Client-server Research Articles

A Federated Learning Protocol for Spiking Neural Membrane Systems.

Although deep learning models have shown promising results in solving problems related to image recognition or natural language processing, they do not match how the biological brain works. Some of the differences include the amount of energy consumed, the way neurons communicate, or the way they learn. To close the gap between artificial neural networks and biological ones, researchers proposed the spiking neural network. Layered Spiking Neural P systems (LSN P systems) are networks of spiking neurons used to solve various classification problems. In this paper, we study the LSN P systems in the context of a federated learning client-server architecture over horizontally partitioned data. We analyze the privacy implications of pre-trained LSN P systems through membership inference attacks. We also perform experiments to assess the performance of an LSN P system trained in the federated learning setup. Our findings suggest that LSN P systems demonstrate higher accuracy and faster convergence compared to federated algorithms based on either perceptron or spiking neural networks.

MATHEMATICAL MODELING OF SECURE MULTIPOINT VIDEO CONFERENCE SESSIONS USING MULTILAYER GRAPHS

The article describes a multilayer graph model for multipoint videoconference sessions. The model presented in this study allows to specify requirements for quality of video capture quality when transferred between terminals and multipoint control unit at the service layer, as well as assessing bandwidth performance and delay of datalink channels at the secure and transport network layer. The given multilayer graph model for videoconference system is designed taking into account the features of secure network configuration, in which several overlay network layers are distinguished over transportation networks. This model specifies a structure of multipoint videoconference session at service layer, at which videoconferencing terminals interoperate through server. Traffic flows generated during session are distributed according to routing rules at the secure network layer. A purpose to select the most optimal route configuration for the entire session includes a number of alternative methods to distribute traffic for each pair of client-server communication seems to be rather complicated. The study proposes an algorithm for searching the shortest path in the graph of a secure overlay network with a step-by-step projection fer each individual route section onto the underlying level of the transport network to find the shortest path within its boundaries. An example for solving a problem on finding route in regard to the overlay network requirements and the actual traffic flow through the transportation network is given. Minimization of the capacity usage of leased channels was chosen as the optimality criterion. Experiments were conducted to evaluate the session quality based on the objective methods for various video-stream parameters, and corresponding quantitative estimates of the required bandwidth in order to organize a session.

FedKD-IDS: A robust intrusion detection system using knowledge distillation-based semi-supervised federated learning and anti-poisoning attack mechanism

In the realm of the Internet of Things (IoT), there has been a notable increase in the development and efficacy of Intrusion Detection Systems (IDS) that leverage machine learning (ML). Specifically, Federated Learning-based IDSs (FL-based IDS) have witnessed significant growth. These systems aim to mitigate data privacy breaches and minimize the communication overhead associated with dataset collection. Limited hardware resources also pose a significant constraint, preventing numerous IoT devices from actively engaging in FL. However, despite these advancements, certain challenges persist in the research domain. Issues such as elevated communication overhead, the potential for recovering private data, non-independent and identically distributed (Non-IID) data and a scarcity of labeled data remain noteworthy concerns. Additionally, vulnerabilities exist in the server-client communication during the FL process, creating opportunities for attackers to execute poisoning attacks on the client side with relative ease. To address these challenges, our paper introduces a semi-supervised approach for FL-based IDS. Our approach, named FedKD-IDS, employs knowledge distillation with a voting mechanism in place of weighted parameter aggregation and incorporates an anti-poisoning method. We conducted experiments to evaluate the effectiveness of our approach across diverse scenarios, including scenarios with Non-IID and varying data distributions. Additionally, we investigated various rates of malicious collaboration to demonstrate their impact in the federated training process. The results obtained from the real-world N-BaIoT dataset indicate that our approach surpasses the performance of the state-of-the-art (SOTA) SSFL method. Especially, even in the context of a poisoning attack where 50% of all collaborators targeted label flipping attack, FedKD-IDS demonstrated an accuracy of 79%, surpassing SSFL, which achieved only 19.86%. Furthermore, the outcomes also validated that the FedKD-IDS method has the capability to exclude over 85% of malicious collaborators during the aggregation phase of the federated training process.

