Head Data Research Articles

Quantum intrusion detection system using outlier analysis

In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks.

Separable Reversible Data Hiding in Encrypted Images for Remote Sensing Images.

High security and effectiveness are critical performance metrics in the data transmission process for satellite remote sensing images, medical images, and so on. Previously, the receiver could gain a high-quality cover image (lossy) after decryption in a separable manner to balance embedding capacity (EC) and security. Completely separable, reversible data hiding in encrypted image (SRDH-EI) algorithms are proposed to address this issue. In this study, the cover image was preprocessed at the sender's end. The pre-embedded pixels and most significant bits (MSB) were compressed via two coding methods to reserve space. Additionally, the header data were embedded for marking. Finally, auxiliary data and secret data were embedded in a forward "Z" and reverse "Z" shape before and after encryption, respectively. The receiver could extract secret data and decrypt the cover image separately using the keys and markers. The experimental results demonstrate that the algorithm reached a high EC for remote sensing images by utilizing pixel correlation at multiple positions within the groups. The cover image could maintain its entropy during the data embedding process, ensuring security. The decrypted image could be recovered without distortion, furthermore, the receiver could achieve complete separability, so it has good application prospects for remote sensing images.

A Self-adaptive and Secure Approach to Share Network Trace Data

A large volume of network trace data are collected by the government and public and private organizations and can be analyzed for various purposes such as resolving network problems, improving network performance, and understanding user behavior. However, most organizations are reluctant to share their data with any external experts for analysis, because they contain sensitive information deemed proprietary to the organization, thus raising privacy concerns. Even if the payload of network packets is not shared, header data may disclose sensitive information that adversaries can exploit to perform unauthorized actions. So network trace data need to be anonymized before being shared. Most of the existing anonymization tools have two major shortcomings: (1) they cannot provide provable protection, and (2) their performance relies on setting the right parameter values such as the degree of privacy protection and the features that should be anonymized, but there is little assistance for a user to optimally set these parameters. This article proposes a self-adaptive and secure approach to anonymize network trace data and provides provable protection and automatic optimal settings of parameters. A comparison of the proposed approach with existing anonymization tools via experimentation demonstrated that the proposed method outperforms the existing anonymization techniques.

An Ensemble of Text Convolutional Neural Networks and Multi-Head Attention Layers for Classifying Threats in Network Packets

Using traditional methods based on detection rules written by human security experts presents significant challenges for the accurate detection of network threats, which are becoming increasingly sophisticated. In order to deal with the limitations of traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet payloads through natural language processing techniques to detect attack intent. However, due to the nature of packet payloads that contain binary and text data, a new approach is needed that goes beyond typical natural language processing techniques. In this paper, we study a token extraction method optimized for payloads using n-gram and byte-pair encoding techniques. Furthermore, we generate embedding vectors that can understand the context of the packet payload using algorithms such as Word2Vec and FastText. We also compute the embedding of various header data associated with packets such as IP addresses and ports. Given these features, we combine a text 1D CNN and a multi-head attention network in a novel fashion. We validated the effectiveness of our classification technique on the CICIDS2017 open dataset and over half a million data collected by The Education Cyber Security Center (ECSC), currently operating in South Korea. The proposed model showed remarkable performance compared to previous studies, achieving highly accurate classification with an F1-score of 0.998. Our model can also preprocess and classify 150,000 network threats per minute, helping security agents in the field maximize their time and analyze more complex attack patterns.

