AIにより推定されたラベル
KiloGramアルゴリズム 特徴重要度分析 PEマルウェア分類
※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。
Abstract
N-grams have been a common tool for information retrieval and machine learning applications for decades. In nearly all previous works, only a few values of n are tested, with n > 6 being exceedingly rare. Larger values of n are not tested due to computational burden or the fear of overfitting. In this work, we present a method to find the top-k most frequent n-grams that is 60× faster for small n, and can tackle large n ≥ 1024. Despite the unprecedented size of n considered, we show how these features still have predictive ability for malware classification tasks. More important, large n-grams provide benefits in producing features that are interpretable by malware analysis, and can be used to create general purpose signatures compatible with industry standard tools like Yara. Furthermore, the counts of common n-grams in a file may be added as features to publicly available human-engineered features that rival efficacy of professionally-developed features when used to train gradient-boosted decision tree models on the EMBER dataset.