训练语言模型以学习命令语法

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2024-07-03 DOI:10.1016/j.array.2024.100355
Zafar Hussain , Jukka K. Nurminen , Perttu Ranta-aho
{"title":"训练语言模型以学习命令语法","authors":"Zafar Hussain ,&nbsp;Jukka K. Nurminen ,&nbsp;Perttu Ranta-aho","doi":"10.1016/j.array.2024.100355","DOIUrl":null,"url":null,"abstract":"<div><p>To protect systems from malicious activities, it is important to differentiate between valid and harmful commands. One way to achieve this is by learning the syntax of the commands, which is a complex task because of the expansive and evolving nature of command syntax. To address this, we harnessed the power of a language model. Our methodology involved constructing a specialized vocabulary from our commands dataset, and training a custom tokenizer with a Masked Language Model head, resulting in the development of a BERT-like language model. This model exhibits proficiency in learning command syntax by predicting masked tokens. In comparative analyses, our language model outperformed the Markov Model in categorizing commands using clustering algorithms (DBSCAN, HDBSCAN, OPTICS). The language model achieved higher Silhouette scores (0.72, 0.88, 0.85) compared to the Markov Model (0.53, 0.25, 0.06) and demonstrated significantly lower noise levels (2.63%, 5.39%, 8.49%) versus the Markov Model’s higher noise rates (9.31%, 29.85%, 50.35%). Further validation with manually crafted syntax and BERTScore assessments consistently produced metrics above 0.90 for precision, recall, and F1-score. Our language model excels at learning command syntax, enhancing protective measures against malicious activities.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"23 ","pages":"Article 100355"},"PeriodicalIF":2.3000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000213/pdfft?md5=68aae0cad29d029f8b3ee94e2999445f&pid=1-s2.0-S2590005624000213-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Training a language model to learn the syntax of commands\",\"authors\":\"Zafar Hussain ,&nbsp;Jukka K. Nurminen ,&nbsp;Perttu Ranta-aho\",\"doi\":\"10.1016/j.array.2024.100355\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To protect systems from malicious activities, it is important to differentiate between valid and harmful commands. One way to achieve this is by learning the syntax of the commands, which is a complex task because of the expansive and evolving nature of command syntax. To address this, we harnessed the power of a language model. Our methodology involved constructing a specialized vocabulary from our commands dataset, and training a custom tokenizer with a Masked Language Model head, resulting in the development of a BERT-like language model. This model exhibits proficiency in learning command syntax by predicting masked tokens. In comparative analyses, our language model outperformed the Markov Model in categorizing commands using clustering algorithms (DBSCAN, HDBSCAN, OPTICS). The language model achieved higher Silhouette scores (0.72, 0.88, 0.85) compared to the Markov Model (0.53, 0.25, 0.06) and demonstrated significantly lower noise levels (2.63%, 5.39%, 8.49%) versus the Markov Model’s higher noise rates (9.31%, 29.85%, 50.35%). Further validation with manually crafted syntax and BERTScore assessments consistently produced metrics above 0.90 for precision, recall, and F1-score. Our language model excels at learning command syntax, enhancing protective measures against malicious activities.</p></div>\",\"PeriodicalId\":8417,\"journal\":{\"name\":\"Array\",\"volume\":\"23 \",\"pages\":\"Article 100355\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2590005624000213/pdfft?md5=68aae0cad29d029f8b3ee94e2999445f&pid=1-s2.0-S2590005624000213-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Array\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590005624000213\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Array","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590005624000213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

摘要

要保护系统免受恶意活动的侵害,必须区分有效命令和有害命令。实现这一目标的方法之一是学习命令的语法,但由于命令语法的扩展性和演变性,这是一项复杂的任务。为此,我们利用了语言模型的强大功能。我们的方法包括从命令数据集中构建专门的词汇表,并使用屏蔽语言模型头训练自定义标记器,从而开发出类似于 BERT 的语言模型。该模型通过预测掩码标记来熟练学习命令语法。在比较分析中,我们的语言模型在使用聚类算法(DBSCAN、HDBSCAN、OPTICS)对命令进行分类方面的表现优于马尔可夫模型。与马尔可夫模型(0.53、0.25、0.06)相比,语言模型获得了更高的 Silhouette 分数(0.72、0.88、0.85),噪声水平(2.63%、5.39%、8.49%)也明显低于马尔可夫模型较高的噪声率(9.31%、29.85%、50.35%)。使用人工编写的语法和 BERTScore 评估进行进一步验证后,精确度、召回率和 F1 分数均超过了 0.90。我们的语言模型在学习命令语法方面表现出色,增强了针对恶意活动的保护措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Training a language model to learn the syntax of commands

To protect systems from malicious activities, it is important to differentiate between valid and harmful commands. One way to achieve this is by learning the syntax of the commands, which is a complex task because of the expansive and evolving nature of command syntax. To address this, we harnessed the power of a language model. Our methodology involved constructing a specialized vocabulary from our commands dataset, and training a custom tokenizer with a Masked Language Model head, resulting in the development of a BERT-like language model. This model exhibits proficiency in learning command syntax by predicting masked tokens. In comparative analyses, our language model outperformed the Markov Model in categorizing commands using clustering algorithms (DBSCAN, HDBSCAN, OPTICS). The language model achieved higher Silhouette scores (0.72, 0.88, 0.85) compared to the Markov Model (0.53, 0.25, 0.06) and demonstrated significantly lower noise levels (2.63%, 5.39%, 8.49%) versus the Markov Model’s higher noise rates (9.31%, 29.85%, 50.35%). Further validation with manually crafted syntax and BERTScore assessments consistently produced metrics above 0.90 for precision, recall, and F1-score. Our language model excels at learning command syntax, enhancing protective measures against malicious activities.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信