=Paper=
{{Paper
|id=Vol-2853/paper47
|storemode=property
|title=Authentication and Request Processing Model in High Load Modes for IoT Components
|pdfUrl=https://ceur-ws.org/Vol-2853/paper47.pdf
|volume=Vol-2853
|authors=Sergii Surkov,Oleksandr Martynyuk
|dblpUrl=https://dblp.org/rec/conf/intelitsis/SurkovM21
}}
==Authentication and Request Processing Model in High Load Modes for IoT Components==
https://ceur-ws.org/Vol-2853/paper47.pdf
Authentication and request processing model in high load
modes for IoT components
Sergii Surkov a, Oleksandr Martynyuk a
a
Odessa National Polytechnic University, Avenue Shevchenko, 1, Odessa, 65044, Ukraine
1
Abstract
The paper is dedicated to the development and study of models and methods of authorization
processes and high load modes for IoT components in the operation queue environment.
An essential part of authorization is the authentication of the transmitted data. In the trusted
nodes, there's a risk of an unauthorized modification of payloads. It poses a threat because
they may be infected with various viruses or botnets. Such nodes include trusted networks,
load balancers, and proxy servers, where the data is transmitted in an unencrypted form. In
the worst case, the attacker may even steal the private key of the TLS certificate. It enables
him not only to analyze all the traffic but also to modify the payload. Situations of data
spoofing at vulnerable points are poorly researched. A new "chunking" method was
previously proposed and compared to the existing ones. Analytical studies have shown an
increase in the performance of the new "chunking" method due to the more efficient usage of
resources.
For efficient resource management in the multithreaded environment, it is necessary to load
all the processor cores equally. A decent solution to take advantage of the multithreading
environment is the operation queues. However, if an operation queue can't process
accumulated operations efficiently, the system goes into high load mode. Such behavior may
cause the system to run out of memory. The patterns of occurrence of high load modes have
been researched previously. To prevent the high load modes, the load balancing of the server
cluster method has been further developed, which can also be applied to a single server.
The new model of authentication and request processing in high load modes for IoT
components has been created. It differs from the existing ones in the ability to prevent high
load modes during the authentication of large payloads. It uses the limitation in the total
bandwidth as well as the limitation of the number of requests. The information system for the
IoT components, which implements the new model has been developed. In the test, several
experiments were conducted, which show: high load mode, the effect of limitation of the
bandwidth, effect of the limitation of the number of requests.
Keywords
authorization protocol, payload authentication, digital signature, information technology,
server cluster, internet of things, smart car.
1. Introduction
The modern concept of web systems security focuses on systems without mandatory registration of
users, where the main focus is to determine whether a user is a real person or not. In that area machine
learning [1, 2] is the main focus of the study.
After user authorization, the main security aspect is the authentication of requests from the user to
the server. In this paper we use term authentication as an act of validating that requests are what they
IntelITSIS’2021: 2nd International Workshop on Intelligent Information Technologies and Systems of Information Security, March 24–26,
2021, Khmelnytskyi, Ukraine
EMAIL: k1x0r@ukr.net (S. Surkov); anmartynyuk@ukr.net (O. Martynyuk);
ORCID: 0000-0001-9224-7526 (S. Surkov); 0000-0003-1461-2000 (O. Martynyuk);
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
claim to be. And the term authorization as the general term of authorization protocol which consists of
authenticating user, authorizing or granting the permissions to the user, and authenticating requests.
With the increasing payload sizes, it's important to research authorization models and methods to
improve the efficiency of payload authentication. For the authentication of large payloads, there are
no protocols that verify it efficiently. To reduce resource consumption for authenticating payloads in
the multithreaded environment, operation queues are used, which allow the developer to reuse
previously created threads.
The most used protocol family of data transmission is HTTP[3, 4]. It's used for web browsers,
desktop applications, mobile clients, and IoT devices. Data transmission can occur through many
network nodes [5], which are trusted, but in some nodes, certificates can be substituted or data is
transmitted in unencrypted form. This makes it necessary to authenticate request payloads for video
streaming, document storage, database services with complex data center infrastructure, and more.
