防止网络安全勒索的疲劳数据:一种反情报方法

A. Vincent
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引用次数: 0

摘要

“现在和最近,confble不再是关于预防和阻止攻击、威胁或暴露,而是关于你能多快地发现攻击正在发生。”对安全信息和事件管理(SIEM)技术和服务的需求不断增长,这些技术和服务收集和分析用于管理威胁的安全事件大数据。大数据提供了分析大量潜在安全事件的能力,并在它们之间建立联系,以创建威胁的优先级列表。有了大数据,不同的数据可以连接起来,这使得网络安全专业人员能够采取主动的方法来防止攻击。高级持续性威胁(apt)还用于查找和识别威胁的来源。集成的安全体系结构和强大的自动信息收集与共享功能在多个安全系统之间实现,称为“反情报”解决战略短板。“反智能”将新的安全产品架构转换为数据收集主干,为用于关联来自多个系统的安全异常的集中存储库提供数据。本文阐述了新的反情报方法,通过应用现代风险分析和缓解方法来保护用户的私人数据免受大数据的影响,以防御未来的网络安全威胁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fatiguing Data to Protect against Cyber Security Extortions: A counter-intelligence methodology
"Now and recently, confab is less about preventing and stopping an attack, threat or exposure, and more about how swiftly you can detect that an attack is happening." There's a growing demand for security information and event management (SIEM) technologies and services, which gather and analyse security event big data that is used to manage threats. Big data offers the ability to analyse immense numbers of potential security events and make connections between them to create a prioritized list of threats. With big data, distinct data can be connected, which allows cyber security professionals to take a proactive approach that prevents attacks. Advanced Persistent Threats (APTs) are also used to find and identify where threats are coming from. Integrated security architecture and power of automated information collection and sharing between many security systems, called “Counter-intelligence” to solve the strategic short comings. “Counter intelligence” translates to new security product architecture into a data collection backbone feeding a centralized repository used to correlate security anomalies from, across multiple systems. This paper illustrates the new counter intelligence approach to defend against future cyber security threats by applying modern risk analysis and mitigation methods to protect users’ private data from big data.
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