Biblio

The Software Defined Network (SDN) provides higher programmable functionality for network configuration and management dynamically. Moreover, SDN introduces a centralized management approach by dividing the network into control and data planes. In this paper, we introduce a deep learning enabled intrusion detection and prevention system (DL-IDPS) to prevent secure shell (SSH) brute-force attacks and distributed denial-of-service (DDoS) attacks in SDN. The packet length in SDN switch has been collected as a sequence for deep learning models to identify anomalous and malicious packets. Four deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Stacked Auto-encoder (SAE), are implemented and compared for the proposed DL-IDPS. The experimental results show that the proposed MLP based DL-IDPS has the highest accuracy which can achieve nearly 99% and 100% accuracy to prevent SSH Brute-force and DDoS attacks, respectively.

The Internet, the de facto platform for large-scale content distribution, suffers from two issues that limit its manageability, efficiency, and evolution. First, the IP-based Internet is host-centric and agnostic to the content being delivered and, second, the tight coupling of the control and data planes restrict its manageability, and subsequently the possibility to create dynamic alternative paths for efficient content delivery. Here, we present the CURLING system that leverages the emerging Information-Centric Networking paradigm for enabling cost-efficient Internet-scale content delivery by exploiting multicasting and in-network caching. Following the software-defined networking concept that decouples the control and data planes, CURLING adopts an interdomain hop-by-hop content resolution mechanism that allows network operators to dynamically enforce/change their network policies in locating content sources and optimizing content delivery paths. Content publishers and consumers may also control content access according to their preferences. Based on both analytical modeling and simulations using real domain-level Internet subtopologies, we demonstrate how CURLING supports efficient Internet-scale content delivery without the necessity for radical changes to the current Internet.

Software-Defined Network (SDN) is the dynamic network technology to address the issues of traditional networks. It provides centralized view of the whole network through decoupling the control planes and data planes of a network. Most SDN-based security services globally detect and block a malicious host based on IP address. However, the IP address is not verified during the forwarding process in most cases and SDN-based security service may block a normal host with forged IP address in the whole network, which means false-positive. In this paper, we introduce an attack scenario that uses forged packets to make the security service consider a victim host as an attacker so that block the victim. We also introduce cost-effective risk avoidance strategy.