The generally accepted protocol of encryption of the data during transmission is TLS[6, 7].
Proxy resources are controlled by the companies in which the people work, or by data centers, and
generally they are considered to be trusted.
In the process of data verification in the operation queue environment, the high load modes may
arise, leading to unstable operation[8, 9] of the information system, and these issues are poorly
researched. This can lead to an increase in memory consumption and therefore to unstable operation
and termination of the server process. The behavior of high load modes for authorization protocols of
large payloads is poorly researched.
The formal analysis for existing authorization processes in information systems has been carried
out. The existing methods and models for ensuring the security of authorization processes in
information systems have been researched. It was found that the existing systems require a lot of
resources for authentication of large payloads.
In a multithreaded environment, it is important to distribute the workload across the processor
cores to efficiently manage resources. Operation queues [10, 11] are an efficient solution in a
multithreaded environment, which helps to distribute the workload among all the processor cores. On
the other hand, data changes during transmission can occur if no encryption is used or the data is
decrypted in transit.
A high load mode occurs when operations in the queue accumulate and do not have time to be
processed. It increases memory consumption, which can cause system instability. The means of
detecting and preventing such modes does not allow to get rid of them completely. The study of these
modes makes it possible to predict their impact on equipment and thereby increase the reliability[12,
13] of the system and reduce their negative effect.
The behavior of the high load modes themselves for authorization protocols for large requests is
poorly understood, and their study is relevant.
The purpose of this work is to improve performance and reduce the harmful effect of high load
modes in the operation queue environment.
To achieve this goal, the following tasks were completed:
- analyzed existing models, methods and information technologies, areas of application,
advantages, disadvantages of methods for request authentication
- analyzed high load modes for authorization protocols in the operation queue environment
- developed a model and method of the authentication of large payloads for HTTP and HTTP/2
protocols for blocking and non-blocking sockets
- developed methods for ranking authentication payload mechanisms
- developed methods for identifying and studying the influence of high load modes on the system
memory in the operation queue environment
- improved method of migration from a system with a single server to the server cluster
- authentication and request processing model in high load modes for IoT components has been
further developed
- the method of building communication mobile systems for embedded car computers that do not
have their own web browsers has been further developed
2. Related Works
To guarantee the immutability of the data during transmission, it's crucial to prevent MITM [14,
15] (Man in the Middle) attacks. The authentication of only the request headers by most authorization
protocols leaves the payload prone to modification. Such attacks are becoming more relevant because
of the increasing number of infected computers and botnets[16].
Protocols like OAuth 1.0a[17] and HAWK[18] use the HMAC method [19] to prevent the
modification of payloads. These protocols work great for the authentication of small payloads.
It's essential for authorization protocols to authenticate the payload, although some rely on TLS
encryption[20]. The generally used method is "filling the buffer" with its subtypes "buffering to
memory" and "buffering to file" for large and small data sizes. Analysis of existing methods for
verifying data integrity showed the necessity for scientific research - modeling existing authorization
protocols for authentication of the payload, creating a new model and method for authentication of
large payloads to improve the system performance.
A model and method of chunking authentication of large payloads have been developed, which
allow to achieve an increase in performance by reducing the number of operations. They differ from
existing ones by working directly with data chunks, and authenticating them as they appear.
2.1. The model of authentication of payloads by chunks
The model of authentication of payloads by chunks was developed [21] it differs from the existing
ones in the representation of the payload as the data stream in the form of chunks. The digital
signature is located in the end, which makes it possible to verify the data during transmission and
removes the necessity to read it again. The size of the chunk and the metadata block must be a
multiple of the block size of the hashing algorithm, and the remainder of the last chunk is filled with
zeros.
An example of a chunking payload authentication model is shown in Figure 1.
Figure 1: An example of a chunking payload authentication model
On the basis of the model, a new method of chunking payload authentication for large payloads
has been developed, which makes it possible to achieve an increase the performance by reducing the
number of operations. The new method has been implemented for HTTP authorization protocols,
which use HMAC for payload verification. This allows web services to authenticate large payloads
and not be exposed to denial-of-service attacks[22].
