Today, artificial intelligence, particularly neural networks, is increasingly being used in software in a variety of industries, from mission-critical applications such as healthcare and the military to commerce and entertainment. One of the main stages of development and implementation of such software is the stage of quality control. To prevent fatal errors and to survive in a highly competitive environment, the software needs proper testing taking into account the peculiarities inherent in the data obtained as a result of the neural network. This article presents the relevance of using artificial intelligence systems in general and neural networks in particular and analyzes the main challenges that arise when assessing the quality of such networks. The author compares the properties of the output data of the artificial intelligence systems of the previous generation and the latest neural networks, highlights the key differences of the latter, such as the potential infinity of the input data sets and their relative unpredictability, the dependence of the results on the network training stage, and the subjective nature of the evaluation of such results. Based on the analysis, the author formulates a set of problems that can be solved using mathematical algorithms and methods. The main part of an article contains a general overview of existing solutions, with an emphasis on such algorithms and methods as calculating accuracy and loss, finding the F-score, interpretation methods and imitation modeling. As a result of the research, the author comes to the conclusion that, despite a sufficient number of existing solutions that can be used to solve the highlighted problems, they still have to be improved to increase the accuracy of neural network evaluation, as one hundred percent accuracy in evaluating data obtained as a result of the operation of neural networks has not yet been achieved.
This article examines the history, types, capabilities, and advantages of next-generation firewall (NGFW) technology. Firewalls are an important tool for protecting network resources from various information security threats. With the development of technology and the changing nature of attacks, especially those involving artificial intelligence, firewalls have also evolved, acquiring new functions and capabilities. This work provides a short survey of the main types, capabilities and benefits of next-generation firewall (NGFW) technology, which is a modern solution for comprehensive network protection against complex and sophisticated security threats. The work also analyzes the distinct features of NGFW and differences between NGFW and previous generations of firewalls, as well as examples of NGFW from well-known vendors that dominate the market, such as Palo Alto Networks, Fortinet and Cisco. The article highlights the main trends, prospects for the development and implementation of NGFW, including the impact of artificial intelligence, machine learning, cloud technologies and the Internet of Things, advantages and disadvantages, capabilities, important aspects, purpose and sphere of application. The article also addresses the significant impact this technology will have on network security. It is emphasized that the introduction of NGFW does not replace other security technologies and tools, but effectively expands the existing arsenal of countering new security threats (primarily as an instrument of proactive countermeasures and rapid response to complex network incidents). The article may be useful for students, researchers, and information security professionals who seek to expand their competencies related to the development of modern firewall technologies and their capabilities.
In today's world, cyber threats are becoming a serious issue for companies in all professional sectors. For all organisations, regardless of their field of activity, cyber threats in today's world are undoubtedly a significant challenge. Undoubtedly, modern organisations should set themselves the task of effectively countering cyber threats regardless of their professional industry. To effectively counter these threats, organisations must have effective incident response systems in place, including in cyberspace. There are many incident response frameworks in the US, each with its own advantages and disadvantages. This article offers a comparative analysis of the four leading US cyber incident response frameworks: NIST Cybersecurity Framework (CSF), CISA Cyber Incident Response Guide, ISO/IEC 27001 and NIST Special Publication 800-61. The purpose of the study is to provide organisations with an overview of the four leading incident response frameworks in the US so that they can choose the most appropriate framework for their specific needs. The research was conducted using a qualitative approach that included a thorough review of official documents, a review of relevant current literature, and consultation with cybersecurity professionals. This article is a valuable resource for organisations and companies looking for an effective and efficient method of responding to incidents, including cyber incidents. It provides an overview of the four leading frameworks in the US, allowing organisations to compare their advantages and disadvantages and ultimately choose the most appropriate framework for their specific objectives.
<div class="htmlview paragraph">In September 1985 the new Audi 5000 CS quattro was introduced to the American market. This luxurious high performance touring sedan has been equipped with a more advanced turbo-charged engine with intercooler and electronic engine management giving improved performance, excellent torque, faster response and better fuel economy.</div> <div class="htmlview paragraph">The basic engine is the tried-and-tested Audi 5-cylinder unit. The turbocharged engine's ancillary systems, the electronic ignition control and fuel injection have all been newly developed, carefully optimized and well matched in the special demands of a turbocharged engine. The ignition system controls the engine and fuel injection and delivers analog and digital signals to the car's instrument panel display. The system also has an integrated self-diagnostic function.</div>