AI Email Account Takeover Detection via Pattern Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing email security solutions are ineffective in detecting email account takeover attacks as they cannot monitor or stop internal emails, and traditional gateway-based systems only filter external communications, failing to identify malicious signals from compromised accounts.
Innovation Solution
An AI engine/classifier continuously monitors communication patterns via API calls to detect and remediate email account takeover attacks by analyzing sender and recipient identities, forwarding rules, IP logins, and link information, enabling real-time detection and remediation of compromised accounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional gateway-based email security solutions are used, then external email communications are filtered, but internal emails from compromised accounts cannot be monitored or stopped
Solution Approach 1:
The AI engine is designed to perform multiple functions: it monitors both external and internal email communications, detects account takeover attempts, analyzes communication patterns, and remediates compromised accounts. This multi-functional approach allows the system to address the limitation of traditional gateway-based solutions that only handle external communications.
Solution Approach 2:
The system transitions from a single-layer gateway filtering approach to a multi-dimensional monitoring system that operates at multiple levels: external email filtering, internal email monitoring, communication pattern analysis, and account behavior tracking. This dimensional expansion enables comprehensive detection of account takeover attacks regardless of email origin.
2Ease of operation
If emails from compromised accounts are analyzed using traditional methods, then legitimate sender headers are assumed safe, but malicious signals are missed
Solution Approach 1:
Instead of assuming emails from known senders are legitimate (traditional approach), the system inverts the assumption by treating all emails as potentially suspicious and analyzing them for anomalous patterns. The AI engine looks for deviations from normal communication behaviors rather than relying on sender reputation, enabling detection of compromised accounts that appear legitimate.
Solution Approach 2:
The system continuously monitors communication patterns and uses feedback from analyzed emails to refine detection accuracy. By tracking sender-recipient relationships, response patterns, and communication frequency over time, the AI engine learns normal behaviors and can identify subtle deviations that indicate account compromise, improving detection precision without false positives.
3Reliability
If real-time detection of email account takeover is implemented, then attacks can be prevented, but system complexity increases
Solution Approach 1:
The AI engine performs self-learning and self-adjustment by automatically analyzing communication patterns and adapting to new attack methods without requiring manual configuration or intervention. The system autonomously identifies anomalous behaviors, makes detection decisions, and triggers remediation actions, reducing operational complexity while maintaining high detection reliability.
Solution Approach 2:
The AI engine acts as an intermediary layer between email systems and security responses. It receives email data, analyzes patterns, and mediates between detection and remediation actions by automatically triggering alerts, blocking suspicious communications, or notifying users. This intermediary role simplifies the overall system architecture by centralizing complex analysis functions.
Data Source
AI summary
A new approach is proposed that contemplates systems and methods to support email account takeover detection and remediation by utilizing an artificial intelligence (AI) engine/classifier that detects and remediates such attacks in real time. The AI engine is configured to continuously monitor and identify communication patterns of a user on an electronic messaging system of an entity via application programming interface (API) calls. The AI engine is then configured to collect and utilize a variety of features and/or signals from an email sent from an internal email account of the entity. The AI engine combines these signals to automatically detect whether the email account has been compromised by an external attacker and alert the individual user of the account and/or a system administrator accordingly in real time. The AI engine further enables the parties to remediate the effects of the compromised email account by performing one or more remediating actions.