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DEVELOPMENT OF INTELLIGENT INFORMATION SECURITY
SYSTEMS BASED ON NEURAL NETWORK INTRUSION DETECTION
SYSTEMS IN CYBERSPACE
Mirabbos Akbarov
Inomjon Yarashov
Diplomat university
The University of World Economy and Diplomacy
e-mail: iyarashov@uwed.uz
https://doi.org/10.5281/zenodo.15396445
Abstract.
This research focuses on the development of intelligent
information security systems utilizing neural network-based intrusion detection
systems (IDS) within cyberspace. It highlights flexibility, learning capability, and
controllability as key conceptual requirements for an effective IDS. The study
emphasizes designing a flexible, intelligent security system that integrates IDS
not only within individual system components but also in data transmission
networks connecting them. The methodology combines artificial intelligence
techniques, systematic analysis methods, and the theory of intelligent
information systems in the context of artificial intelligence. The research
employs a systematic-conceptual approach to information protection, stressing
the importance of interconnected local systems to ensure the overall security of
the network. The primary outcome of the research shows that effective
information protection can only be achieved through the integration of neural
network technologies into a unified center, following a systematic-conceptual
approach. This approach is essential for counteracting unauthorized attacks and
ensuring security across all system components. Additionally, the research
underscores the importance of adopting a unified approach that integrates legal,
organizational, and technical measures to protect information. Applying this
approach to the development of IDS powered by neural network technologies is
expected to lead to new tools, methods, and strategies for intelligent information
security management, thereby enhancing the protection of cyberspace.
Keywords:
intelligent systems, information security, neural systems,
intrusion detection systems, information security strategies, attack detection
system
Introduction.
An Intrusion Detection System (IDS), or Attack Detection
System (ADS), is a software or hardware-based tool designed to prevent
unauthorized access to computer systems, networks, and data for malicious
purposes such as data theft, system sabotage, or unauthorized surveillance [2].
These systems play a crucial role in ensuring the security and integrity of
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information systems by monitoring network traffic and identifying potential
threats or vulnerabilities. IDSs are considered an essential part of a
comprehensive information security architecture [1-10], as they provide an
additional layer of defense against attacks. They are particularly vital in complex
network environments where traditional security measures, such as firewalls,
may not be sufficient to detect sophisticated attacks. Figure 1 illustrates the key
components of an information security system, highlighting the critical role of
IDS in protecting system integrity.
IDSs that leverage neural network technologies [11-26] are gaining
recognition for their ability to process and analyze large volumes of data in real-
time. These advanced systems can identify patterns and anomalies in network
traffic that could indicate potential security breaches. Neural network-based
IDSs have proven to be effective in detecting a wide range of threats, including
known and emerging attack patterns, by continuously learning from new data
and adapting to the evolving nature of cyber threats. The integration of artificial
intelligence and machine learning techniques into IDS design has significantly
improved their detection accuracy, making them highly promising tools for
modern cybersecurity.
Figure 1. Survey of intrusion detection systems: techniques, datasets and
challenges[1]
One of the key advantages of neural networks is their ability to learn and
adapt over time. During the training phase, neural networks are exposed to vast
amounts of network data, enabling them to recognize both typical and atypical
traffic patterns. Once trained, neural networks can autonomously detect new,
previously unknown threats based on the learned features and behaviors of
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legitimate and malicious activities. This adaptability makes neural network-
based IDSs particularly effective in detecting zero-day attacks and other
advanced persistent threats (APTs) that traditional signature-based IDS may
miss. The continuous evolution of neural network models allows them to remain
effective even in the face of rapidly changing attack techniques and methods,
ensuring a higher level of protection for the systems they safeguard.
