Biblio

Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area.

Securing multi-party communication is very challenging particularly in dynamic networks. Existing multi-recipient cryptographic schemes pose variety of limitations. These include: requiring trust among all recipients to make an agreement, high computational cost for both encryption and decryption, and additional communication overhead when group membership changes. To overcome these limitations, this paper introduces a novel multi-recipient asymmetric cryptographic scheme, AMOUN. This scheme enables the sender to possibly send different messages in one ciphertext to multiple recipients to better utilize network resources, while ensuring that each recipient only retrieves its own designated message. Security analysis demonstrates that proposed scheme is secure against well-known attacks. Evaluation results demonstrate that lightweight AMOUN outperforms RSA and Multi-RSA in terms of computational cost for both encryption and decryption. For a given prime size, in case of encryption, AMOUN achieves 86% and 98% lower average computational cost than RSA and Multi-RSA, respectively; while for decryption, it shows performance improvement of 98% compared to RSA and Multi-RSA.