patterns derived from triangle, where the cutting angles with respect to the wood grain were 0°, 15°, 30°, 45°, 60°, 75° and 90°. The feed speeds of the CNC routing were set for 2, 2.5, 3, 3.5 and 4 m/min. The surface quality was analyzed by a range of roughness, waviness and primary profile parameters combined with stereomicroscopic images. Although the principle of cutting and interaction with wood of the two processing tools, laser and CNC, is very different, the analysis with roughness parameters has revealed that the cutting angle with respect to the grain has an essential influence, whatever the cutting tool. The surface quality decreases with an increase in the feed speed of the CNC router and processing at 15° and 60° appears to be the worst option from all angle variants for both types of processing. Cutting by laser is melting wood in latewood areas resulting in a smoothing effect. Full article