An Online Knowledge-Based Support System

Technology advances have touched all the fields of human life, and the educational system is not an exception. Students in Nigerian universities experience challenges in their early or later stages of school due to a lack of a knowledge-based support system. This results in massive failure and confusion in later days in school. A knowledge-based online support system has been developed and implemented in this paper to improve academic support services for students and faculty members in the university. The need for such a support system stems from the various academic problems that the students often experience, including a lack of answers to certain questions, which can result in academic failure, taking too long to graduate, or dropping out of the university altogether. The system is web-based; therefore, users can access the necessary information when needed, and, in most cases, it solves common academic issues in a short time using the web. Following the Agile software development methodology and employing a client-server architecture, the system was designed in a user-friendly and efficient manner. Highlights of the system include an extensive database of FAQs, real-time interactive services, and multi-typed query handling. Testing phases were performed and integrated, and modular testing showed that the system worked as intended, providing valid inputs and also flagging any invalid inputs without causing the system failures, and performing provisions for user corrections. The results suggest that the online support system could enhance the academic environment at AE-FUNAI and therefore would benefit students and staff members alike. Nevertheless, certain limitations still prevail, such as poor internet access, which calls for constant enhancement and provision of support to the system.

Characteristics, Barriers, and Facilitators of Virtual Decision-Making Capacity Assessments During the COVID-19 Pandemic: Online Survey.

With a growing older adult population, the number of persons with dementia is expected to rise. Consequently, the number of persons needing decision-making capacity assessments (DMCA) will increase. The COVID-19 pandemic has impacted how we deliver patient care including DMCAs with a much more rapid shift to virtual assessments. Virtual DMCAs offer patients and health care professionals distinct advantages over in-person delivery by improving reach, access, and timely provision of health care. However, questions have arisen as to whether DMCAs can be effectively conducted virtually. This study aimed to determine the characteristics, barriers, and facilitators of conducting virtual DMCA during the COVID-19 pandemic. We conducted an online survey among health care providers who perform DMCAs in Alberta from March 2022 to February 2023. The survey consisted of 25 questions on demographics, preferences, and experience in conducting DMCAs virtually, and risks and barriers to doing virtual DMCAs. The data were analyzed using descriptive statistics. There were 31 respondents with a mean age of 51.1 (SD 12.7) years. The respondents consisted of physicians (45.2%, 14/31), occupational therapists (29%, 9/31), and social workers (16.1%, 5/31), with a majority (93.6%, 29/31) based in Edmonton. The mean number of years of experience conducting DMCAs was 12.3 (SD 10.7), with a median of 8 DMCAs (IQR 18.5) conducted per year. Most respondents conduct capacity interviews, with a majority (55.2%, 16/29) being associated primarily with acute care services. Furthermore, 54.8% (17/31) were interested in conducting DMCAs virtually; however, only 25.8% (8/31) had administered DMCAs virtually. Barriers and facilitators to virtual DMCAs relate to patients' characteristics and environment (such as communication difficulties, hearing or visual impairment, language barriers, ease of use of technology, or cognitive impairment), technology and technical support (need for technical support in both the client's and assessor's sides, the unreliability of internet connection in rural settings, and the availability of high-fidelity equipment), and assessors' ability to perform DMCA's virtually (ability to observe body language, interact with the client physically when needed, and build rapport can all be affected when conducting a DMCA virtually). In terms of implications for clinical practice, it is recommended that the patient or caregiver be familiar with technology, have a stable internet connection, use a private room, not be recorded, use a standardized assessment template, and have a backup plan in case of technical difficulties. Conducting DMCAs virtually is a relatively infrequent undertaking. Barriers and facilitators to adequate assessment need to be addressed given that virtual assessments are time-saving and expand reach.

Environmental Condition Aware Super-Resolution Acceleration Framework in Server-Client Hierarchies