Implementation of Efficient Intrusion Detection of Imbalanced Network Traffic using Deep Learning

A crucial component of network security is intrusion detection, but this task can be difficult when dealing with statistics on the distribution of network traffic, where benign cases exceed malicious ones by a large margin. The goal of this project is to create a powerful intrusion detection system using a recurrent neural network (RNN) to address this problem. RNNs are excellent at identifying temporal dependencies in sequential data, which makes them useful for examining network traffic patterns. The suggested methodology calls for preprocessing the data, balancing the dataset, feature extraction, choosing an RNN model, training, assessing, adjusting, and monitoring in real time. The network traffic data is cleaned and formatted during the preprocessing stage, and balancing algorithms make sure the RNN is trained on a representative dataset. It then extracts pertinent aspects, including behavioral patterns and packet header data. Effective intrusion detection requires the use of an appropriate RNN architecture, such as LSTM or GRU. The model is subsequently trained, assessed using a range of criteria, and refined through iterations. The optimized RNN model is then implemented for real-time intrusion detection, monitoring network traffic continuously and producing alerts for probable intrusions. This research seeks to improve the precision and efficacy of network security systems, maintaining the integrity of crucial information in the linked world of today, by utilizing RNNs for intrusion detection in imbalanced network trafficking. Keywords IDS, Imbalanced Network Traffic, Deep Learning, Recurrent Neural Network.

End-to-end communication protocol in IoT-enabled ZigBee network: Investigation and performance analysis

ZigBee is the prominent technology on the Internet of Things that enables smart applications and services. However, this technology forms personal area networks, which will not directly communicate with internet end users. The IoT-enabled Zigbee devices cannot handle the IPv6 packets, which have a maximum packet size of 1280 bytes, and the transmission of IPv6 packets over ZigBee-based IEEE 802.15.4 networks, which will be performed using a gateway. via the ZigBee coordinator. The gateways and the ZigBee coordinator must complete the neighbor discovery process, which increases the coordinator’s complexity. Additional ZigBee devices suffer from header size problems, routing structures, and data forwarding issues. This paper presents an efficient end-to-end communication protocol for IoT-enabled ZigBee devices and with an Internet host using the 6LoWPAN protocol. This 6LoWPAN connects IP-based infrastructures with a collection of Zigbee devices that provide end-to-end communication to route the IPv6 packets into ZigBee-enabled IoT networks. Next, we proposed the RPL-AODV routing protocol, which refers to the capacity to forward or route the data packets from one Zigbee device to the 6LoWPAN Boarder Router (6BR) via several hops. This proposed routing protocol combines the advantages of RPL and AODV routing protocols in ZigBee devices of IoT networks that establish the path from the origin node to the target node on an on-demand basis. Finally, we evaluated the performance of this protocol with various metrics and found improved results compared to existing protocols.

A smart packet type identification scheme for selective discard of video packets

In recent years, due to the growth of social networks and development of video sharing platforms, transmission of video content has increased dramatically. This huge amount of data imposes a heavy load on the limited bandwidth of the networks. As a result, data loss will be inevitable. Nevertheless, since some types of video frames, such as B-frames are more resilient to packet loss, discarding them can ease network congestion while maintaining QoE. However, frame-type information is only available in the packet payload. Limited processing power of devices in high-speed networks, data encryption and user credibility issues, make finding the frame types a costly operation. Therefore, an intelligent router with fast and reliable decision-making is proposed to model live video streams structures and recognize the importance of video packets at the network level. This information enables the network routers to discard insignificant video packets ahead of congestion. The proposed method does not need to read the IP layer payload and uses only the packet header data for decision making. Experimental results show an intelligent router, at the worst scenario can classify packet type with better than 95.1% accuracy, as if packet types are marked at the source without any bias. Tests on various HD video, with 1–10% packet drop rates, shows while the proposed packet discarding scheme is 0.8–2.7 dB inferior to ground truth method, it is still 6–10 dB better than dropping packets at random.

