A Distributed Denial of Service (DDoS) attack occurs when large amounts of traffic from hundreds, thousands, or even millions of other computers are routed to a network or server to crash the system and disrupt its function. These attacks are commonly used to shut down websites or applications temporarily. Such problems often need to be addressed with models that can manage the time information contained in network traffic flows. In this work, we apply a Hybrid Deep Learning method to detect malicious web traffic in the form of DDoS attacks, controlling the web flow of information reaching a server, using any dependencies between the different elements of a data stream. An original and cutting-edge Hierarchical Temporal Memory (HTM) hybrid model has been proposed. The operation of this model is predicated primarily on the portion of the cerebral cortex known as the neocortex. The neocortex is in charge of various fundamental brain functions, including the perception of senses, the comprehension of language, and the control of movement. For the hybrid implementation to be capable of encoding time sequences that incorporate incoming data, a Long Short-Term Memory (LSTM) shell is added.

Adding the adequate level of security of information systems dealing with sensitive data, privacy, or defense systems involves some form of access control. The audits performed are dealing with the determination of the allowed activities of the legal users, when attempting to access resources of the system. Usually, full access is provided after the user has been successfully authenticated through an authentication mechanism (e.g., password), while the corresponding authorization control is based on the confidentiality level of the respective resources and the authorization level assigned to each user. A very important diversification occurring in modern digital technologies is related to the identification based on blockchain technology, which is presented as a public, distributed data series, unable to modify its history and grouped in time-numbered blocks. In this work, a blockchain-based verifiable user data access control policy for secured cloud data storage is suggested for a version associated with big data in health care. It is an innovative system of applying classified access policies to secure resources in the cloud, which operates based on blockchain technology. System evaluation is carried out by studying a case in its resilience to Eclipse attack under different malicious user capabilities for routing table poisoning.