In the current landscape, high-resolution (HR) videos have gained immense popularity, promising an elevated viewing experience. Recent research has demonstrated that the video super-resolution (SR) algorithm, empowered by deep neural networks (DNNs), can substantially enhance the quality of HR videos by processing low-resolution (LR) frames. However, the existing DNN models demand significant computational resources, posing challenges for the deployment of SR algorithms on client devices. While numerous accelerators have proposed solutions, their primary focus remains on client-side optimization. In contrast, our research recognizes that the HR video is originally stored in the cloud server and presents an untapped opportunity for achieving both high accuracy and performance improvements. Building on this insight, this article introduces an end-to-end video CODEC-assisted super-resolution (E 2 SR+) algorithm, which tightly integrates the cloud server with the client device to deliver a seamless and real-time video viewing experience. We propose the motion vector search algorithm executed in a cloud server, which can search the motion vectors and residuals for a part of the HR video frames and then pack them as add-ons. We also design an auto-encoder algorithm to down-sample the residuals to save the bitstream cost while guaranteeing the quality of the residuals. Lastly, we propose a reconstruction algorithm performed in the client to quickly reconstruct the corresponding HR frames using the add-ons to skip part of the DNN computations. To implement the E 2 SR+ algorithm, we design corresponding E 2 SR+ architecture in the client, which achieves significant speedup with minimal hardware overhead. Given that the environmental condition varies in the server–client hierarchies, we believe that simply applying E 2 SR+ to all frames is irrational. Accordingly, we offer an environmental condition–aware system to chase the best performance while adapting to the diverse environment. In the system, we design a linear programming (LP) model to simulate the environment and allocate frames to three existing mechanisms. Our experimental results demonstrate that the E 2 SR+ algorithm enhances the peak signal-to-noise ratio by 1.2, 2.5, and 2.3 compared with the state-of-the-art (SOTA) methods EDVR, BasicVSR, and BasicVSR++, respectively. In terms of performance, the E 2 SR+ architecture offers significant improvements over existing SOTA methods. For instance, while BasicVSR++ requires 98 ms on an NVIDIA V100 graphics processing unit (GPU) to generate a 1,280 × 720 HR frame, the E 2 SR+ architecture reduces the execution time to just 39 ms, highlighting the efficiency and effectiveness of our proposed method. Overall, the E 2 SR+ architecture respectively achieves 1.4×, 2.2×, 4.6×, and 442.0× performance improvement compared with ADAS, ISRAcc, the NVIDIA V100 GPU, and a central processing unit. Lastly, the proposed system showcases its superiority and surpasses all the existing mechanisms in terms of execution time when varying environmental conditions.

RFL-LSU: A Robust Federated Learning Approach with Localized Stepwise Updates

Distributed intelligence enables the widespread deployment of AI technology, greatly promoting the development of AI. Federated learning is a widely used distributed intelligence technology that allows iterative optimization of global model while protecting user data privacy. Currently, federated learning faces some security threats, as its open architecture provides attackers with opportunities to disrupt the learning process by submitting malicious updates or inserting backdoors. In this article, we propose a robust federated learning method to defend against potential malicious attacks. Specifically, we enhance the algorithm’s performance and stability by implementing localized stepwise updates on the client side and element-wise anomaly detection on the server side. We conducted experiments in a more realistic non-i.i.d. scenario and compared the results with other typical federated learning methods. Our results demonstrate that our approach exhibits strong robustness under non-i.i.d. data distributions, outperforming other methods in terms of test accuracy and resilience to attacks.

Image Spam Detection through Pattern Recognition with IP Tracing Technique

Image spam is a kind of E-mail spam where the message text of the spam is presented as a picture in an image file. The basic rationale behind image spam is difficult to detect using text spam filtering methods which is designed to detect patterns in text in the plain-text E-mail body or attachments. A new trend in email spam is the emergence of image spam. The most previous works of image spam detection focused on filtering the image spam on the client side. This proposed system considered more desirable comprehensive solution which embraces the both server side filtering and client side detection methods. The spectral clustering algorithm is introduced to similarity measure for cluster analysis of spam images to filter the attack activities of spammers and fast trace back the spam sources. The active learning algorithm is limited where the learner guides the users to label as few images as possible while maximizing the classification accuracy.

Automatically Injecting Robustness Statements into Distributed Applications

When developing a distributed application, several issues need to be handled, and software components should include some mechanisms to make their execution resilient when network faults, delays, or tampering occur. For example, synchronous calls represent a too-tight connection between a client requesting a service and the service itself, whereby potential network delays or temporary server overloads would keep the client side hanging, exposing it to a domino effect. The proposed approach assists developers in dealing with such issues by providing an automatic tool that enhances a distributed application using simple blocking calls and makes it robust in the face of adverse events. The proposed devised solution consists in automatically identifying the parts of the application that connect to remote services using simple synchronous calls and substituting them with a generated customized snippet of code that handles potential network delays or faults. To accurately perform the proposed transformation, the devised tool finds application code statements that are data-dependent on the results of the original synchronous calls. Then, for the dependent statements, a solution involving guarding code, proper synchronization, and timeouts is injected. We experimented with the analysis and transformation of several applications and report a meaningful example, together with the analysis of the results achieved.

