实例探究 > Debt Collector Impersonation / Invoice Fraud Attack

Debt Collector Impersonation / Invoice Fraud Attack

公司规模
1,000+
地区
  • America
国家
  • United States
产品
  • Abnormal Behavior Technology (ABX)
  • Abnormal Identity Model
  • Abnormal Relationship Graph
  • Abnormal Content Analysis
技术栈
  • Natural Language Processing
  • API-based Office 365 Integration
  • G Suite Integration
  • Vendor Mail Detector
实施规模
  • Enterprise-wide Deployment
影响指标
  • Cost Savings
  • Customer Satisfaction
  • Digital Expertise
技术
  • 网络安全和隐私 - 身份认证管理
  • 网络安全和隐私 - 网络安全
  • 网络安全和隐私 - 安全合规
适用行业
  • Professional Service
  • 零售
适用功能
  • 商业运营
  • 销售与市场营销
用例
  • 欺诈识别
服务
  • 网络安全服务
  • 系统集成
  • 培训
关于客户
The customer is a global retailer that was targeted by a sophisticated invoice fraud attack. The retailer operates on a large scale, dealing with numerous vendors and financial transactions daily. This makes them a prime target for Business Email Compromise (BEC) attacks. The retailer's employees are responsible for processing invoices and ensuring timely payments to avoid penalties and maintain good vendor relationships. The company has a significant digital presence and relies on email communication for its operations.
挑战
The retailer was targeted by an attacker impersonating a debt collection agency using a lookalike domain. The attacker also spoofed the retailer's COO to add credibility to the fraudulent invoice request. The attack involved sophisticated social engineering techniques and spanned six back-and-forth conversations over one day. The urgency created by the debt collection pretext led employees to overlook red flags and begin processing the payment.
解决方案
Abnormal Security detected and stopped the attack using its Abnormal Behavior Technology (ABX). ABX combines the Abnormal Identity Model, Abnormal Relationship Graph, and Abnormal Content Analysis to detect and prevent such attacks. Specific techniques used included identity modeling with VendorBase, domain impersonation detection, relationship graph analysis, and content analysis using natural language processing. The solution was deployed in passive mode, allowing for a comprehensive view of the attack lifecycle without impacting email flow. Abnormal Security's platform integrates seamlessly with Office 365 and G Suite, requiring no configuration and minimal setup time.
运营影响
  • Abnormal Security's detection capabilities prevented the fraudulent payment from being processed, safeguarding nearly $30,000.
  • The platform's ability to detect domain impersonation and spoofed emails provided a robust defense against sophisticated social engineering attacks.
  • The seamless integration with existing email systems ensured that the retailer's operations were not disrupted during the detection and prevention process.
  • The use of advanced techniques like natural language processing and relationship graph analysis enabled high-confidence detection of fraudulent activities.
  • The retailer's employees were alerted to the attack, increasing their awareness and understanding of potential email threats.
数量效益
  • Prevented a financial loss of nearly $30,000.

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