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Yang, Li-hua, Huang, Hua.  2019.  A Classification Method of Ancient Ceramics Based on Support Vector Machine in Ceramic Cloud Service Platform. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :108–112.
To efficiently provide the ancient ceramic composition analysis and testing services, it is necessary to efficiently classify the ancient ceramics in ceramic cloud service platform. In this paper, we get the 8 kinds of major chemical contents of the body and glaze in each sample according to analyze 35 samples. After establishing of the classification model of two samples, the results indicate: as long as choosing SVM algorithm correctly, the classification results of body and glaze samples will be quite ideal, and the support vector machine is a very valuable new method which can process ancient porcelains data.
Islam, M. N., Patil, V. C., Kundu, S..  2017.  Determining proximal geolocation of IoT edge devices via covert channel. 2017 18th International Symposium on Quality Electronic Design (ISQED). :196–202.

Many IoT devices are part of fixed critical infrastructure, where the mere act of moving an IoT device may constitute an attack. Moving pressure, chemical and radiation sensors in a factory can have devastating consequences. Relocating roadside speed sensors, or smart meters without knowledge of command and control center can similarly wreck havoc. Consequently, authenticating geolocation of IoT devices is an important problem. Unfortunately, an IoT device itself may be compromised by an adversary. Hence, location information from the IoT device cannot be trusted. Thus, we have to rely on infrastructure to obtain a proximal location. Infrastructure routers may similarly be compromised. Therefore, there must be a way to authenticate trusted routers remotely. Unfortunately, IP packets may be blocked, hijacked or forged by an adversary. Therefore IP packets are not trustworthy either. Thus, we resort to covert channels for authenticating Internet packet routers as an intermediate step towards proximal geolocation of IoT devices. Several techniques have been proposed in the literature to obtain the geolocation of an edge device, but it has been shown that a knowledgeable adversary can circumvent these techniques. In this paper, we survey the state-of-the-art geolocation techniques and corresponding adversarial countermeasures to evade geolocation to justify the use of covert channels on networks. We propose a technique for determining proximal geolocation using covert channel. Challenges and directions for future work are also explored.