2.2. The method of authentication of payloads by chunks
In the "chunking" method, not all of the request data is buffered, but a small portion, which is used
to update digital signature of the request. This has the advantage of reducing CPU utilization for both
blocking and non-blocking sockets.
The "chunking" method gives an advantage in reduced CPU usage with both blocking and non-
blocking sockets. It has the best effect in server applications with non-blocking sockets. Despite the
blocking sockets are less efficient than non-blocking ones, the method still gives a huge advantage for
authentication of large payloads. And the blocking sockets might be feasible to use it in client
applications with existing libraries.
Using our method, it's possible to create or modify an HMAC-based HTTP authorization protocol
to enable support of large payload authentication. As a result, the "chunking" method provides an
ability for the servers to efficiently authenticate large payloads, which makes them less prone to
denial of service attacks.
The method of chunking payload authentication is shown in Figure 2.
Figure 2: The method of authentication of payloads by chunks
After the chunk is received, the digital signature is updated and processed further. The incoming
chunk size for the "Update digital signature" unit is irrelevant. It is possible because the mechanism of
this unit structures the incoming data stream according to the hashing algorithm block size.
The data processing flow for the "chunking" method is shown in Figure 3.
Figure 3: The data processing sequence for the "chunking" method
Because there's no necessity to process the "chunks" again after they were received, no
intermediate storage is required. Therefore, the "chunking" method is limited only by the
computational power of the CPU.
The handling of the data chunks is performed by the operation queues in the multithreaded
environment[23, 24]. The most common use case of transmitting large payloads to the server is
uploading a file to persistent storage. Worth noting, that the disk-related operations are usually
distributed on a single logical disk. This is usually the weakest point in the system.
The model and method of authentication of payloads by chunks are used in the communication
model of IoT components.
2.3. Communication model of IoT components
The communication model of IoT[25] components was further developed, combining the "request-
response" and "publisher-subscriber" behavioral models, which differs from the existing ones in the
ability to work and verify large payloads, which allows to achieve an increase in the speed and
reliability[26] of the information system when working with large payloads.
The communication model consists of three components: client, server and module. A central
server functions as a mediator between IoT modules and clients. In the case of a single client, the only
advantage of the new model over the request-response model may be the amount of data sent and
received. The advantage of the new model will be if multiple clients are connected to the central
server. The new model allows transferring and verifying a large amount of data using them on low-
power devices using the "chunking" method.
An example of IoT communication model is shown in Figure 4.
Client 1 Client 2 Server Module
Request Request
Response Response
Request
Request
Response Response
Figure 4: An example of an IoT communication model
The use of the "request-response" model makes it possible to perform individual requests. Taking
advantage of the speed of receiving updates to the state of the publisher-subscriber model allows the
clients to receive updates from the modules on the fly.
2.4. The influence of high load modes on data authorization mechanisms
The analysis showed the advantage of the "chunking" method. To validate the analysis, it is
necessary to compare the researched methods. To do this, the method of comparison of data
authentication mechanisms was developed. For the input of the new method, "Buffering to File",
"Buffering to Memory" and "Chunking" data authentication mechanisms were created. The results
have confirmed the analysis and proved the advantage of the "chunking" method by 13-22% for a
large payloads and 1 percent for small payloads[27].
Based on the method of comparison of data authentication mechanisms, there were developed
methods for identifying high load modes and studying the influence of high load modes on data
authorization mechanisms in the operation queue environment. The results of this study can be used to
find the optimal load for the equipment.
The original method differs from the existing ones in balancing according to the capabilities of the
servers, which allows to achieve an increase in the performance and reliability[28, 29] of the server
cluster by selecting the optimal load of the equipment in the cluster and the possibility of self-
recovering. From the server side of the cluster, to confirm that the load balancer is available, it must
periodically send heartbeat to the load balancer. Research has shown that these measures will allow
self-recovering in emergency situations.
