No abstract

Diamond electrolyte solution-gate-field effect transistors (SGFETs) are suitable for applications as chemical ion sensors because of their wide potential window and good physical and chemical stabilities. In this study, we fabricated an anodically oxidized diamond SGFET from a full hydrogen-terminated diamond SGFET and demonstrated control of the device threshold voltage by irreversible anodic oxidation. The applied anodic bias voltage (VAO) was varied gradually from low to high (1.1–1.7 V). As the anodic oxidation proceeded, the threshold voltage shifted to more negative values with no degradation of hole mobility. Thus, anodic oxidation is a useful method for controlling the threshold voltage of diamond SGFETs.

Sho MIZUNO†a) , Nonmember, Mitsuhiro HATADA †, † †b) , Tatsuya MORI †c) , and Shigeki GOTO †d) , Members SUMMARY Damage caused by malware has become a serious problem. The recent rise in the spread of evasive malware has made it difficult to detect it at the pre-infection timing. Malware detection at post-infection timing is a promising approach that fulfills this gap. Given this background, this work aims to identify likely malware-infected devices from the measurement of Internet traffic. The advantage of the traffic-measurementbased approach is that it enables us to monitor a large number of endhosts. If we find an endhost as a source of malicious traffic, the endhost is likely a malware-infected device. Since the majority of malware today makes use of the web as a means to communicate with the C&C servers that reside on the external network, we leverage information recorded in the HTTP headers to discriminate between malicious and benign traffic. To make our approach scalable and robust, we develop the automatic template generation scheme that drastically reduces the amount of information to be kept while achieving the high accuracy of classification; since it does not make use of any domain knowledge, the approach should be robust against changes of malware. We apply several classifiers, which include machine learning algorithms, to the extracted templates and classify traffic into two categories: malicious and benign. Our extensive experiments demonstrate that our approach discriminates between malicious and benign traffic with up to 97.1% precision while maintaining the false positive rate below 1.0%.