Networked microcontrollers for accessible, distributed spatial audio

State-of-the-art systems for spatial and immersive audio are typically very costly, being reliant on specialist audio hardware capable of performing computationally intensive signal processing and delivering output to many tens, if not hundreds, of loudspeakers. Centralised systems of this sort suffer from limited accessibility due to their inflexibility and expense. Building on the research of the past few decades in the transmission of audio data over computer networks, and the emergence in recent years of increasingly capable, low-cost microcontroller-based development platforms with support for both networking and audio functionality, we present a prototype decentralised, modular alternative. Having previously explored the feasibility of running a microcontroller device as a networked audio client, here we describe the development of a client-server system with improved scalability via multicast data transmission. The system operates on ubiquitous, commonplace computing and networking equipment, with a view to it being a simple, versatile, and highly-accessible platform, capable of granting users the freedom to explore audio spatialisation approaches at vastly reduced expense. Though faced by significant technical challenges, particularly with regard to maintaining synchronicity between distributed audio processors, the system produces perceptually plausible results. Findings are commensurate with a capability, with further development and research, to disrupt and democratise the fields of spatial and immersive audio.

PKM IMPROVEMENT COMPETENCY CURRICULUM UJI KOMPETENSI KEAHLIAN (UKK) AND NETWORK AUTOMATION TO IMPROVE SOFT SKILL ASPECTS AT SMKS NUSANTARA

The implementation of Program Pengabdian kepada Masyarakat (PKM), to improve the competence of SMK TKJ Nusantara 1 Ciputat students in terms of skills and soft skills in facing the Uji Kompetensi Keahlian (UKK) through a series of structured network training and practicum activities. The methods used include theoretical training and intensive practicum implementation that focuses on 1) Network Security Design, Installation and Configuration of Dynamic Routing 2) Wired and Wireless Network Design with VLAN and Routing 3) Troubleshooting Network Services on Client Server Networks 4) Troubleshooting Network Security on WAN Networks 5) Network Automation on MikroTik routers using Paramiko and Netmiko Python. The results achieved showed a significant increase in the technical skills and soft skills of the students. The final scores of the student groups ranged from 8.05 to 9.15. These results indicate that each student is increasingly mastering the technical skills needed for the configuration and management of questions and assignments tested on the UKK. The results of this PKM activity also include the preparation of UKK modules and writing books for Network automation on MikroTik RouterOS using Python, which is intended as a reference and ongoing learning material for students and teachers in practical learning and teaching activities.

Flexible hybrid post-quantum bidirectional multi-factor authentication and key agreement framework using ECC and KEM

Post-quantum computing becomes a real threat in the coming years, resulting in vulnerable security protocols that rely on traditional public key algorithms. It is not evident to provide protection against it in a cost-efficient manner, especially for Internet of Things (IoT) devices with limited capabilities. There is a high variety of IoT applications, some require only short-term security (e.g. agriculture) and others long-term security (e.g. healthcare). In order to provide a unified security approach for such heterogenity in IoT, we propose a flexible hybrid authentication and key agreement framework for a client–server architecture, which relies both on the classical elliptic curve cryptography (ECC) and on a quantum secure key encapsulation mechanism (KEM). There are five versions that can be derived from the framework, going from a fully hybrid, and partial hybrid to classical construction. The trade-off between performance and security strength is demonstrated for each of these versions. The overall cost of the protocols is highly reduced thanks to the usage of multifactors in the authentication process, both on the user side by means of biometrics and the device side by means of physically unclonable functions (PUFs). We show that both Kyber and Mc Elience as KEM can offer reasonable performance, depending on the situation. The unified framework offers optimal security protection against the most well-known attacks.

