Non-destructive Raman spectroscopy has been used to study ancient ceramics. On the basis of spectral features characteristic to the microstructures, the composition and technological processing of ceramics in ancient times could be quantitatively determined. Ceramics are heterogeneous materials composed of grains of different phases, coated by different glazes containing various pigments. The question of reliability and representation of the Raman spectra recorded from the surface of glaze or on a section of shard is discussed. As an illustration, ancient (13-14th centuries) Vietnamese (proto)porcelains made at Ha Lan (Nam Dinh) were studied with particular attention to the analysis of the SiO 4 -based glassy network: Spectral components of the Si-O stretching mode are analysed in terms of isolated (Q 0 ), more or less associated (Q 1 , Q 2 , Q 3 ) or fully-bonded (Q 4 ) SiO 4 tetrahedra. The results show the facility and reliability of Raman spectroscopy as a non-destructive technique suitable for discrimination between ancient ceramics and modern copies.

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Keeping Internet users protected from cyberattacks and other threats is one of the most prominent security challenges for network operators nowadays. Among other critical threats, distributed denial-of-service (DDoS) becomes one of the most widespread attacks in the Internet, which is very challenging to mitigate appropriately as DDoS attacks cause the system to stop working by resource exhaustion. Software-defined networking (SDN) has recently emerged as a new networking technology offering unprecedented programmability that allows network operators to configure and manage their infrastructures dynamically. The flexible processing and centralized management of the SDN controller allow flexibly deploying complex security algorithms and mitigation methods. In this paper, we propose a novel DDoS attack mitigation in SDN-based Internet Service Provider (ISP) networks for TCP-SYN and ICMP flood attacks utilizing machine learning approach, i.e., K-Nearest-Neighbor (KNN) and XGBoost. By deploying a testbed, we implement the proposed algorithms, evaluate their accuracy, and address the trade-off between the accuracy and mitigation efficiency. Through extensive experiments, the results show that the algorithms can efficiently mitigate the attack by over 98.0% while benign traffic is not affected.