@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00106615,
 author = {愛甲, 健二 and 松木, 隆宏 and Kenji, Aiko and Takahiro, Matsuki},
 book = {コンピュータセキュリティシンポジウム2014論文集},
 issue = {2},
 month = {Oct},
 note = {数理統計的手法により実現されたマルウェア検知アルゴリズムは，学習と評価が保持するデータセットに依存するためその有用性の証明が難しい．本稿ではマルウェアの時系列データに着目し，対象となる検知アルゴリズムの性能が持続する期間を調査，推定し，検知精度の低下と再学習が必要となる時期を予測する手法を提案する．, The malware detection algorithm implemented by mathematical statistical methods, proof of its usefulness is difficult because it depends on the data set. In this paper, focusing on time-series data of malwares, we propose a method to investigate the period in which the performance of the detection algorithm of interest is sustained, it is estimated, to predict the timing of re-learning and lowering of the detection accuracy is required.},
 pages = {704--710},
 publisher = {情報処理学会},
 title = {時系列データに基づくマルウェア検知アルゴリズムの評価},
 volume = {2014},
 year = {2014}
}