The increasing complexity of modern day networked applications and the massive demand on the Internet resources has reignited interest and concern in the underlying networking infrastructures and their ability to cope with such complexity and adapt to the demands of the business applications, particularly where such applications require a high degree of robustness and reliability. As a result, software-defined networking has emerged as a promising approach to the definition of network architectures that could carry a high degree of adaptability and robustness reminiscent of the future Internet. Fault tolerance and network updates are considered two of the current research challenges that hamper the growth of software-defined networking in this area. Therefore, this paper represents a step toward tackling these two issues in the context of single link failures. Our main contribution lies in the definition of new algorithms that aim to enhance the problem of finding alternative paths in large-scale networks with minimal cost and time-to-update factors. The new solution aims at increasing the efficiency of flow operation reduction during link failures. We evaluate our framework and show how its implementation results in improved efficiency.

There is increasing demand in modern day business applications for communication networks to be robust and reliable due to the complexity and critical nature of such applications. As such, data delivery is expected to be reliable and secure even in the harshest of environments. Software-Defined Networking (SDN) is gaining traction as a promising approach for designing network architectures which are robust and flexible. One reason for this is that separating the data plane from the control plane, increases the controller’s ability to configure the network rapidly. When network failure events occur, the network manager may trade-off the optimality of the achieved network reconfiguration with the responsivenss of the reconfiguration process. Responsiveness may be favoured when the network resources are under stress and the failure rate is high. We contribute SDN recovery methods that leverage information about the structure of the network to expedite network restoration when a link failure occurs. They operate by detecting community-like structures in the network topology and then they find alternative paths which have low operation and installation costs using this information. Extensive simulations are conducted to evaluate the proposed SDN recovery methods using open-source simulation tools. They provide evidence that the proposed approaches lead to performance gains when an alternative path is required among a set of candidate paths.

In this paper we outline the challenges of Web API management in Internet of Things (IoT) projects.Web API management is a key aspect of service-oriented systems that includes the following elements: metadata publishing, access control and key management, monitoring and monetization of interactions, as well as usage control and throttling. We look at how Web API management principles, including some of the above elements, translate into a world of connected devices (IoT). In particular, we present and evaluate a prototype that addresses the issue of managing authentication with millions of insecure low-power devices communicating with non-HTTP protocols. With this first step, we are only beginning to investigate IoT API management, therefore we also discuss necessary future work.

The increasing amount and complexity of Cyber security attacks in recent years have made text analysis and data mining techniques an important factor in discovering features of such attacks and detecting future security threats. In this paper, we report on the results of a recent case study that involved the analysis of a community data set collected from five small and medium companies in Korea. The data set represents Cyber security incidents and response actions. We investigated in the study the kind of problems concerned with the prediction of response actions to future incidents from features of past incidents. Our analysis is based on text mining methods, such as n-gram and bag-of-words, as well as on machine learning algorithms for the classification of incidents and their response actions. Based on the results of the study, we also suggest an experience-sharing model, which we use to demonstrate how companies may share their trained classifiers without the sharing of their individual data sets in a collaborative environment.

The Internet of Things (IoT) has significant security and privacy risks. Recent attacks have shown that not only are many IoT devices at risk of exploit, but those devices can be successfully used to attack wider systems and cause economic damage. Currently, most devices connect to a cloud service that is provided by the manufacturer of the device, offering no choice to move to more secure systems. We outline a proposed model for IoT that allows the identity of users and devices to be federated. Users and devices are issued with secure, random, anonymised identities that are not shared with third parties. We demonstrate how devices can be connected to third-party applications without inherently de-anonymising them. Sensor data and actuator commands are federated through APIs to cloud services. All access to device data and commands is based on explicit consent from users. Each user’s data is handled by a personal cloud instance providing improved security and isolation, as well as providing a trusted intermediary for both devices and cloud services. We demonstrate this model is workable with a prototype system that implements the major features of the model. We present experiment results including performance, energy usage, capacity and cost metrics from the prototype. We compare this work with other related work and outline areas for discussion and future work.