3. Proposed model and method
As a conclusion to the paper[27], a suggestion was made to counteract high load modes based on
an active number of connections. Having a low number of maximum concurrent requests limit leaves
a chance that a server might not be 100% loaded. Moreover, a denial-of-service attack might happen
with a technique of starting a number of concurrent connections to the server with the minimal
possible speed. To counteract high load modes, a method of migration from a single server to the
server cluster has received further development. The main difference between the original method[32]
and the modified one is the choice of the least loaded server for large requests.
In the original method, the server is selected by the least number of active requests. The improved
method differs between small requests and large requests. The small requests are processed in the
same manner in both methods. For requests with a large payload, the improved method uses the
combination of the concurrent write speed and the number of active connections.
The data transmission speed per client is not constant and is changing over time. This might
expose the server cluster to denial-of-service attacks. In order to prevent them, the maximum number
of connections per server is also limited. If network interface speed is higher than maximum
concurrent write speed, it's advisable to limit network bandwidth for the process to this value. Both
values can be determined by the method of studying the influence of high load modes on data
authorization mechanisms in the operation queue environment.
That's why in the new method for large requests, the server with the least concurrent write speed is
selected. If all the servers are loaded at 100% then the server with the fewest active requests is
selected. This allows determining which server is the least loaded more accurately.
The modified method improves the reliability, which is a crucial safety factor [30, 31] of cluster
systems. Any request sent to the load balancer [32, 33] can be processed by any of the servers in the
web cluster. It was improved by adding a step of verification of the current number of active requests
and consists of the following steps:
1) the load balancer finds the server with the least concurrent write speed or the fewest requests in
the last minute.
2) check whether the current number of active requests can put the system into high load mode and
reject it if it's higher than it was found by the method of identifying high load modes
3) the incoming request is transmitted to the selected server.
4) process the request
The method might be simplified for a single server:
1) to get the current number of active requests in the server
2) check whether the current number of active requests can put the system into high load mode and
reject it if it's higher than it was found by the method of identifying high load modes
3) process the request
In this work, a new model for processing requests in high load modes was developed, which
differs from the existing ones in the ability to work and verify large payloads and to prevent high load
modes. This makes it possible to achieve an increase in the speed and reliability of the information
system when working with large payloads. Authentication and request processing model in high load
modes for IoT components is shown in figure 5.
Figure 5: Authentication and request processing model in high load modes for IoT components
In this model, it is assumed that it is known which "Request Handler" processes a new request or a
data chunk of the request.
The first stage is "High Load Mode Preventer", which limits the number of requests in the
operation queue. Its mechanism uses a simplified version of the method of migration from single
server to the server cluster. The maximum number of requests is obtained by the method of
identifying the high load modes.
The second stage is "Asynchronous Processing", in which the IoT communication model is
implemented, operations are created for processing a data chunk that is added to the operation queue.
The third stage is the authentication of a data chunk according to the "chunking" method. After
that, the data goes to the "Request Handler".
On the way back, the Request Handler creates operations for sending chunks of data that are
signed before sending. The "High Load Mode Preventer" step is not needed for sending data.
4. Results
Based on the authentication and request processing model in high load modes for IoT components,
an information system for the communication of IoT components was created. The central server is
responsible for communication between clients and modules. To discover the new modules, the Bonjour
discovery protocol is used, after which the server connects to them using the HTTP/2(3) protocol.
Figure 6: Class diagram of the developed library
For the HTTP/2(3) protocols, a method of chunking authentication of large payloads is
implemented in the ChunkVerifier and ChunkSigner classes.
The class diagram of the central server is shown in Figure 6.
To test the new model four experiments were carried out. The model uses the modified method of
"method of migration from single server to the server cluster" to implement the "High Load Mode
Preventer" component. To control the environment more precisely, the client and server were running
on a single computer.
The test computer has the following configuration:
OS: Ubuntu 20.04 LTS
CPU: Intel Core i7 8700K
RAM: 32G
SSD: Samsung 970 EVO Plus 1Tb
The first experiment aims to show the high load mode without any measures of preventing it. As
input, 20 clients were chosen with a speed of 1Gbps per client. The results of the experiment are
shown in figure 7.
Figure 7: The high load mode without any measures of preventing it
The plot shows linear growth and linear decrease. The peak memory consumption has reached
10534 megabytes, which might potentially cause the server to crash.
