Local Network Environment Research Articles

Deep Packet Inspection Model Based on Support Vector Machine for Anomaly Detection in Local Area Networks

Deep packet inspection is a network security solution that identifies and flags anomalous network traffic patterns in a local network environment. Traditional signature-based techniques for intrusion detection are limited in identifying different attacks or completely new kinds, which makes them unsuitable in some situations. In addition, most previous methods for anomaly detection have low detection rate and high false alarm. In this study, a deep packet inspection model based on support vector machine (SVM) for anomaly detection in local area networks was proposed. The proposed method combined the SelectKBest method and SVM for the categorization of anomaly in a local network environment. Results showed that the proposed method outperformed other related machine learning methods with accuracy, precision, recall, and F1-score of 94.81%, 94.03%, 94.13%, and 94.0799%, respectively. The accuracy result shows that most network traffic can be correctly identified by the SVM using the SelectKBest approach, with minimal false positives or negatives.

Tunable ultra-broadband multi-band NIR emission in Bi-doped aluminogermanate glasses and fibers via controllable Al2O3 content for broadband amplifiers

The tunable ultra-broadband multi-band NIR emission of Bi-doped germanate glass and fiber can be achieved via constructing various glass local network environments with steerable Al2O3 content and show potential applications in broadband amplifiers.

Seamless real-time thermal imaging system with ESP8266: wireless data transfer and display using UDP

Thermal imaging technology has become increasingly popular for various applications, including industrial monitoring, building automation, and medical diagnostics. However, existing thermal imaging systems often come with high costs and limited connectivity options. In this paper, we propose a method to address these challenges by utilizing the ESP8266 microcontroller to create a thermal imaging system that can measure thermal pixel values, transfer the data wirelessly using the ESP8266’s networking capabilities and display the pixel data in real-time on a Thin-film-transistor liquid-crystal (TFT) display. The objective is to establish a seamless and real-time transfer of thermal images within a local network environment. User datagram protocol (UDP) supports transmission via broadcast and multicast, making it highly efficient for delivering data to multiple clients or devices on a network. It allows a single UDP packet to be simultaneously sent to multiple destinations, enhancing its effectiveness. This feature simplifies the implementation of network protocols and applications, reducing their overall complexity. UDP is particularly well-suited for devices with limited resources, such as microcontrollers or embedded systems, where memory and computing power are constrained. Experimental results demonstrate the successful transmission and display of thermal pixel data between the ESP8266 microcontrollers using the UDP protocol. The project utilizes the Arduino framework along with ESP8266WiFi and UDP libraries to enable network connectivity and UDP communication. The sender and receiver devices are connected to the same local network, guaranteeing efficient and low-latency transmission of thermal pixel data. The system achieves real-time communication within a radius of approximately 15–18 m, ensuring immediate visualization of thermal images on connected displays. By minimizing latency, the system enables a seamless and instantaneous viewing experience offering seamless and instantaneous image visualization for the users.

Public goods games played on hypergraphs, by agents with bounded learning and planning

Public goods games between model agents with bounded rationality and a simple learning rule, which have been previously shown to represent experimentally observed human playing behavior, are studied by direct simulation on various lattices with different network topology. Despite strong coupling between playing groups, we find that average investments do not significantly depend upon network topology, but are determined solely by immediate local network environment. Furthermore, the dependence of investments on characteristic agent parameters factorizes into a function of individual cognitive budget, K, and a simple function 1/(1+c(0)/β), where c(0) is the group centrality and β=12.5 for all networks investigated. Given the good agreement of agent behavior with available experiments, this seems to indicate that even complex societal networks of investment in public goods may be accessible to predictive simulation with limited effort.

Vitamin interdependencies predicted by metagenomics-informed network analyses and validated in microbial community microcosms

Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, we use an organism abundance correlation network to investigate factors that control community organization in mine tailings-derived laboratory microbial consortia grown under dozens of conditions. The network is overlaid with metagenomic information about functional capacities to generate testable hypotheses. We develop a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs, and predict that a Variovorax species is a hub as an important source of thiamine. Quantification of thiamine during the growth of Variovorax in minimal media show high levels of thiamine production, up to 100 mg/L. A few of the correlated thiamine auxotrophs are predicted to produce pantothenate, which we show is required for growth of Variovorax, supporting that a subset of vitamin-dependent interactions are mutualistic. A Cryptococcus yeast produces the B-vitamin pantothenate, and co-culturing with Variovorax leads to a 90-130-fold fitness increase for both organisms. Our study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities.

Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning.