The development of phishing during the COVID-19 pandemic: An analysis of over 1100 targeted domains

To design preventive policy measures for email phishing, it is helpful to be aware of the phishing schemes and trends that are currently applied. How phishing schemes and patterns emerge and adapt is an ongoing field of study. Existing phishing works already reveal a rich set of phishing schemes, patterns, and trends that provide insight into the mechanisms used. However, there seems to be limited knowledge about how email phishing is affected in periods of social disturbance, such as COVID-19 in which phishing numbers have quadrupled. Therefore, we investigate how the COVID-19 pandemic influences the phishing emails sent during the first year of the pandemic. The email content (header data and html body, excl. attachments) is evaluated to assess how the pandemic influences the topics of phishing emails over time (peaks and trends), whether email campaigns correlate with momentous events and trends of the COVID-19 pandemic, and what hidden content revealed. This is studied through an in-depth analysis of the body of 500.000 phishing emails addressed to Dutch registered top-level domains collected during the start of the pandemic. The study reveals that most COVID-19 related phishing emails follow known patterns indicating that perpetrators are more likely to adapt than to reinvent their schemes.

Technical note: Four-year experience with utilization of DICOM metadata analytics in clinical digital radiography practice.

Digital radiography (DR) still presents many challenges and could have complex imaging acquisition and processing patterns in a clinical practice hindering quality standardization. This technical note aims to report the 4-year experience with utilizing a custom DICOM metadata analytics program in clinical DR at a large institution. Thirty-eight DR systems of three vendors at multiple locations were configured to automatically send clinical DICOM images to a DICOM receiver. A suite of custom MATLAB programs was established to extract and store public and private header data weekly. Specific use cases are provided for systematic image acquisition investigation, image processing harmonization, exposure index (EI) longitudinal monitoring and EI target optimization. For systematic acquisition investigation, an example of adult lumbar spine exam analysis was provided with statistics on manual acquisition versus the use of automatic exposure control (AEC, including AEC dose level, active cell, and backup timer), grid usage, and collimation for various projections. For processing harmonization, up to 12.6% of protocols were revealed to have processing parameter differences in an example of a mobile radiography fleet. In addition, inconsistent use of a post-acquisition image size function was also demonstrated, which resulted in anatomy size display variations. Bimonthly monitoring of median EI values showed expected trends, including changes after an AEC dose level adjustment for adult posterior-anterior chest imaging on a scanner system. An example of adult axillary shoulder EI target refinement was shared using the median value, eμ , based on the lognormal EI data distribution after parsing down to acquisitions with appropriate techniques. This analytics program enables systematic analysis of image acquisition and processing details. The information provides invaluable insights into real practice patterns, which can support data-driven quality standardization and optimization.

Adaptive symbol-by-symbol decision feedback threshold detection receiver to suppress oceanic turbulence scintillation effect

In this paper, an adaptive symbol-by-symbol decision feedback (DFB) threshold detection receiver is proposed that can suppress the scintillation effect of oceanic turbulence in underwater wireless optical communication (UWOC) system. The DFB receiver has a simple structure and can estimate the instantaneous channel state information (CSI) through the previously detected signal after adding pilot-symbols to the transmission data header. It thereby adaptively adjusts the decision threshold of the UWOC system receiver. The DFB receiver is independent of perfect CSI (PCSI). The channel model and transmitted signal power information are not essential for it. Moreover, the channel bandwidth can accommodate the inserted pilot-symbols. Using the gamma–gamma oceanic turbulence model, the performance of the DFB receiver under intensity fluctuation scintillation index σI2=0.15 and σI2=1.60 with different signal-to-noise ratios (SNRs) γ and different pilot-symbol lengths L was simulated. The results showed that the performance of the proposed DFB receiver was highly similar to the performance of the ideal receiver with PCSI known in advance. When σI2=1.60, γ=33dB, L=10, the bit error rate performance of the decision threshold TDFB of the DFB receiver was the same as that of the decision threshold TPCSI of the PCSI receiver, and the SNR gain loss was 0 dB. Furthermore, a wavelet denoising method was designed to improve the DFB receiver performance. When the sampling frequency of the DFB receiver was 25 MHz, σI2=1.60, γ=24dB, L=10, the proposed wavelet denoising preprocessing could obtain a SNR gain of 2.1 dB. These findings suggest that the proposed DFB receiver can promote the development of real-time applications of UWOC system.

