Quantum Internet: from Communication to Distributed Computing!

In this invited paper, the authors discuss the exponential computing speed-up achievable by interconnecting quantum computers through a quantum internet. They also identify key future research challenges and open problems for quantum internet design and deployment.Comment: 4 pages, three figures, invited pape

D-STREAMON: from middlebox to distributed NFV framework for network monitoring

Many reasons make NFV an attractive paradigm for IT security: lowers costs, agile operations and better isolation as well as fast security updates, improved incident responses and better level of automation. On the other side, the network threats tend to be increasingly complex and distributed, implying huge traffic scale to be monitored and increasingly strict mitigation delay requirements. Considering the current trend of the net- working and the requirements to counteract to the evolution of cyber-threats, it is expected that also network monitoring will move towards NFV based solutions. In this paper, we present D- StreaMon an NFV-capable distributed framework for network monitoring realized to face the above described challenges. It relies on the StreaMon platform, a solution for network monitoring originally designed for traditional middleboxes. An evolution path which migrates StreaMon from middleboxes to Virtual Network Functions (VNFs) has been realized.Comment: Short paper at IEEE LANMAN 2017. arXiv admin note: text overlap with arXiv:1608.0137

A survey on the application of deep learning for code injection detection

Abstract Code injection is one of the top cyber security attack vectors in the modern world. To overcome the limitations of conventional signature-based detection techniques, and to complement them when appropriate, multiple machine learning approaches have been proposed. While analysing these approaches, the surveys focus predominantly on the general intrusion detection, which can be further applied to specific vulnerabilities. In addition, among the machine learning steps, data preprocessing, being highly critical in the data analysis process, appears to be the least researched in the context of Network Intrusion Detection, namely in code injection. The goal of this survey is to fill in the gap through analysing and classifying the existing machine learning techniques applied to the code injection attack detection, with special attention to Deep Learning. Our analysis reveals that the way the input data is preprocessed considerably impacts the performance and attack detection rate. The proposed full preprocessing cycle demonstrates how various machine-learning-based approaches for detection of code injection attacks take advantage of different input data preprocessing techniques. The most used machine learning methods and preprocessing stages have been also identified

Proposal for a comprehensive bandwidth management scheme and connection acceptance rule for B-ISDN

A feasible and cost-effective resource management scheme is urgently needed in the Broadband Integrated Services Digital Network adopting the Asynchronous Transfer Mode (ATM) technique. In this paper we propose a simple and comprehensive strategy to manage bandwidth allocations, congestion control and quality of service (QOS) integrity in a multi-service ATM network. The proposed framework involves a core network that grants a limited number of grade of service (GOS) profiles and suggest the design of edge-adaptors able to match QOS user\u27s requirements with associated connection acceptance algorithms are presented. Also, for some particular QOS requirements, the edge-adapter dimensioning process is developed. The effectiveness of the scheme is demonstrated by numerical examples which report the network utilization performance when the proposed core network policies and edge adapter design are applied

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