A Metric to Assist in Detecting International Phishing or Ransomware Cyberattacks

W. Patterson, Jeremy Blacksttone
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Abstract

Over the past decade, the number of cyberattacks such as ransomware, phishing, and other forms of malware have increased significantly, as has the danger to innocent users. The ability to launch such devastating attacks is no longer limited to well-funded, highly structured organizations including government agencies whose missions may well include cyberattacks.The focus of our study is threats to an individual not from such highly organized institutions, but rather less organized cybercriminal organizations with limited resources.The Internet provides ample opportunities for such criminal organizations to launch cyberattacks at minimal cost. One tool for such lower-level criminal organizations is Google Translate (GT) needed to launch a cyberattack on a user in a relatively advantaged country such as the United States, United Kingdom, or Canada. It has been observed that many such attacks may originate in a lesser developed country (LDC), where the local language is a language not common persons in target countries, for example English.It is a reasonable assumption that informal cyberattackers may not have a command of English and to use English for an attack online they may require a mechanism, such as the no-cost GT.In previous work, a number of authors have attempted to develop an index to measure the efficiency or what might be called an ABA translation. This involves beginning with a test document in language A, then GT to translate into language B, then back again to A. The resulting original text is then compared to the transformation by using a modified Levenshtein distance computation for the A versions.The paper analyzes the process of determining an index to detect if a text has been translated from an original language and location, assuming the attack document has been written in one language and translated using GT into the language of the person attacked. The steps involved in this analysis include:a) Consistency: in order to determine consistency in the use of the ABA/GT process, the primary selection of test is compared with random samples from the test media;b) Expanded selection of languages for translation: prior work has established use of the technique for 12 language pairs. The current work extends analysis to a wider set of languages, including those reported as having the highest levels of cyberattacks.c) Back translation of selected languages: used to extend the quality of those translations are made.d) New language pairs are considered: by analyzing the countries and indigenous languages of the countries paired with the highest levels of cyberattack and the highest levels of cyberdefense, additional language pairs are added to this analysis;e) Comparison to prior results: results found in this paper are used for a proposed network for all language pairs considered in this analysis.The end product is a metric giving a probability of determining the original source language of the cyberattack as compared to the translation to the victim's language, with the expectation that this will allow for an increased likelihood of being able to identify the attackers.
协助检测国际网络钓鱼或勒索软件网络攻击的度量
在过去的十年里,勒索软件、网络钓鱼和其他形式的恶意软件等网络攻击的数量显著增加,对无辜用户的威胁也在增加。发动这种毁灭性攻击的能力不再局限于资金充足、结构严密的组织,包括政府机构,它们的任务很可能包括网络攻击。我们研究的重点是对个人的威胁,而不是来自这种高度组织化的机构,而是来自组织较少、资源有限的网络犯罪组织。互联网为这类犯罪组织以最低成本发动网络攻击提供了充足的机会。谷歌Translate (GT)是这种较低级别的犯罪组织使用的工具之一,它可以对美国、英国或加拿大等相对有利的国家的用户发动网络攻击。据观察,许多这类攻击可能起源于较不发达国家,那里的当地语言是目标国家不常见的语言,例如英语。一个合理的假设是,非正式的网络攻击者可能不掌握英语,并且为了使用英语进行在线攻击,他们可能需要一种机制,例如免费的gt。在之前的工作中,许多作者试图开发一个指数来衡量效率,或者可以称为ABA翻译。这包括从语言a的测试文档开始,然后用GT将其翻译成语言B,然后再返回到a。然后通过使用a版本的修改Levenshtein距离计算将生成的原始文本与转换进行比较。本文分析了确定索引以检测文本是否从原始语言和位置翻译的过程,假设攻击文档是用一种语言编写的,并使用GT翻译成被攻击者的语言。该分析涉及的步骤包括:a)一致性:为了确定使用ABA/GT过程的一致性,将测试的主要选择与测试介质中的随机样本进行比较;b)扩展翻译语言的选择:先前的工作已经确定了该技术在12对语言中的使用。目前的工作将分析扩展到更广泛的语言,包括那些被报道为具有最高级别网络攻击的语言。c)选定语言的反翻译:用于提高这些翻译的质量。d)考虑新的语言对:通过分析与最高级别网络攻击和最高级别网络防御配对的国家和本土语言,将额外的语言对添加到此分析中;e)与先前结果的比较:本文中发现的结果用于本分析中考虑的所有语言对的拟议网络。最终产品是一个度量标准,给出了确定网络攻击的原始源语言的概率,与翻译成受害者的语言相比,期望这将增加能够识别攻击者的可能性。
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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