Research on Architecture of Digital Bond Trading Contract System Based on Blockchain Technology

The traditional identification method of the digital bond trading contract system is single chain identification. Due to a large number of digital bond trading users, single chain identification will affect the performance of the system in the trading process. Therefore, we aim to propose a digital bond trading contract system based on blockchain technology and study the system architecture. In this paper, the architecture of the bond trading system is optimized by using the technology of blockchain, and the software architecture is developed by using the technology of blockchain. Through the performance test of the system, the advantages of the system architecture in practical application are verified. Firstly, the overall hardware architecture of the system is designed, and the internal structure of the controller in the control layer is redeployed. Combining the Mork tree and Patricia tree, the data structure based on blockchain and the block header data structure of the transaction contract system are optimized. By analyzing the operation process of the smart contract, the process of the smart contract consensus algorithm is optimized. The system performance test results show that the performance of the designed system in system transaction throughput, transaction delay, and system security is better than the traditional system, which verifies the effectiveness and reliability of the designed system.

Photonic neuromorphic technologies in optical communications

Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.

Applying neural networks to predict HPC-I/O bandwidth over seismic data on lustre file system for ExSeisDat

HPC or super-computing clusters are designed for executing computationally intensive operations that typically involve large scale I/O operations. This most commonly involves using a standard MPI library implemented in C/C++. The MPI-I/O performance in HPC clusters tends to vary significantly over a range of configuration parameters that are generally not taken into account by the algorithm. It is commonly left to individual practitioners to optimise I/O on a case by case basis at code level. This can often lead to a range of unforeseen outcomes. The ExSeisDat utility is built on top of the native MPI-I/O library comprising of Parallel I/O and Workflow Libraries to process seismic data encapsulated in SEG-Y file format. The SEG-Y File data structure is complex in nature, due to the alternative arrangement of trace header and trace data. Its size scales to petabytes and the chances of I/O performance degradation are further increased by ExSeisDat. This research paper presents a novel study of the changing I/O performance in terms of bandwidth, with the use of parallel plots against various MPI-I/O, Lustre (Parallel) File System and SEG-Y File parameters. Another novel aspect of this research is the predictive modelling of MPI-I/O behaviour over SEG-Y File benchmarks using Artificial Neural Networks (ANNs). The accuracy ranges from 62.5% to 96.5% over the set of trained ANN models. The computed Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values further support the generalisation of the prediction models. This paper demonstrates that by using our ANNs prediction technique, the configurations can be tuned beforehand to avoid poor I/O performance.

The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research

Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomic, and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical computed tomography (CT) and magnetic resonance imaging (MRI) scans from stroke patients worldwide, based on the open source XNAT imaging informatics platform. The aims of this repository are to: (i) store, manage, process, and facilitate sharing of high-value stroke imaging data sets, (ii) implement containerized automated computational methods to extract image characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomic, and clinical data to perform large-scale analysis of complications after stroke; and (iv) develop SNIPR as a collaborative platform aimed at both data scientists and clinical investigators. Currently, SNIPR hosts research projects encompassing ischemic and hemorrhagic stroke, with data from 2,246 subjects, and 6,149 imaging sessions from Washington University’s clinical image archive as well as contributions from collaborators in different countries, including Finland, Poland, and Spain. Moreover, we have extended the XNAT data model to include relevant clinical features, including subject demographics, stroke severity (NIH Stroke Scale), stroke subtype (using TOAST classification), and outcome [modified Rankin Scale (mRS)]. Image processing pipelines are deployed on SNIPR using containerized modules, which facilitate replicability at a large scale. The first such pipeline identifies axial brain CT scans from DICOM header data and image data using a meta deep learning scan classifier, registers serial scans to an atlas, segments tissue compartments, and calculates CSF volume. The resulting volume can be used to quantify the progression of cerebral edema after ischemic stroke. SNIPR thus enables the development and validation of pipelines to automatically extract imaging phenotypes and couple them with clinical data with the overarching aim of enabling a broad understanding of stroke progression and outcomes.