The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection System (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a fog computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a federated training and detection procedure through deep learning to minimize the impact of a single point of failure (SPOF) and reduce the workload of each device, thus achieving accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with a near-real-time detection and mitigation system.

Computation-Based Investigation of Motion and Dynamics of Lithium in Phase Separated Silicon-Oxide Anode Materials

Si attracts significant attention as an alternative anode material to replace the conventional graphite anode in lithium-ion batteries (LIBs). Si offers extremely high theoretical energy-storage capacity of 4200 mAh/g for Li4.4Si, while the theoretical energy-storage capacity of graphite is only 372 mAh/g for Li/C6. In addition, Si is abundant, eco-friendly, and non-toxic, and it also has a safe thermodynamic potential with an average voltage of about 0.4 V vs. Li+/Li, making them attractive candidates for LIB anodes. However, the capacity of the Si anode exhibits a relatively high initial charge capacity and then rapidly decreases as the charge/discharge cycle proceeds, hampering its extensive application. This rapid performance degradation can be attributed to the poor connection between the active material and the current collector resulting from the severe volume expansion and contraction of the Si particles during the repeated charge and discharge cycles, respectively.Controlled addition of O to Si is often taken as a viable approach to utilize Si as an anode material. Silicon suboxides (SiOx, 0 < x < 2), compared with Si, offer a low energy capacity but a high stability against volume expansion. As a result, silicon monoxide (SiO) anodes are indeed commercialized by being blended with graphite. However, only low amounts (about 5 wt%) of SiO are blended with graphite because of the poor first cycle efficiency of SiO. During the charge process, SiOx reacts with Li and produces in-situ byproducts such as Li2O and Li-silicates, which are irreversible in following discharge cycles. This irreversible Li consumption is known to decrease the initial Coulombic efficiency (ICE) of SiO, typically below 75%. The low ICE of SiOx along with its inherently poor electronic conductivity leads to poor rate performance and increased capacity decay, hindering its substantial application in LIBs. To realize more extensive application of SiOx for LIB anodes, physical properties of SiOx should be improved in a direction to mitigate the low ICE issue.In this study, we investigate the motion and dynamics of a Li atom in SiOx by performing a series of first principles-based atomistic simulations. To this end, we perform Monte Carlo simulations within a continuous random network model to generate realistic SiOx structures with varying O-to-Si ratios. Then, we implement a density-functional theory calculation-based path sampling scheme to obtain detailed thermodynamic information when a Li atom penetrates a SiO matrix. Subsequent electronic structure analysis reveals that the thermodynamic stability of a Li atom is determined by local Si-O network environment surrounding Li. The identified thermodynamic information regarding the Li dynamics in SiO provides additional insight into the origin and solutions of the low ICE, highlighting the importance of controlling the SiO morphology during the synthesis of SiO. This fundamental understanding can be an important theoretical basis for developing practically applicable high-ICE silicon suboxide-based anode materials. Figure 1

Experimental Performance Evaluation of Enhanced User Interaction Components for Web-Based Collaborative Extended Reality

COVID-19-related quarantine measures resulted in a significant increase of interest in online collaboration tools. This includes virtual reality (VR) or, in more general term, extended reality (XR) solutions. Shared XR allows for activities such as presentations, training of personnel or therapy to take place in a virtual space instead of a real one. To make online XR as accessible as possible, a significant effort has been put into the development of solutions that can run directly in web browsers. One of the most recognized solutions is the A-Frame software framework, created by Mozilla VR team and supporting most of the contemporary XR hardware. In addition, an extension called Networked-Aframe allows multiple users to share virtual environments, created using A-Frame, in real time. In this article, we introduce and experimentally evaluate three components that extend the functionality of A-Frame and Networked-Aframe. The first one extends Networked-Aframe with the ability to monitor and control users in a shared virtual scene. The second one implements six degrees of freedom motion tracking for smartphone-based VR headsets. The third one brings hand gesture support to the Microsoft HoloLens holographic computer. The evaluation was performed in a dedicated local network environment with 5, 10, 15 and 20 client computers. Each computer represented one user in a shared virtual scene. Since the experiments were carried out with and without the introduced components, the results presented here can also be regarded as a performance evaluation of A-Frame and Networked-Aframe themselves.