The network bandwidth is limited to the maximum drive concurrent write speed for the next series
of experiments to prevent high load modes. In the experiments, the maximum concurrent write speed
and the maximum number of active clients were determined by the method of studying the influence
of high load modes on data authorization mechanisms in the operation queue environment. For SSD,
which is used in the test configuration the maximum concurrent write speed is measured around 680
MB/s.
To show the effect of the limitation of network bandwidth in the second experiment the same
number of clients is used, but the total network speed is limited to 680 MB/s (5.4 Gbit/s). The results
are shown in figure 8.
Figure 8: The effect of the limitation of network bandwidth
With the limitation of the network speed, the situation might seem to be stabilized. But the server
isn't limited to process a large number of clients. To show the effect of the increased number of clients
it was increased to 400. The network speed limit of 680 MB/s (5.4 Gbit/s) remains the same. The
results are shown in figure 9.
Figure 9: The impact of an increased number of clients with the limitation of network bandwidth
To show the effect of the limitation of the number of active clients it was limited to 121. The
network speed limit of 680 MB/s (5.4 Gbit/s) remains the same. The results of the experiment are
shown in figure 10.
Figure 10: The effect of the limitation of the number of active clients
The modified method of migration from a single server to a server cluster has added the set of
measures for preventing high load modes. The series of experiments have proven the effectiveness of
them.
The resulting communication model was used in the development of a module for remote control
of a PC, which controls "POWER" and “RESET” switches and reads “HDD LED” and “POWER
LED” signals using opto-isolators.
The method of building communication mobile systems based on the OAuth 1.0 protocol was
further developed, which differs from the existing ones in the ability to work with embedded car
computers that do not have their own web browser and allows to achieve an increased security by
working directly with standard development tools for third-party applications.
The new method is characterized by the use of the communication model of the Internet of Things
and the "chunking" method of data verification for communication between a device with a web
browser and a car computer. The method consists of the following steps:
1) On the car computer, get the URL from the web service for authorization in the web browser
2) Initiate a launch on a device a web browser with the received URL
3) After successful authorization by the user, transfer the access token to the car computer
4) On the car computer, verify the validity of the token
The interdependence between the car computer, web browser device, and web service are shown in
Figure 11.
Figure 11: Interdependence between car computer, device with web browser and web service
The introduction of the new method significantly expands the capabilities of car computers.
5. Conclusion
In this work, a relevant scientific and practical problem is solved, associated with increasing the
speed and reducing the harmful effect of high load modes for authentication processes and data
processing in an operation queue environment. The main results are as follows:
1. Based on the analysis of existing authorization methods with the capability of authenticating
large payloads in the operation queue environment, it is concluded that developing and researching
new authentication methods of large payloads in the operation queue environment is a relevant task.
2. A model and method for chunking data authentication for large payloads have been
developed. Analysis of the new method showed an advantage in performance compared to the
existing ones due to the absence of the necessity to read the data again.
3. Based on the analysis of existing models, new models and methods for authentication of large
payloads for blocking and non-blocking sockets were created.
4. A method for ranking payload authentication mechanisms was developed, on the basis of
which experiments were carried out, which confirmed the advantage of the "chunking" method by
13-22% for a large payloads and 1 percent for small payloads.
5. Methods have been developed to identify the high load modes and study the effect of high
load modes on the use of system memory, which made it possible to increase the accuracy of
predicting high load modes and improve system reliability by limiting the equipment load.
6. To limit the load on the equipment, the method of load balancing in the server cluster was
further developed, which made it possible to reduce the negative effect of the influence of high load
modes on the system by improving the distribution of the load between the servers in the cluster.
7. Experiments were carried out to evaluate the new protocol, to establish the requirements for
the server cluster operating with the new authorization protocol, which showed that the
improvements in the load balancing mechanism prevented falling into high load modes in the
researched situations
8. A method has been developed for building communication mobile systems based on the
OAuth protocol for embedded car computers that do not have their own web browsers. Based on the
method, a module of the Internet of Things information system was created for authorization, built-
in by car computers that do not have their own web browsers.
