Emerging network services and subsequent growth in the networking infrastructure have gained tremendous momentum in recent years. Application performance requiring rapid real-time network provisioning, optimized traffic management, and virtualization of shared resources has induced the conceptualization and adoption of new networking models. Software defined networking (SDN), one of the predominant and relatively new networking paradigms, seeks to simplify network management by decoupling network control logic from the underlying hardware and introduces real-time network programmability enabling innovation. The present work reviews the state of the art in software defined networking providing a historical perspective on complementary technologies in network programmability and the inherent shortcomings which paved the way for SDN. The SDN architecture is discussed along with popular protocols, platforms, and existing simulation and debugging solutions. Furthermore, a detailed analysis is presented around recent SDN development and deployment avenues ranging from mobile communications and data centers to campus networks and residential environments. The review concludes by highlighting implementation challenges and subsequent research directions being pursued in academia and industry to address issues related to application performance, control plane scalability and design, security, and interdomain connectivity in the context of SDN.

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Traffic classification utilizing flow measurement enables operators to perform essential network management. Flow accounting methods such as NetFlow are, however, considered inadequate for classification requiring additional packet-level information, host behaviour analysis, and specialized hardware limiting their practical adoption. This paper aims to overcome these challenges by proposing two-phased machine learning classification mechanism with NetFlow as input. The individual flow classes are derived per application throughk-means and are further used to train a C5.0 decision tree classifier. As part of validation, the initial unsupervised phase used flow records of fifteen popular Internet applications that were collected and independently subjected tok-means clustering to determine unique flow classes generated per application. The derived flow classes were afterwards used to train and test a supervised C5.0 based decision tree. The resulting classifier reported an average accuracy of 92.37% on approximately 3.4 million test cases increasing to 96.67% with adaptive boosting. The classifier specificity factor which accounted for differentiating content specific from supplementary flows ranged between 98.37% and 99.57%. Furthermore, the computational performance and accuracy of the proposed methodology in comparison with similar machine learning techniques lead us to recommend its extension to other applications in achieving highly granular real-time traffic classification.

PurposeThe purpose of this paper is to investigate the level of susceptibility to social engineering amongst staff within a cooperating organisation.Design/methodology/approachAn e‐mail‐based experiment was conducted, in which 152 staff members were sent a message asking them to follow a link to an external web site and install a claimed software update. The message utilised a number of social engineering techniques, but was also designed to convey signs of a deception in order to alert security‐aware users. The external web site, to which the link was pointing, was intentionally badly designed in the hope of raising the users' suspicions and preventing them from proceeding with the software installation.FindingsIn spite of a short window of operation for the experiment, the results revealed that 23 per‐cent of recipients were fooled by the attack, suggesting that many users lack a baseline level of security awareness that is useful to protect them online.Research limitations/implicationsAfter running for approximately 3.5 h, the experiment was ceased, after a request from the organisation's IT department. Thus, the correct percentage of unique visits is likely to have been higher. Also, the mailings were sent towards the end of a working day, thus limiting the number of people who got to read and respond to the message before the experiment was ended.Practical implicationsDespite its limitations, the experiment clearly revealed a significant level of vulnerability to social engineering attacks. As a consequence, the need to raise user awareness of social engineering and the related techniques is crucial.Originality/valueThis paper provides further evidence of users' susceptibility to the problems, by presenting the results of an e‐mail‐based social engineering study that was conducted amongst staff within a cooperating organisation.

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