Currently, the IT market is promoting the use of cloud resources to build solutions using machine learning. Some projects require full independence from external resources, mainly due to the volatility of prices for renting computing power and services provided by the cloud. The paper presents the creation process of scalable “on-premise” environment en-abling the construction and development of artificial intelligence systems in the field of natural language processing. The proposed approach was based on aspects related to containerization, scalability, and automation of the machine learning process. Therefore, the created “on-premise” environment can be used in the implementation and delivery of systems based on artificial intelligence.

Vehicular Ad-Hoc Networks (VANET) are liable to the Black, Worm and Gray Hole attacks because of the broadcast nature of the wireless medium and a lack of authority standards. Black Hole attack covers the situation when a malicious node uses its routing protocol in order to publicize itself for having the shortest route to the destination node. This aggressive node publicizes its availability of fresh routes regardless of checking its routing table. The consequences of these attacks could lead not only to the broken infrastructure, but could cause hammering people's lives. This paper aims to investigate and compare methods for preventing such types of attacks in a VANET.

The vehicles that are currently manufactured have become increasingly related to the Internet, supporting a range of recent features that are valuable towards both automakers and drivers. Vehicular Ad-Hoc Networks (VANET) have recently emerged collectively of the solutions that would facilitate connecting many cars to the vast network. Not solely it may preserve the protection, but also the efficiency of the traffic. However, VANETs themselves are prone to attacks. Those attacks could directly cause the corruption of networks and so presumably provoke a substantial loss of driver's time, money, and even their lives. The paper focuses on the Timing and Bogus attacks and presents the attainable solutions that would solve the safety leaks in the vehicle networks.