利用实时数据挖掘构建针对网络犯罪的自适应防御

Baber Majid Bhatti, N. Sami
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引用次数: 4

摘要

在当今瞬息万变的世界,网络犯罪正以令人不安的速度增长。就其定义而言,网络犯罪是通过利用威胁和漏洞而产生的。然而,最近的历史表明,这类犯罪往往出人意料,很少遵循趋势。这使得防御系统在竞争中落后,因为它们无法识别网络犯罪的新模式,也无法改善到所需的安全水平。本文通过实时数据挖掘可视化安全系统的授权,这些系统将能够动态识别网络犯罪的模式。这将有助于这些安全系统加强其防御能力,同时适应新出现的模式所要求的水平。为了限制在本文的范围内,这种方法的应用是在选定的网络犯罪场景的背景下进行讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building adaptive defense against cybercrimes using real-time data mining
In today's fast changing world, cybercrimes are growing at perturbing pace. At the very definition of it, cybercrimes get engendered by capitalizing on threats and exploitation of vulnerabilities. However, recent history reveals that such crimes often come with surprises and seldom follow the trends. This puts the defense systems behind in the race, because of their inability to identify new patters of cybercrime and to ameliorate to the required levels of security. This paper visualizes the empowerment of security systems through real-time data mining by the virtue of which these systems will be able to dynamically identify patterns of cybercrimes. This will help those security systems stepping up their defense capabilities, while adapting to the required levels posed by newly germinating patterns. In order to confine within scope of this paper, the application of this approach is being discussed in the context of selected scenarios ofcybercrime.
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