Simulation Application of VR Technology in Communication Network

With the further improvement of the level of informatization, the three-dimensional visualization technology of coal mine safety production has always been a hot issue of current research, among which virtual reality technology is the key technology to achieve three-dimensional visualization. This research mainly discusses the simulation application of VR technology in communication network. First of all, the exchange of data in communication is done in the form of data packets. The data packet transmitted between processes is in binary format, and the content of the data packet is composed of a data header and data content. Then determine the location of the roadway, equipment, and cable to be constructed, and design the object model in 3DS Max. Finally, in order to realize the docking and integration of the digital platform of the whole mine, VIR tools are used to develop the virtual scene platform. In the open-phase fault simulation experiment, the voltage and current parameters of the fault phase are all zero, and the diagnosis result is greater than 0.8. This research improves the management efficiency of power supply and distribution network through real-time communication of mine grid data and integrated display and control of virtual scenes.

Research on the Optimization Model of Blockchain Hierarchical Proxy

In the decentralized blockchain, each node stores the same data copy that costs a lot of repeated storage space to store valuable information. It makes storage efficiency insufficient. Meanwhile, each node needs to exchange the huge messages to create and verify the block. With rapid increasing in the number of communication nodes and communication frequency, it has caused a large network load. In order to further improve the storage efficiency and decrease the network load, a hierarchical proxy model is proposed. Firstly, designing a hierarchical proxy model which accurately divides normal nodes and proxy node groups, and it performs directional division of tasks for different nodes. Secondly, in a small-scale management cluster composed of proxy nodes, cluster failure priority classification can effectively deal with the failure of proxy nodes. Through the hierarchical storage mode, the block header and body data are separated, and it uses global or local method to store. The hierarchical operation mode makes use of idle resources to make transaction verification and block creation run in parallel. On this basis, the proxy model adopts delay correction strategy and concentrated multiple correction strategy to further reduce the communication loss. Through malicious node blacklist, the node with malicious behavior is added to the blacklist, and it prevents the node from further making evil. Finally, the simulation experiments show that the hierarchical proxy model effectively resists malicious behaviors, reduces network load and has great advantages in terms of data storage efficiency.

File Fragment Classification using Content Based Analysis

One of the major components in Digital Forensics is the extraction of files from a criminal’s hard drives. To achieve this, several techniques are used. One of these techniques is using file carvers. File carvers are used when the system metadata or the file table is damaged but the contents of the hard drive are still intact. File carvers work on the raw fragments in the hard disk and reconstruct files by classifying the fragments and then reassembling them to form the complete file. Hence the classification of file fragments has been an important problem in the field of digital forensics. The work on this problem has mainly relied on finding the specific byte sequences in the file header and footer. However, classification based on header and footer is not reliable as they may be modified or missing. In this project, the goal is to present a machine learningbased approach for content-based analysis to recognize the file types of file fragments. It does so by training a Feed-Forward Neural Network with a 2-byte sequence histogram feature vector which is calculated for each file. These files are obtained from a publicly available file corpus named Govdocs1. The results show that content-based analysis is more reliable than relying on the header and footer data of files.

SIVIM Alpine – Database of high-mountain grasslands in the Iberian Peninsula

SIVIM Alpine (GIVD ID: EU-00-034) is a thematic database focused on vegetation plots from alpine grasslands of the Iberian Peninsula. The main aim of the database is to centralize historical and new vegetation plots of grassland-like communities above the treeline from Spanish mountains, the Pyrenees (including France and Andorra) and Serra da Estrela (Portugal). The database was registered in GIVD in December 2020, and it is currently available in EVA under semi-restricted regime. SIVIM Alpine includes both digitized relevés from the literature and unpublished data. Most of digitized relevés overlap with SIVIM (GIVD ID EU-00-004) but the header data and the geographical coordinates of SIVIM Alpine have been improved when possible. The database is routinely updated with new surveys conducted with GPS and detailed ecological data. Nowadays, SIVIM Alpine contains 6,420 vegetation plots corresponding to all phytosociological alliances described in the Iberian Peninsula for high-mountain grassland vegetation, 85% of them also classified at the association level. Plot size is available for 80% of the relevés. Plant taxonomy keeps the names provided by the original authors of the relevés, with an additional correspondence to Euro+Med and The Plant List, when possible. The database is continuously updated by revisiting the original sources. Different versions of the database have been used to vegetation analysis at national at continental scales. Abbreviations: EVA = European Vegeation Archive; GIVD = Global Index of Vegetation-Plot Databases; SIVIM = Iberian and Macaronesian Vegetation Information System.

