End-to-End QoS "Smart Queue" Management Algorithms and Traffic Prioritization Mechanisms for Narrow-Band Internet of Things Services in 4G/5G Networks.

This paper proposes a modified architecture of the Long-Term Evolution (LTE) mobile network to provide services for the Internet of Things (IoT). This is achieved by allocating a narrow bandwidth and transferring the scheduling functions from the eNodeB base station to an NB-IoT controller. A method for allocating uplink and downlink resources of the LTE/NB-IoT hybrid technology is applied to ensure the Quality of Service (QoS) from end-to-end. This method considers scheduling traffic/resources on the NB-IoT controller, which allows eNodeB planning to remain unchanged. This paper also proposes a prioritization approach within the IoT traffic to provide End-to-End (E2E) QoS in the integrated LTE/NB-IoT network. Further, we develop "smart queue" management algorithms for the IoT traffic prioritization. To demonstrate the feasibility of our approach, we performed a number of experiments using simulations. We concluded that our proposed approach ensures high end-to-end QoS of the real-time traffic by reducing the average end-to-end transmission delay.

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection.

In this paper, to solve the problem of detecting network anomalies, a method of forming a set of informative features formalizing the normal and anomalous behavior of the system on the basis of evaluating the Hurst (H) parameter of the network traffic has been proposed. Criteria to detect and prevent various types of network anomalies using the Three Sigma Rule and Hurst parameter have been defined. A rescaled range (RS) method to evaluate the Hurst parameter has been chosen. The practical value of the proposed method is conditioned by a set of the following factors: low time spent on calculations, short time required for monitoring, the possibility of self-training, as well as the possibility of observing a wide range of traffic types. For new DPI (Deep Packet Inspection) system implementation, algorithms for analyzing and captured traffic with protocol detection and determining statistical load parameters have been developed. In addition, algorithms that are responsible for flow regulation to ensure the QoS (Quality of Services) based on the conducted static analysis of flows and the proposed method of detection of anomalies using the parameter Hurst have been developed. We compared the proposed software DPI system with the existing SolarWinds Deep Packet Inspection for the possibility of network traffic anomaly detection and prevention. The created software components of the proposed DPI system increase the efficiency of using standard intrusion detection and prevention systems by identifying and taking into account new non-standard factors and dependencies. The use of the developed system in the IoT communication infrastructure will increase the level of information security and significantly reduce the risks of its loss.