Main part:
The development of intelligent information security systems, particularly
those based on neural network intrusion detection systems (IDS) in cyberspace,
is a critical area of research in the field of cybersecurity. Software-as-a-Service
(SaaS) models, which offer cloud-based applications, are increasingly being
targeted by malicious entities due to their inherent vulnerabilities. These
services are often subject to delays and interruptions caused by internet
unavailability, presenting opportunities for attacks, particularly from botnets.
Research in cloud security predominantly focuses on preventing these attacks,
such as Distributed Denial of Service (DDoS) and spamming, which are
increasingly facilitated by botnets. SaaS services, as they leverage computing
power from both cloud servers and customer machines, are uniquely susceptible
to being exploited as attack vectors for botnets [2]. As a result, cybersecurity
efforts must address not only the protection of cloud infrastructure but also the
identification and mitigation of attacks within these services (Fig. 2)
In the context of information security, the term "intrusion" can be
understood in multiple ways, such as a security breach, attack, or penetration.
According to standard terminology [2], an attack on an information system
involves one or more security incidents that exploit vulnerabilities within the
system, often exacerbated by human factors. These incidents can lead to the
realization of various threats that compromise system integrity. An information
security incident is defined as any unwanted or unexpected event that may
disrupt the functioning of business operations or compromise information
security.
The concept of intelligent information security systems, particularly those
based on neural network-based intrusion detection systems (IDS), is
fundamental to the development of critical technologies [1]. This concept
emphasizes the need for flexible IDS systems that function at both the network
nodes and across the transmission networks between these nodes. Neural
network-based IDSs, enhanced with artificial immune system mechanisms, offer
significant potential as functionally independent and trainable subsystems
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within a broader information security architecture. These systems require
coordination through a centralized control center to effectively manage the
overall security infrastructure [1]. The core conceptual requirements for an
effective IDS system are adaptability and learnability.
Figure 2. A machine learning based attack detection and mitigation using a
secure SaaS framework[2]
Adaptability in neural network-based IDSs is achieved through the inherent
properties of artificial neural networks, which allow these systems to learn and
adjust to new types of threats. Controllability is ensured through regulated
processes that govern the implementation of macro-processes in information
security management. Intrusion protection functions play a crucial role in the
development of these intelligent security systems. A functional approach to IDS
development is grounded in the concept of information security functions, which
form the basis for organizing security measures. A key conceptual requirement
for these functions is completeness, which refers to the systematic and regular
execution of these functions to achieve the necessary level of security[8].
The protection function is understood as a homogeneous set of actions and
decisions implemented to ensure system security. The completeness of these
functions means that their ongoing and consistent execution contributes to the
achievement of desired security outcomes. The full set of protection functions
against intrusions in the system includes: 1) prevention of conditions leading to
intrusions; 2) prevention of malicious code infiltration; 3) detection of emerging
intrusions; 4) prevention of the impact of intrusions on system information; 5)
detection of the effects of intrusions on information; 6) localization of intrusion
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impact; and 7) mitigation of intrusion consequences. Each function's outcome
will trigger one of several possible events, such as detecting, preventing,
localizing, or eliminating the effects of an intrusion. These events collectively
form a set of incompatible random events, and the sum of the probabilities of
favorable outcomes represents the overall reliability of information protection
from intrusions. This approach provides a strong foundation for optimizing the
allocation of resources dedicated to safeguarding information and enhancing the
effectiveness of IDS systems.
Conclusion
The research focused on the challenge of ensuring reliable information
security using Intrusion Detection Systems (IDS) based on neural network
attack detection technologies. It emphasized the importance of employing early
and proactive tactics, leveraging effective tools, methods, and measures for
information protection in cyberspace. This approach is grounded in the
fundamental principles of the systematic-conceptual approach to information
security, which highlights the need for integrated and adaptable systems capable
of mitigating threats effectively. The findings underscore the necessity of using
neural network-based IDS to enhance security, focusing on anticipatory actions
to address potential vulnerabilities and ensure robust defense mechanisms in
the ever-evolving landscape of cyberspace security.
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