MODERN PROBLEMS AND AI-BASED SOLUTIONS IN CYBERSECURITY

THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 MODERN PROBLEMS AND AI-BASED SOLUTIONS IN CYBERSECURITY Umarova Dildora Bakhtiyarovna, Ilhomjonov Sirojiddinbek Ikromjon o‘g‘li 1Doctor of Philosophy (PhD) in Art History, Acting Associate Professor of the Department of television and media technologies, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent city, Uzbekistan 2Student of the Department of Television and Media Technologies, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan https://doi.org/10.5281/zenodo.17908136 Abstract. The advancement of digital technology is significantly increasing the need for cybersecurity considerations. Due to the widespread and networked use of the Internet, the regular use of social networks, the number of cyberattacks and their level of sophistication is increasing. Today, cyber threats such as phishing, ransomware, DDoS, and social engineering are eroding the effectiveness of traditional defenses. It weakens existing security mechanisms. Therefore, the need to use the capabilities of artificial intelligence (AI) technologies in the field of cybersecurity has become an urgent issue. With the help of artificial intelligence technologies, protection systems will be able to independently analyze and make decisions without human intervention. As a result, the reliability and security of information systems increases. AI-based models reduce errors due to human factor and allow for the automatic detection and elimination of an unexpected cyberthreat. Artificial intelligence technologies not only improve defensive measures, but also take the reliability of systems to a new level by anticipating a new type of cyberattack, monitoring abnormal behavior on the network, and analyzing the level of risk. Keywords: AI, Digital Technology, phishing, ransomware, DDoS, Social Engineering, machine learning, automated protection Introduction. In the 21st century, the demand for cybersecurity is growing at a high rate and at a rapid pace. Regular use of the Internet, smart devices, cloud technologies and social networks have made data exchange easier and faster, but at the same time, the need for information security has increased dramatically. Nowadays, the number of cyberattacks such as phishing, ransomware, DDoS as well as social manipulation (Social Engineering) is increasing, and types of them have become increasingly sophisticated. As a result, both old and traditional defense systems are not able to give the expected result against these types of attacks. In this regard, the use of artificial intelligence technologies is introduced in modern cybersecurity spheres. With the help of AI technologies, it is possible to detect attack behavior occurring in networks at an early stage, predict potential risks, and develop automated defense systems. With the help of such systems, cyberrisks are monitored in real time and automatic protection measures are implemented. As a result, the reliability of information systems increases and anticipated risks can be eliminated at the earliest possible stage. AI-based approaches are able to adapt to new types of attacks with the ability to self-learn and independently update their strategy. This process makes systems more stable and able to make decisions quickly. AI technologies are making it possible to further improve existing defense mechanisms in the field of cybersecurity. THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 Materials and methods The study looked at statistics of cyberattacks published in open sources, as well as cybersecurity data prepared by international companies such as IBM, Kaspersky, Cisco, and Darktrace. In addition, scientific articles on threat detection using artificial intelligence-based defenses and machine learning models (Intrusion Detection Systems) were also studied. Research methods: Analytical approach: the causes, consequences of cyberrisks and protection mechanisms against them have been studied and analyzed. Comparison Method: The effectiveness of artificial intelligence-based solutions was compared with traditional security approaches. Model analysis: the process of threat detection based on machine learning algorithms (Decision Tree, Random Forest, Neural Network) was studied. Experimental (theoretical) analysis: The capabilities of AI systems in early detection of cyber risks were evaluated through modeling. The results of the study showed that due to the integration of artificial intelligence technologies into cybersecurity systems, the possibility of detecting attacks at an early stage, automatically responding to them, and reducing errors caused by the human factor increases significantly. Results and discussion The results of the study show that the introduction of artificial intelligence (AI) technologies into cybersecurity systems significantly improves the effectiveness of attack detection and network protection. AI-based intelligent models make it possible to detect unusual behavior and unexpected activity in the network in real time. Such attacks are exposed at an early stage, and user activity is constantly monitored. The system will be able to eliminate cyberattacks automatically. Machine Learning The use of Machine Learning and Deep Learning algorithms provides a significant advantage in detecting cyberattacks. Studies have shown that using these algorithms the accuracy of detecting threats such as phishing, DDoS, as well as social engineering can be in the range of 85–95%. These results are 30–40 percent higher than normal, humancontrolled safety mechanisms. AI technologies reduce errors caused by the human factor and provide the system with self-learning capabilities. For example, by using neural network-based Intrusion Detection Systems (IDS) or Anomaly Detection System (ADS) modules, unusual traffic or user behavior in networks is automatically analyzed and decisions are made in real time. Artificial intelligence-based approaches allow for not only detection, but also prediction. It is also possible to dynamically optimize security policies and efficiently allocate system resources using algorithms such as Reinforcement Learning, Random Forest, Decision Tree, and Deep Neural Network. Especially in the areas of real-time monitoring, anomaly activity analysis, risk prediction, and modeling, AI technologies are achieving very high results. For example, a case study by IBM, Cisco, and Darktrace noted that AI-based defense systems increased the detection rate of cyberattacks by 60% and the response effectiveness by 70%. Conclusion Cybersecurity is playing an important strategic place in the operations of states, organizations and even ordinary users. While the internet and mobile technology are rapidly evolving, so is the number of cyberattacks. Along with the acceleration of this process, the sophistication and scope of threats have also widened, leaving traditional security measures THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 incapable of eliminating them to the full extent possible. The development of cybersecurity systems based on artificial intelligence technologies is becoming both a requirement of the times and an urgent need. In the future, the effectiveness of cybersecurity infrastructure will directly depend on the extent to which artificial intelligence technologies are integrated and whether they are regularly updated. AI systems with the ability to continuously learn and adapt will serve to detect a new type of cyber threat early and take the level of protection to a higher level. AI-based approaches are becoming one of the key areas defining the future of cybersecurity. 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