IS-USG: INTELLIGENCE SYSTEM ULTRASONOGRAPHY Machine Learning Implementation for Recognition of Pregnant Sheep in Gumukmas Multifarm Jember

In 2023, the government's program related to food independence emphasizes the importance of meat independence, including goat meat. CV. Gumukmas Multi Farm Jember faces several challenges in managing pregnant sheep, such as early detection of pregnancy, preventing abortion, and managing sheep health history. Therefore, the Intelligence System Ultrasonography (IS-USG) system was developed to detect sheep pregnancy more accurately and integrate medical data with the help of machine learning. This system allows automatic monitoring of pregnancy conditions and fetal health, and recommends interventions such as probiotic feeding. IS-USG is implemented using a web and mobile-based client-server architecture, with Laravel and MySQL backends, and RESTful API integration for real-time data synchronization. Testing was conducted to evaluate the effectiveness of the system, and initial results showed that this system can improve sheep reproductive management, accelerate pregnancy detection, and minimize the risk of birth failure.

Event log extraction methodology for Ethereum applications

The adoption of smart contracts in decentralized blockchain-based applications enables reliable and certified audits. These audits allow the extraction of valuable information from blockchains, which can be used to reconstruct the execution of the application and facilitate advanced analyses. One of the most commonly used techniques in this context is process mining, which leverages event logs to trace and accurately represent the process execution of applications. However, extracting execution data from blockchains poses significant challenges, and the current methodologies developed have some limitations. Most approaches are tailored to specific use cases, requiring that analysis techniques are defined during the smart contract’s development. Other techniques are applied a posteriori, relying on blockchain events that often lack a standardized format. This absence of standardization requires complex processing steps to correlate logs with the executed actions and such approaches are not universally applicable to all smart contracts on the blockchain, further limiting their scope. Lastly, none of the existing techniques can extract information from event logs embedded in internal transactions of smart contracts.To address these limitations, we propose EveLog an application-agnostic methodology that can be applied to any EVM-compatible application without predefined constraints. Its primary goal is to extract information from smart contracts, capturing both public and internal transactions, and organizing the results into a structured XES event log. The EveLog methodology consists of five key steps: (i) extraction of data from smart contract transactions, (ii) decoding raw data, (iii) selection of sorting criteria, (iv) construction of traces, and (v) generation of the XES event log. EveLog has been implemented in a client–server application and tested on existing solutions, specifically the CryptoKitties application, a blockchain-based game on the Ethereum blockchain. The study was conducted using 12,996 blocks, including over 8000 real transactions from the Ethereum mainnet.

A complete client–server architecture providing prevention from insider attack using homogeneous multi-core RSA crypto-processor

A complete client–server architecture providing prevention from insider attack using homogeneous multi-core RSA crypto-processor

Smart-KG: Partition-Based Linked Data Fragments for querying knowledge graphs

RDF and SPARQL provide a uniform way to publish and query billions of triples in open knowledge graphs (KGs) on the Web. Yet, provisioning of a fast, reliable, and responsive live querying solution for open KGs is still hardly possible through SPARQL endpoints alone: while such endpoints provide a remarkable performance for single queries, they typically can not cope with highly concurrent query workloads by multiple clients. To mitigate this, the Linked Data Fragments (LDF) framework sparked the design of different alternative low-cost interfaces such as Triple Pattern Fragments (TPF), that partially offload the query processing workload to the client side. On the downside, such interfaces still come with the expense of unnecessarily high network load due to the necessary transfer of intermediate results to the client, leading to query performance degradation compared with endpoints. To address this problem, in the present work, we investigate alternative interfaces, refining and extending the original TPF idea, which also aims at reducing server-resource consumption, by shipping query-relevant partitions of KGs from the server to the client. To this end, first, we align formal definitions and notations of the original LDF framework to uniformly present existing LDF implements and such “partition-based” LDF approaches. These novel LDF interfaces retrieve, instead of the exact triples matching a particular query pattern, a subset of pre-materialized, compressed, partitions of the original graph, containing all answers to a query pattern, to be further evaluated on the client side. As a concrete representative of partition-based LDF, we present smart-KG+, extending and refining our prior work (In WWW ’20: The Web Conference 2020 (2020) 984–994 ACM / IW3C2) in several respects. Our proposed approach is a step forward towards a better-balanced share of the query processing load between clients and servers by shipping graph partitions driven by the structure of RDF graphs to group entities described with the same sets of properties and classes, resulting in significant data transfer reduction. Our experiments demonstrate that the smart-KG+ significantly outperforms existing Web SPARQL interfaces on both pre-existing benchmarks for highly concurrent query execution as well as an accustomed query workload inspired by query logs of existing SPARQL endpoints.

