Using Machine Learning Techniques to Increase the Effectiveness of Cybersecurity

In today's world, a great number of organizations generate and accumulate large amounts of information, which is of great value to owners, and is also considered by attackers as a valuable resource for enrichment. Any data storage system has vulnerabilities that will be exploited during cyberattacks. The inability to build a system secure enough against unauthorized access to data, forces companies to respond on an ongoing basis to evolving technologies of misappropriation of information by developing more effective methods of identifying and combating cyberattacks. This article examines the features of the use of machine learning methods to identify illegal access by third parties to the information of individuals and legal entities with economic and reputational damage. The study considers methods of processing various types of data (numerical values, textual information, video and audio content, images) that can be used to build an effective cybersecurity system. Obtaining a high level of identification of unauthorized access to data and combating their theft is possible through the implementation of modern machine learning approaches, which are constantly improving by creating innovative data processing algorithms and the use of powerful cloud computing services, acting as an element to counter rapidly evolving technologies

A Survey on Data Plane Programming with P4: Fundamentals, Advances, and Applied Research

With traditional networking, users can configure control plane protocols to match the specific network configuration, but without the ability to fundamentally change the underlying algorithms. With SDN, the users may provide their own control plane, that can control network devices through their data plane APIs. Programmable data planes allow users to define their own data plane algorithms for network devices including appropriate data plane APIs which may be leveraged by user-defined SDN control. Thus, programmable data planes and SDN offer great flexibility for network customization, be it for specialized, commercial appliances, e.g., in 5G or data center networks, or for rapid prototyping in industrial and academic research. Programming protocol-independent packet processors (P4) has emerged as the currently most widespread abstraction, programming language, and concept for data plane programming. It is developed and standardized by an open community and it is supported by various software and hardware platforms. In this paper, we survey the literature from 2015 to 2020 on data plane programming with P4. Our survey covers 497 references of which 367 are scientific publications. We organize our work into two parts. In the first part, we give an overview of data plane programming models, the programming language, architectures, compilers, targets, and data plane APIs. We also consider research efforts to advance P4 technology. In the second part, we analyze a large body of literature considering P4-based applied research. We categorize 241 research papers into different application domains, summarize their contributions, and extract prototypes, target platforms, and source code availability.Comment: Submitted to IEEE Communications Surveys and Tutorials (COMS) on 2021-01-2