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EN
This paper presents an approach to simplifying security in large organizations, ensuring that the work of ordinary employees is more accessible without negatively impacting the organization's security. Access to the Internet is becoming increasingly important in various aspects of human life, including interpersonal communication, professional work, and business operations. Enterprises have digitized information on their devices, which can be of interest to attackers. Incorrect security measures can expose companies to various losses, such as financial costs or image losses. As the number of enterprises connected to the Internet grows, IT system security is gaining importance. Cyberattacks are malicious actions by individuals or organized groups with motives such as financial gain, employee dissatisfaction, or government interference. Attackers aim to access financial data, customer lists, financial data, and customer databases. Cybersecurity should be the basis of every organization, regardless of its size. The number of attacks each year is related to increased investments in cybersecurity strategies and focusing on finding and stopping hackers. Information security protects information to prevent unauthorized access, use, and disclosure. It includes implementing policies and procedures to protect information and prevent data loss or theft. However, implementing security measures can be challenging for ordinary employees, leading to decreased security.
EN
Artificial Intelligence (AI) methods are widely used in our lives (phones, social media, self-driving cars, and e-commerce). In AI methods, we can find convolutional neural networks (CNN). First of all, we can use these networks to analyze images. This paper presents a method for classifying items into particular categories on an auction site. The technique prompts the seller to which category assign the item when creating a new auction. We choose a neural network with a number of image convolution layers as the best available approach to address this task. All tests were carried out in the Matlab environment using GPU and CPU. Then, the tested and verified solution was implemented in the TensorFlow environment with a CPU processor. Thanks to the cross-validation method, the effectiveness of the recognition system was fully verified in several stages. We obtained promising results. Consequently, we implemented the developed method by adding a new sales offer on the Clemens website.
EN
Data files, photos, and videos on the internet are vast sources of information about the person who posted them. These files contain content about appearance, behaviour, views, and material status. Analyzing these files helps verify the accuracy of the content and helps verify the creation method. Social media platforms like Facebook, Twitter, and Instagram often post this information. Public databases provide information about enterprises, corporations, and public figures, enabling access to government trips, scientific articles, and company reputations. These resources help in understanding potential collaborations and identifying potential partners. Open-source intelligence (OSINT) is a collection of tools and methods for extracting information from publicly available sources. It helps verify the accuracy and authenticity of information, as seen in the FBI's 2020 investigation of a Philadelphia woman involved in protests and preparing precise attacks like spear phishing. In this manuscript, we present an up-to-date overview of research that uses open-source methods and techniques. We will concentrate on the tools and methods advancing the cybersecurity industry. Studying the manuscript of OSINT opportunities and challenges can help readers understand the state of the art in theory and practice. We will also highlight the future directions and requirements for OSINT methods and the newly designed tools using these methods.
4
Content available Trivializing verification of cryptographic protocols
EN
One of the main problems of the digital world is information security. Every second, peopleprocess millions of pieces of information that must be protected from unauthorized access. Cryptographic protocols that define the communication plan and the cryptographictechniques used to secure the messages come to the rescue. These protocols should alsobe regularly verified regarding their ability to protect systems from exposure to threatsfrom the computer network. Bearing in mind the need to secure communication, verify thecorrect operation of security methods and process large amounts of numerical data, we decided to deal with the issues of modeling the execution of cryptographic protocols and theirverification based on the CMMTree model. In this article, we present a tool that verifiesa protocol’s security. The tool allows for modelling a protocol and verifying that the pathin the execution tree represents an attack on that protocol. The tool implements a specially defined hierarchy of protocol classes and a predicate that determines whether a nodecan be attached to a tree. We conducted a number of tests on well-known cryptographicprotocols, which confirmed the correctness and effectiveness of our tool. The tool foundthe attack on the protocols or built an execution tree for them.
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