9. The authentication and request processing model in high load modes for IoT components was
further developed, on the basis of which the information system of IoT components has been
created.
6. References
[1] A. Kupin, I. Muzyka, R. Ivchenko, Information Technologies of Processing Big Industrial Data
and Decision-Making Methods, in International Scientific-Practical Conference on Problems of
Infocommunications Science and Technology, PIC S and T, (2019), doi:
10.1109/INFOCOMMST.2018.8632096.
[2] O. Pomorova, T. Hovorushchenko, Artificial neural network for software quality evaluation
based on the metric analysis, in IEEE East-West Design & Test Symposium, EWDTS’2013,
Kharkiv, (2013), pp. 200-203, doi: 10.1109/EWDTS.2013.6673193.
[3] R. Fielding, J. Reschke, Hypertext Transfer Protocol (HTTP/1.1): Message Syntax and Routing,
IETF RFC 7230, 2015. URL: https://tools.ietf.org/html/rfc7230
[4] M. Belshe, R. Peon, Hypertext Transfer Protocol Version 2 (HTTP/2), IETF RFC 7540, 2015.
URL: https://tools.ietf.org/html/rfc7540
[5] J. M. Kizza, Guide to Computer Network Security Third Edition. Springer, 2015.
[6] Rescorla E., HTTP over TLS, IETF RFC 2818 (Informational), 2014. URL:
http://tools.ietf.org/html/rfc2818
[7] Dierks T., The Transport Layer Security (TLS) Protocol. – IETF RFC 5246 2014. URL:
http://tools.ietf.org/html/rfc5246
[8] S. Farooqi, F. Zaffar, N. Leontiadis, Z. Shafiq, Measuring and mitigating OAuth access token
abuse by collusion networks, in: Communications of the ACM, New York, NY, United States,
(2020), vol. 63, pp. 103-111, doi: 10.1145/3387720.
[9] A. Mukherjee, Physical-Layer Security in the Internet of Things: Sensing and Communication
Confidentiality Under Resource Constraints, Proceedings of the IEEE, vol. 103, no. 10, pp. 1747-
1761, (2015), ISSN: 1558-2256, doi: 10.1109/JPROC.2015.2466548
[10] Apple Inc., Grand Central Dispatch, 2020. URL: https://github.com/apple/swift-corelibs-
libdispatch
[11] S. Grosch, Concurrency by Tutorials (Second Edition): Multithreading in Swift with GCD and
Operations. McGaheysville, VA, United States Razeware LLC, 2020, p. 100.
[12] D. Maevsky, V. Kharchenko, M. Kolisnyk, E. Maevskaya, Software reliability models and
assessment techniques review: Classification issues, in 2017 9th IEEE International Conference
on Intelligent Data Acquisition and Advanced Computing Systems: Technology and
Applications (IDAACS), Bucharest, Romania, (2017), pp. 894-899, doi:
10.1109/IDAACS.2017.8095216.
[13] Hovorushchenko T., Pavlova O., Method of Activity of Ontology-Based Intelligent Agent for
Evaluating the Initial Stages of the Software Lifecycle., Advances in Intelligent Systems and
Computing, vol. Vol. 836, pp. 169-178, (2019), doi: 10.1007/978-3-319-97885-7_17
[14] Y. Feng, M. Sathiamoorthy, A security analysis of the OAuth protocol, in IEEE Pacific Rim
Conference on Communications, Computers and Signal Processing (PACRIM), Victoria, BC,
Canada, (2013), pp. 271-276, doi: 10.1109/PACRIM.2013.6625487.
[15] A. Lombardi, WebSocket Lightweight Client-Server Communications. O'Reilly Media, 2016.
[16] S. Lysenko, K. Bobrovnikova, O. Savenko, A Botnet Detection Approach Based on The Clonal
Selection Algorithm, in Proceedings of 2018 IEEE 9th International Conference on Dependable
Systems, Services and Technologies (DeSSerT-2018), Kyiv, Ukraine, (2018).
[17] Hammer-Lahav E., The OAuth 1.0 Protocol. IETF RFC 5849, 2010. URL:
http://tools.ietf.org/html/rfc5849.
