When a site is damaged by a major disaster, there is a risk of large-scale data loss within the premises or isolation from the Internet due to network disruption. To achieve both data durability and availability from the disaster area, Risk-aware Data Protection (RDP) has been proposed to distribute redundant data to neighboring sites based on the disaster risk. However, RDP has only been studied using a method called replication, while Erasure Coding (EC) applied to RDP is still unexplored. Therefore, the challenge of this study is to quantitatively compare the effectiveness of these methods in RDP. For this purpose, we formulate evaluation indicators to measure the remaining data ratio after a disaster; and then propose how to apply several existing combinatorial optimization algorithms to RDP. Based on the results of experiments, we determined that EC performs better than replication if the probability of site damage is small.

This paper proposes a Software-Defined Network (SDN)-based Moving Target Defense (MTD) mechanism to protect the network from potential scans. The proposed mechanism can work in combination with an IPS without affecting its normal behavior. To do so, an SDN controller changes the packets' headers passing through switches using virtual IP addresses while the operation of IPS continues monitoring the devices' actual IP addresses. Preliminary results in an emulated environment show that it is possible to achieve a seamless collaboration between the MTD and IDS to detect low and high-rate scans.

SDN is an emerging network paradigm whose main characteristic is the separation of the Control from the Data Plane, allowing the implementation of innovative, robust, and flexible ways to program networks. Moreover, SDN has recently extended its coverage to Hybrid wired and/or wireless environments. However, SDN-enabled devices must be connected to a controller (i.e., a central entity) to be programmable. Therefore, if multiple controllers fail in a short period, regardless of the ample resource availability in Hybrid SDN environments, the whole network infrastructure, and the overall service are compromised. This article presents a mechanism capable of handling multi-controller failure in SDN by considering the hybrid nature of the infrastructure and the scale of the failure. Preliminary results show that by applying the proposed mechanism, it is possible to increase the device's controller coverage by up to 70% even if half of the controllers are unavailable compared to conventional approaches.

Software Defined Networking (SDN) has recently drawn attention by changing the network control paradigm. SDN devices handle the network traffic based on entries stored in their flow table. However, due to the flow table’s limited size, attacks such as DDoS can cause the table to overflow. Some of the current table overflow mitigation methods distribute flow entries to other network devices on the physical infrastructure, which might not be effective in small to medium-sized private networks. Therefore, this research proposes a method to mitigate table overflow attacks on SDN by dynamically filtering attackers using Network Function Virtualization (NFV). Preliminary results show that the proposed approach can effectively mitigate table overflow attacks without relying on network infrastructure.

Over the past years, various anomaly detection techniques for Wireless Sensor Networks (WSNs) have been proposed in the literature. These detectors are generally only effective against particular security threats and thus many detectors have to be executed to ensure high-level security in WSNs. Due to the hardware limitations, WSNs cannot execute all of these detectors simultaneously. Thus, WSNs have to appropriately schedule when to execute a particular detector and when to execute the others. This paper presents an optimized solution by providing a unified framework that combines the usage of a costly yet highly-accurate detector, called an oracle detector, and a low cost and less-accurate detector, called a partial detector. A detection pattern is created and optimized using a first-order approximation method. Simulation results show that combining several detectors into an optimized pattern is essential to provide low execution time overhead.

There are various information flows in the network because of the diverse ways in which companies and individuals communicate. Therefore, OpenFlow is attracting much attention since it can flexibly deal with various types of information flow requirements through centralized management of devices. However, whenever is necessary to change routes due to a network failure or a topology change, there is a delay in the service as processes, such as the transmission of the commands and switch processing time. In this paper, we describe the design a method to control information flow effectively based on various network switch costs such as route switching time in OpenFlow.

In this paper, we propose a network control method considering the disaster risk of OpenFlow control and management plane (C/M-Plane). We consider the disaster risk of both data plane (D-Plane) and the C/M-Plane. Even if the C/M-Plane is damaged and the switch becomes uncontrollable, the switch operates in the state at that time. Our method controls the entire network to keep communication according to it to maintain data transfer even if some switches become uncontrollable.

Wireless Sensor Networks (WSNs) typically consist of failure-prone sensor nodes (SN) and more reliable sink nodes (SK). To prevent data loss due to hardware failures, the data stored in SNs' memory must be regularly transferred to SKs. Hence, choosing an appropriate data transfer rate between SN and SK is important to minimize data loss in a faulty WSN. This paper presents an analytical method to derive the optimal data transfer rate of the SN based on a Markov Model.

Due to the increasing need for timely and reliable access to user-generated content, Distributed Storage Systems (DSS) became more relevant in the past years. However, since they have more interconnections among layers compared to traditional network applications, load imbalance issues arise. In this paper, we propose a hybrid approach combining server and link load balancing for multipath routing in DSS. The approach is Software Defined Networking (SDN) based, and uses a process we call on-demand inverse multiplexing. Preliminary results show that, by applying the proposal, the overall throughput considerably increases and resource usage remain balanced.

The use of Distributed Storage Systems (DSS) has considerably increased in the past years, alongside the need for effective data transfer from storage to storage. Although current network infrastructure can reliably handle large amounts of traffic, networking techniques have not changed for several years, leading to an under-use of resources, i.e. most routing solutions still use single-path routing. In this paper, we present a pragmatic approach for multipath routing in DSS, which is based on Software Defined Networking (SDN) that uses parallel links at the edge-side. Path discovery is calculated by finding the k-maximum disjoint paths in a multigraph. Preliminary results show that, by using our multipath solution, not only the overall throughput increases but also the efficiency of resources usage.