Visible to the public Biblio

Filters: Author is Stoyanov, I. S.  [Clear All Filters]
2020-02-26
Bikov, T. D., Iliev, T. B., Mihaylov, Gr. Y., Stoyanov, I. S..  2019.  Phishing in Depth – Modern Methods of Detection and Risk Mitigation. 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). :447–450.

Nowadays, everyone is living in a digital world with various of virtual experiences and realities, but all of them may eventually cause real threats in our real world. Some of these threats have been born together with the first electronic mail service. Some of them might be considered as really basic and simple, compared to others that were developed and advanced in time to adapt themselves for the security defense mechanisms of the modern digital world. On a daily basis, more than 238.4 billion emails are sent worldwide, which makes more than 2.7 million emails per second, and these statistics are only from the publicly visible networks. Having that information and considering around 60% and above of all emails as threatening or not legitimate, is more than concerning. Unfortunately, even the modern security measures and systems are not capable to identify and prevent all the fraudulent content that is created and distributed every day. In this paper we will cover the most common attack vectors, involving the already mass email infrastructures, the required contra measures to minimize the impact over the corporate environments and what else should be developed to mitigate the modern sophisticated email attacks.

Sokolov, S. A., Iliev, T. B., Stoyanov, I. S..  2019.  Analysis of Cybersecurity Threats in Cloud Applications Using Deep Learning Techniques. 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). :441–446.

In this paper we present techniques based on machine learning techniques on monitoring data for analysis of cybersecurity threats in cloud environments that incorporate enterprise applications from the fields of telecommunications and IoT. Cybersecurity is a term describing techniques for protecting computers, telecommunications equipment, applications, environments and data. In modern networks enormous volume of generated traffic can be observed. We propose several techniques such as Support Vector Machines, Neural networks and Deep Neural Networks in combination for analysis of monitoring data. An approach for combining classifier results based on performance weights is proposed. The proposed approach delivers promising results comparable to existing algorithms and is suitable for enterprise grade security applications.