Malware API Sequence Detection Model based on Pre-trained BERT in Professional domain

Rongheng Xu, Jilin Zhang, Li Zhou
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Abstract

With the development of the Internet, Internet information security is becoming more and more important. As far as malware detection is concerned, the increasingly serious distortion and scrambling have brought great challenges to the traditional detection methodstraditional methods such as feature database are difficult to effectively detect non-input viruses, and there is a very high cost of experts in detection. With the development of artificial intelligence technology, machine learning and deep learning methods are widely used to deal with tasks in the computer field. In dynamic detection, API call sequences generated by malicious software are widely used in software classification as features, because these sequences represent the behaviors of malicious software. However, traditional methods cannot capture the global relationship of API sequences. We use the BERT model based on transformer to learn the global relationship and add Windows API corpus to the pre-training model.
基于专业领域预训练BERT的恶意软件API序列检测模型
随着互联网的发展,互联网信息安全变得越来越重要。就恶意软件检测而言,日益严重的失真和置乱给传统的检测方法带来了极大的挑战,传统的特征库等方法难以有效检测出非输入型病毒,且检测专家的成本非常高。随着人工智能技术的发展,机器学习和深度学习方法被广泛用于处理计算机领域的任务。在动态检测中,恶意软件产生的API调用序列作为特征被广泛用于软件分类,因为这些序列代表了恶意软件的行为。然而,传统的方法无法捕捉API序列之间的全局关系。我们使用基于transformer的BERT模型来学习全局关系,并在预训练模型中加入Windows API语料库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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