Multi-agent reinforcement learning for resource allocation in IoT networks with edge computing

To support popular Internet of Things (IoT) applications such as virtual reality and mobile games, edge computing provides a front-end distributed computing archetype of centralized cloud computing with low latency and distributed data processing. However, it is challenging for multiple users to offload their computation tasks because they are competing for spectrum and computation as well as Radio Access Technologies (RAT) resources. In this paper, we investigate computation offloading mechanism of multiple selfish users with resource allocation in IoT edge computing networks by formulating it as a stochastic game. Each user is a learning agent observing its local network environment to learn optimal decisions on either local computing or edge computing with a goal of minimizing long term system cost by choosing its transmit power level, RAT and sub-channel without knowing any information of the other users. Since users' decisions are coupling at the gateway, we define the reward function of each user by considering the aggregated effect of other users. Therefore, a multi-agent reinforcement learning framework is developed to solve the game with the proposed Independent Learners based Multi-Agent Q-learning (IL-based MA-Q) algorithm. Simulations demonstrate that the proposed IL-based MA-Q algorithm is feasible to solve the formulated problem and is more energy efficient without extra cost on channel estimation at the centralized gateway. Finally, compared with the other three benchmark algorithms, it has better system cost performance and achieves distributed computation offloading.

Fall Behavior Recognition Based on Deep Learning and Image Processing

Accidental fall detection for the elderly who live alone can minimize the risk of death and injuries. In this article, we present a new fall detection method based on "deep learning and image, where a human body recognition model-DeeperCut is used. First, a camera is used to get the detection source data, and then the video is split into images which can be input into DeeperCut model. The human key point data in the output map and the label of the pictures are used as training data to input into the fall detection neural network. The output model then judges the fall of the subsequent pictures. In addition, the fall detection system is designed and implemented with using Raspberry Pi hardware in a local network environment. The presented method obtains a 100% fall detection rate in the experimental environment. The false positive rate on the test set is around 1.95% which is very low and can be ignored because this will be checked by using SMS, WeChat and other SNS tools to confirm falls. Experimental results show that the proposed fall behavior recognition is effective and feasible to be deployed in home environment.

The Toolkit of Success Rate Calculation of Broiler Harvest

Failed in the harvest should be avoided by the owners of broiler farms. Various ways are done to make the harvest successful and make a profit. Preparations made before the period of broiler development can be done optimally, with the hope that the harvest is successful. But the problem, on the field in fact not as smooth as had been planned at the beginning or before the development period. There is always a lack or inappropriate so that the harvest is not optimal or even harvest failure. Based on the background that has been described, we tried to create a toolkit that can be used to calculate success rate of the broiler harvest. With this toolkit, broiler farm owners are expected to have a guide and can make the right move in the period of broiler development. To calculate the success rate of broiler harvest, we used a model based on the performance index calculation. Toolkit that created running on local network environment using an Apache web server. The Toolkit uses JavaScript library to display the calculation results in the form of charts that are easier to understand. Based on the results of testing that has been done, this toolkit can perform calculations in a relatively short time. Can present the results of calculations with accurate information. The accuracy of success rate calculation toolkit reached 90%. It is obtained by comparing the performance index and the total weight of broiler that obtained when harvested.

Multimodal human-machine interface devices in the cloud

In an increasingly connected and multidisciplinary world, we propose a new paradigm of web application development. It makes the whatever modal input to use the same interface to connect to the applications. It can essentially free the web application programmers and the end users from the need of physically handling the data input devices when they are building a multimodal system. The same application can be used for a whole range of physically different peripherals, but similar from the logical point of view of data entry. This paper discusses the implementation of a pilot project, currently in a local network environment, where all the devices in the LAN are identified and described in an interface server. Users in the local network may, upon request, make use of such devices. The communication of these peripherals with the web applications will be carried out by a network of modules that run under the websocket technology. The whole process of communication and connection establishment is automatic and guided by the existing configurations in the interface server. The entire platform runs under SOA strategy and is fully scalable and configurable. Its use is not limited to games because it has much wider possibilities, interactivity in teaching, accessibility for people with special needs, adaptation of web applications to the use of uninitiated, etc.

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.

MotivationDetecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins.ResultsWe propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker’s ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology.Availability and implementationThe CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/.Supplementary information Supplementary data are available at Bioinformatics online.

Received Signal Strength Estimation in Wireless Local Area Network (WLAN) Environment

This paper is focused on indoor propagation prediction in wireless local area network. In this paper, measurements of received signal strength were carried out within fifteen meters of the three access points with a step size of one meter at a test bed environment in the Faculty of Engineering Building; Block A, Nnamdi Azikiwe University Awka, Anambra State, Nigeria. The Netstumbler software installed in a laptop (DELL. Model: Inspiron E1505) with an inbuilt wireless interface card was used in recording the received signal strength from each of the access points. Results obtained showed that the received signal strength decreases as the distance of the receiver increases from the transmitting point and the developed mathematical signal model can be used as a valuable tool for accurate prediction of the received signal strength in the testbed environment.

