CSIT - 2021 рік

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https://elar.khmnu.edu.ua/handle/123456789/10490

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Перегляд CSIT - 2021 рік за Ключові слова "machine learning"

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  • Ескіз
    Документ
    Research of machine learning based methods for cyberattacks detection in the internet of things infrastructure
    (Хмельницький національний університет, 2021) Bobrovnikova, K.; Kapustian, M.; Denysiuk, D.; Бобровнікова, К.; Капустян, М.; Денисюк, Д.
    The growing demand for IoT devices is accelerating the pace of their production. In an effort to accelerate the launch of a new device and reduce its cost, manufacturers often neglect to comply with cybersecurity requirements for these devices. The lack of security updates and transparency regarding the security status of IoT devices, as well as unsafe deployment on the Internet, makes IoT devices the target of cybercrime attacks. Quarterly reports from cybersecurity companies show a low level of security of the Internet of Things infrastructure. Considering the widespread use of IoT devices not only in the private sector but also in objects for various purposes, including critical infrastructure objects, the security of these devices and the IoT infrastructure becomes more important. Nowadays, there are many different methods of detecting cyberattacks on the Internet of Things infrastructure. Advantages of applying the machine-based methods in comparison with signature analysis are the higher detection accuracy and fewer false positive, the possibility of detecting both anomalies and new features of attacks. However, these methods also have certain disadvantages. Among them there is the need for additional hardware resources and lower data processing speeds. The paper presents an overview of modern methods aimed at detecting cyberattacks and anomalies in the Internet of Things using machine learning methods. The main disadvantages of the known methods are the inability to detect and adaptively respond to zero-day attacks and multi-vector attacks. The latter shortcoming is the most critical, as evidenced by the constantly increasing number of cyber attacks on the Internet of Things infrastructure. A common limitation for most known approaches is the need for significant computing resources and the significant response time of cyberattack detection systems
  • Ескіз
    Документ
    Using artificial intelligence accelerators to train computer game characters
    (Khmelnytskyi National University, 2021) Hnatchuk, Y.; Sierhieiev, Y.; Hnatchuk, A.; Гнатчук, Є.; Сєргєєв, Є.; Гнатчук, А.
    A review of the literature has shown that today, given the complexity of computational processes and the high cost of these processes, the gaming computer industry needs to improve hardware and software to increase the efficiency and speed of processing artificial intelligence algorithms. An analysis of existing machine learning tools and existing hardware solutions to accelerate artificial intelligence. A reasonable choice of hardware solutions that are most effective for the implementation of the task. Possibilities of practical use of the artificial intelligence accelerator are investigated. The effectiveness of the proposed solutions has been proven by experiments. The use of an artificial intelligence accelerator model allowed to accelerate the learning of a computer game character by 2.14 times compared to classical methods.