Balkan Vegetation Database (BVD) – updated information and current status

The Balkan Vegetation Database (BVD; GIVD ID: EU-00-019) is a regional database, which was established in 2014. It comprises phytosociological relevés covering various vegetation types from nine countries of the Balkan Peninsula (Albania – 153 relevés, Bosnia and Herzegovina – 1715, Bulgaria – 12,282, Greece – 465, Croatia – 69, Kosovo – 493, Montenegro – 440, North Macedonia – 13 and Serbia – 2677). Currently, it contains 18,306 relevés (compared to 9.580 in 2016), and most of them (82.8%) are geo-referenced. The database includes both digitized relevés from the literature (65.6%) and unpublished data (34.5%). Plot size is available for 84.7% of all relevés. During the last four years some “header data information” was improved e.g. elevation (now available for 83.4% of all relevés), aspect (67.7%), slope (66%), total cover of vegetation (54.3%), cover of tree, shrub, herb, bryophyte and lichen layers (27.1%, 20.1%, 40.2%, 11.5% and 2.1%), respectively. Data access is either semi-restricted (65.6%) or restricted (34.4%). Most relevés (84.6%) are classified to syntaxa of different levels. The database has been used for numerous studies with various objectives from floristic, vegetation and habitat-related topics, to macroecological studies at the local, regional, national, continental and global levels. During the last four years, BVD data were requested from 111 different projects via the EVA and sPlot databases.

National infrastructure of phytosociological data storage of Ukraine: theoretical aspects

У статті охарактеризовані роль та перспективи використання сучасних фітосоціологічних баз даних для комплексних геоботанічних досліджень. На основі аналізу зарубіжного та вітчизняного досвіду створення такого роду інформаційних ресурсів обґрунтовано необхідність їх розбудови та стандартизації шляхом формування національної інфраструктури для введення, накопичення та зберігання фітосоціологічних даних України. Така інфраструктура передбачає розроблення та впровадження уніфікованих технічних протоколів та нормативно-методичних правил дигіталізації, компіляції, менеджменту та використання фітосоціологічних даних для комплексних метааналізів. Технічні протоколи в рамках однієї програмної оболонки мають об’єднати єдиний флористичний список для введення даних щодо видового складу фітоценозів, а також уніфіковану форму заголовних даних для додавання іншої інформації щодо рослинних угруповань. Базуючись на аналізі наявних флористичних списків, що використовуються українськими фітосоціологами обґрунтовано необхідність створення нового найбільш повного флористичного зведення із актуальними таксономічними даними, що періодично оновлюватимуться. Такий список має об’єднати усі відомі з території України таксони судинних та криптогамних рослин, бути максимально повним та коректним. Розроблена стандартна структура метаданих для введення інформації щодо вертикальної і горизонтальної будови угруповань, екологічних та фізико-географічних умов, созологічної цінності, а також біотопічної та синтаксономічної належності кожного геоботанічного опису. Запропоновано систему комунікації для отримання фітосоціологічної інформації необхідної для конкретних досліджень, а також можливі форми контролю за потоками та використанням інформації захищеної авторським правом. Розроблено форми доступу до даних, механізми їх отримання та перелік правил щодо регламентації дотримання норм авторського права та наукової етики під час використання даних у процесі аналізів.