An Online Jewelry Cert Print Service Based on Web Service

For implementing jewelry cert print in the web environment, based on web service and ActiveX technology, an online jewelry cert print service is proposed by encapsulation the jewelry cert print module running in the local network environment. The service can implement the cert typesetting and batch print, and can communicate soap message with other web services. The practicability and validity of the online jewelry cert print service is verified through the application in the jewelry inspection business management system.

A Service Oriented Jewelry Image Watermarking Encryption Method

To avoiding tampering with jewelry image, based on web service technology, a jewelry image watermarking encryption service is proposed by encapsulating the watermarking encryption module which runs in the local network environment. The service can judge the barcode regions in the jewelry image, and get the jewelry serial number by analyzing the barcode content. The practicability and validity of the watermarking encryption service is verified through the application in the jewelry inspection business management system.

The Impact of Information Provision on Agglomeration Bonus Performance: An Experimental Study on Local Networks

The agglomeration bonus is an incentive mechanism to induce adjacent landowners to spatially coordinate their land use for the delivery of ecosystem services from farmland. This paper uses laboratory experiments to explore the performance of the agglomeration bonus in achieving the socially optimal land management configuration in a local network environment where the information available to subjects varies and the strategic setting is unfavorable for efficient coordination. The experiments indicate that if subjects are informed about both their direct and indirect neighbors' actions, they are more likely to produce the socially optimal configuration. Thus effectiveness of the policy can be improved by implementing information dissemination exercises among landowners. However given the adverse strategic setting, increased game experience leads to coordination failure and optimal land choices only at the localized level independent of the information available to subjects. Thus success of the agglomeration bonus scheme on real landscapes will have to take account of the roles of both information and experience on participant behavior.

Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression

Gene expression analysis is often used to investigate the molecular and functional underpinnings of a phenotype. However, differential expression of individual genes is limited in that it does not consider how the genes interact with each other in networks. To address this shortcoming we propose a number of network-based analyses that give additional functional insights into the studied process. These were applied to a dataset of sex-specific gene expression in the chicken gonad and brain at different developmental stages. We first constructed a global chicken interaction network. Combining the network with the expression data showed that most sex-biased genes tend to have lower network connectivity, that is, act within local network environments, although some interesting exceptions were found. Genes of the same sex bias were generally more strongly connected with each other than expected. We further studied the fates of duplicated sex-biased genes and found that there is a significant trend to keep the same pattern of sex bias after duplication. We also identified sex-biased modules in the network, which reveal pathways or complexes involved in sex-specific processes. Altogether, this work integrates evolutionary genomics with systems biology in a novel way, offering new insights into the modular nature of sex-biased genes.

An IoT Anomaly Detection Model Based on Artificial Immunity

To resolve the anomaly detection problem in the distributed environment of the Internet of Things (IoT), an artificial immunity-based anomaly detection model for the IoT is proposed in this paper. The proposed model adopts artificial immune mechanisms to recognize anomaly behavior of IoT security threats. It is consisted of Anomaly detection agents (ADA) and Central Service System (CSS). ADA is deployed by the IoT gateway. It collects the initial data of the sense layer of the IoT. It works independently and produces excellent detection elements. It shares its excellent detection elements with the other ones and uploads them to the CSS. Theory analysis shows that the proposed model is able to adapt the local network environment of IoT and improve the anomaly detection ability in the global IoT environment.

Reflectance properties of polymer‐stabilised cholesteric liquid crystals cells with cholesteryl compounds of different functionality

In this paper, we describe the synthesis of cholesteryl compounds of different functionality. A series of polymer‐stabilised cholesteric liquid crystal (PSCLC) cells are prepared by photo‐polymerisation of a cholesteric liquid crystal (Ch‐LC) mixture containing a non‐reactive liquid crystal, a nematic diacrylate and the above cholesteryl compounds. The effect of cholesteryl compound functionality on the morphology and the reflectance properties of PSCLC cells is evaluated, as are parameters such as the polymerisation temperature. The results indicate that the higher functional cholesteryl compound is more effective for broadening the reflection band of the Ch‐LC composites, which is speculated to be a result of the pitch differences in the local network environment. Scanning electron microscopy is used to examine the morphologies of the polymer network of the PSCLC cells. We have found that the morphologies of the polymer network are determined by the functionality of the cholesteryl compounds as well as polymerisation temperature, which further influence the reflectance properties of the composites.