生詞： The detection of malicious software (malware) is an important problem in cyber security, especially as more of society becomes dependent on computing systems. Already, single incidences of malware can cause millions of dollars in damages (Anderson et al. 2013). Anti-virus products provide some protection against malware, but are growing increasingly ineffective for the problem. Current anti-virus technologies use a signature-based approach, where a signature is a set of manually crafted rules in an attempt to identify a small family of malware. These rules are generally specific, and cannot usually recognize new malware even if it uses the same functionality. This approach is insufficient as most environments will have unique binaries that will have never been seen before (Li et al. 2017) and millions of new malware samples are found every day. The limitations of signatures have been recognized by the anti-virus providers and industry experts for many years (Spafford 2014). The need to develop techniques that generalize to new malware would make the task of malware detection a seemingly perfect fit for machine learning, though there exist significant challenges 對惡意軟件（malware）的檢測是網絡安全中的一個重要問題，特別是隨著社會越來越依賴于計算機系統。 惡意軟件的單一事件可已經以造成數百萬美元的損失（安德森等人。2013年）。 反病毒產品提供了一些針對惡意軟件的保護，但對這個問題的效果越來越差。當前的反病毒技術使用基于簽名的方法，其中簽名是一組手動創建的規則，試圖識別一個小的惡意軟件。 這些規則通常是特定的，并且通常無法識別新的惡意軟件，即使它使用相同的功能。 這種方法是不夠的，因為大多數環境都有以前從未見過的獨特二進制文件（Li等人。2017年），每天都會發現數百萬個新的惡意軟件樣本。 多年來，反病毒提供商和行業專家一直認識到簽名的局限性（Spafford 2014）。 盡管存在著巨大的挑戰，但開發能夠推廣到新惡意軟件的技術的需要，會使惡意軟件檢測任務看起來非常適合機器學習

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