EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN CYBERSECURITY

Аннотация

Abstract: This article provides a scientific analysis of the application of artificial intelligence algorithms in the field of cybersecurity, their effectiveness, and practical results. It analyzes how key AI technologies such as machine learning, in-depth learning, and anomaly detection work in security systems. In the process of ensuring cybersecurity, the functions of AI, such as machine learning, detection of unusual situations, and automated decision- making, are highlighted. Risks that can arise through artificial intelligence are also considered - in particular, such aspects as deepfake technologies, algorithmic errors, ethical problems, and counterattacks. Keywords: Artificial intelligence, cybersecurity, machine learning, anomaly detection, deepfake, adversarial attack, information security. The digital transformation process is affecting all aspects of modern society. At the same time, with the expansion of the global digital infrastructure, cybersecurity threats are also increasing. Traditional defense mechanisms often cannot withstand complex and rapidly changing attacks. Therefore, the use of AI-based algorithms is seen as an important solution in the field of cybersecurity. This article analyzes the effectiveness of AI algorithms in cybersecurity and considers their practical significance based on real-life examples. 1. The potential of AI in cybersecurity. Artificial intelligence technologies are being seen as a tool to complement human capabilities in detecting and preventing cyber threats. The following aspects are particularly noteworthy: - Early detection of threats: Intrusion Detection Systems (IDS) using AI detect unusual activity in the network in real time. For example, if a deviation from the user's usual behavior is detected, the system automatically sends a warning signal. 50 INNOVATIVE RESEARCH IN SCIENCE International scientific-online conference - Automatic analysis of cyber attacks: Through algorithms developed based on machine learning and deep analysis, viruses, phishing attacks and malware can be automatically analyzed and countermeasures can be taken. - Automated decision-making: Artificial intelligence systems can independently make security decisions without human intervention. This allows for a quick and effective response to cyber attacks. - AI-based identity verification systems: Biometric identification (facial recognition, voice, fingerprints) is performed with high accuracy through artificial intelligence, thereby ensuring reliable protection of personal information. 2. Potential risks and challenges of artificial intelligence: Along with the possibilities of artificial intelligence, it is also being considered as a source of new types of threats. - Risks that can be caused by artificial intelligence: There is a possibility of spreading false information in society, politics, or the economy through "deepfake" videos or audio materials created using AI. - Qarshi hujumlar: Bunday hujumlar SI tizimlarini chalg‘itishga qaratilgan bo‘lib, sun’iy intellektga maxsus zararli ma’lumotlar kiritilishi orqali uni noto‘g‘ri qaror qabul qilishga undash mumkin. - Ethical and social issues: Security systems based on artificial intelligence can increase excessive control over people, which can lead to privacy risks. - Algorithmic uncertainty and erroneous decisions: AI-based algorithms do not always give accurate results. A model trained on the wrong data can identify false threats or ignore existing risks. 3. Methods for effective implementation of artificial intelligence in cybersecurity. - Hybrid approaches: Integrating artificial intelligence technologies with traditional security mechanisms increases efficiency. AI systems controlled by humans provide a balanced and sustainable approach. - Quality control of training data: The accuracy of the AI model largely depends on the accuracy, purity and relevance of the training data to real conditions. Therefore, data must be constantly monitored to prevent data breaches. - Explainable AI: Models and systems that can explain the reasons and rationale for decisions made by AI need to be developed. This will increase user trust and serve to ensure ethical requirements. 51 INNOVATIVE RESEARCH IN SCIENCE International scientific-online conference - Legal and ethical procedures for cybersecurity: National regulatory documents and international standards are needed to govern AI-based security systems and define their scope of operation. Despite the effectiveness of AI algorithms, there are a number of problems: - Data dependency - The quality of algorithms depends on the accuracy and breadth of the data collected. - False positives - Some systems incorrectly identify situations that are not threats. - AI itself can be the target of attacks - AI can be misled through so-called adversarial attacks. - Ethnic and legal issues - There are concerns about privacy and automated decision-making. In conclusion, AI algorithms are becoming an important