DEVELOPMENT OF INTELLIGENT INFORMATION SECURITY SYSTEMS BASED ON NEURAL NETWORK INTRUSION DETECTION SYSTEMS IN CYBERSPACE

Аннотация

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 22 ACADEMIC RESEARCH IN MODERN SCIENCE International scientific-online conference 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 23 ACADEMIC RESEARCH IN MODERN SCIENCE International scientific-online conference 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 un