Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction
A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemen…
In the Direction of Service Guarantees for Virtualized Network Functions
The trend of consolidating network functions from specialized hardware to software running on virtualization servers brings significant advantages for reducing costs and simplifying service deployment. However, virtualization techniques have significant limitations when it comes to networking as there is no support for guaranteeing that network functions meet their service requirements. In this paper, we present a design for providing service guarantees to virtualized network functions based on rate control. The design is a combination of rate regulation through token bucket filters and the regular scheduling mechanisms in operating systems. It has the attractive property that traffic profi…