Efficient federated learning for pediatric pneumonia on chest X-ray classification

According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia.

Web-платформа для організації хмарних обчислень у нейрофізіологічних дослідженнях на основі даних айтрекінгу

The purpose of the work is to develop the architecture and web version of the software complex based on the proposed new concept of cloud computing organization, which allows to expand the diagnostic capabilities of the tools of model-oriented information technology for assessing the neurophysiological state of a person using methods of nonlinear dynamic identification of the oculomotor system based on eye tracking data. The concept of cloud computing is proposed, which is based on the combination of PaaS and SaaS services as part of the developed software complex, due to which the cross-platform nature of cloud computing is ensured, the productivity and efficiency of scientific research increases. The developed architecture allows you to easily expand the functionality of the software complex and adapt it to different application conditions. The key feature of the complex is its undemanding hardware on the client side thanks to the use of cloud computing and its modular structure, which allows for easy scaling of the complex, as well as the isolation of the script code execution process in the cloud computing environment to increase the level of security when interpreting the script code on the server . Compared to other similar services, the complex has several advantages: it provides effective work in research and educational applications, supports Python and JavaScript programming languages, and also allows the use of software-implemented identification methods through specially developed GUI interfaces. In addition, the complex offers social opportunities and a high level of abstraction, which allows to optimize the research process.

Federated influencer learning for secure and efficient collaborative learning in realistic medical database environment.

Enhancing deep learning performance requires extensive datasets. Centralized training raises concerns about data ownership and security. Additionally, large models are often unsuitable for hospitals due to their limited resource capacities. Federated learning (FL) has been introduced to address these issues. However, FL faces challenges such as vulnerability to attacks, non-IID data, reliance on a central server, high communication overhead, and suboptimal model aggregation. Furthermore, FL is not optimized for realistic hospital database environments, where data are dynamically accumulated. To overcome these limitations, we propose federated influencer learning (FIL) as a secure and efficient collaborative learning paradigm. Unlike the server-client model of FL, FIL features an equal-status structure among participants, with an administrator overseeing the overall process. FIL comprises four stages: local training, qualification, screening, and influencing. Local training is similar to vanilla FL, except for the optional use of a shared dataset. In the qualification stage, participants are classified as influencers or followers. During the screening stage, the integrity of the logits from the influencer is examined. If the integrity is confirmed, the influencer shares their knowledge with the others. FIL is more secure than FL because it eliminates the need for model-parameter transactions, central servers, and generative models. Additionally, FIL supports model-agnostic training. These features make FIL particularly promising for fields such as healthcare, where maintaining confidentiality is crucial. Our experiments demonstrated the effectiveness of FIL, which outperformed several FL methods on large medical (X-ray, MRI, and PET) and natural (CIFAR-10) image dataset in a dynamically accumulating database environment, with consistently higher precision, recall, Dice score, and lower standard deviation between participants. In particular, in the PET dataset, FIL achieved about a 40% improvement in Dice score and recall.

Software tools for organizing cloud computing in psychophysiological research based on eyetracking data

The architecture and web version of the software complex have been developed, which significantly expands the diagnostic capabilities of model-oriented information technologies for the assessment of the neurophysiological state. The complex provides cross-platform cloud computing, increases the productivity and efficiency of scientific research, using methods of non-parametric identification of the oculomotor system based on eyetracking data, which is achieved thanks to a new concept of cloud computing organization. Cloud computing technology has been further developed thanks to the proposed concept that combines the principles of PaaS (Platform as a Service) and SaaS (Software as a Service). The key feature of this software complex is its undemanding hardware on the client side thanks to the use of cloud computing, and its modular structure, which allows it to be easily scaled. Compared to other similar services, the complex has several advantages: it provides effective work in research and educational areas, supports several programming languages for improving algorithms, and also allows the use of ready-made identification methods through specially developed GUI interfaces. In addition, it offers social capabilities and a high level of abstraction that optimizes the research process.