[18] E. Hammer, HAWK / HTTP Holder-Of-Key Authentication Scheme, 2019. URL:
https://github.com/hueniverse/hawk
[19] H. Krawczyk, M. Bellare, HMAC: Keyed-Hashing for Message Authentication, 2011. URL:
https://tools.ietf.org/html/rfc2104
[20] E. Hammer, OAuth 2.0 and the Road to Hell, 2012. URL: http://hueniverse.com/2012/07/oauth-
2-0-and-the-road-to-hell/
[21] S. S. Surkov, Model and method of chunk processing of payload for HTTP authorization
protocols, Proceedings of 2020 IEEE 15th International Conference on Advanced Trends in
Radioelectronics, Telecommunications and Computer Engineering (TCSET), Slavske, Ukraine,
pp. 317-321, (2020), doi: 10.1109/TCSET49122.2020.235447
[22] Lysenko S., Bobrovnikova K., Matiukh S., Hurman I., Savenko O., Detection of the botnets’
low-rate DDoS attacks based on self-similarity, International Journal of Electrical and Computer
Engineering (Q2), vol. 10., №4, pp. 3651-3659, (2020), ISSN: 2088-8708
[23] O. Drozd, M. Kuznietsov, O. Martynyuk, M. Drozd, A method of the hidden faults elimination
in FPGA projects for the critical applications, in Proceedings of 2018 IEEE 9th International
Conference on Dependable Systems, Services and Technologies (DESSERT’2018), Kyiv,
Ukraine, (2018), pp. 231 – 234, doi: 10.1109/DESSERT.2018.8409131.
[24] A. Drozd, V. Kharchenko, S. Antoshchuk, J. Sulima, M. Drozd, Checkability of the digital
components in safety-critical systems: problems and solutions, in IEEE East-West Design & Test
Symposium, Sevastopol, Ukraine, (2011), doi: 10.1109/EWDTS.2011.6116606.
[25] V. M. Kuntsevich, et al. (Eds), Control Systems: Theory and Applications. Book Series in
Automation, Control and Robotics: River Publishers, Gistrup/Delft, 2018, pp. 1-326.
[26] A. Kupin, A. Senko, Principles of intellectual control and classification optimization in
conditions of technological processes of beneficiation complexes, in CEUR Workshop
Proceedings, (2015), vol. 1356, pp. 153–160. URL: http://ceur-ws.org/Vol-1356/paper_34.pdf
[27] S. Surkov, Reduction of the impact of critical modes for authorization protocols for large
requests in operation queue enviroment, Applied Aspects of Information Technology, vol. Vol.3
No.3, (2020), doi: 10.15276/aait.03.2020.3
[28] A. Drozd, J. Drozd, S. Antoshchuk, V. Nikul, M. Al-dhabi, Objects and Methods of On-Line
Testing: Main Requirements and Perspectives of Development, in Proc. IEEE East-West Design
& Test Symposium, Yerevan, Armenia, (2016).
[29] Drozd O., Perebeinos I., Martynyuk O., Zashcholkin K., Ivanova O., Drozd M., Hidden fault
analysis of FPGA projects for critical applications, in IEEE International Conference TCSET,
Lviv-Slavsko, Ukraine, (2020), doi: 10.1109/TCSET49122.2020.235591.
[30] H. Sanae, Security Requirements and Model for Mobile Agent Authentication, Smart Network
Inspired Paradigm and Approaches in IoT Applications, Singapore, Republic of Singapore, pp.
179-189, (2019), doi: 10.1007/978-981-13-8614-5_11
[31] K. Saini, Squid Proxy Server 3.1: Beginner's Guide Paperback. Birmingham, United Kingdom:
Packt Publishing, 2011, p. 332.
[32] D. Wessels, Squid: The Definitive Guide. Sebastopol, CA, United States: O'Reilly Media, 2010,
p. 472.
[33] M. Rash, Linux Firewalls: Attack Detection and Response with iptables, psad, and fwsnort. San
Francisco, CA, United States: No Starch Press, 2007, p. 336.