Credential Spray Attack Detection via Bucketed Failure Analysis
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Solution Overview
Problem
Detecting password spray attacks in cloud accounts is challenging due to the difficulty in distinguishing actual attacks from non-malicious behaviors that resemble them, leading to issues with false positives and the need for effective intrusion detection systems.
Innovation Solution
An intrusion detection system (IDS) is implemented with a processor and memory, utilizing a behavior analyzer to analyze access attempt behavior by inserting user account identifiers into digital buckets based on access failure events and computing an extent to generate alerts when the number of affected accounts meets or exceeds an alert threshold, thereby detecting credential spray attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional intrusion detection methods are used to detect password spray attacks, then attack detection capability is improved, but false positive rate increases due to inability to distinguish attacks from non-malicious behaviors
Solution Approach 1:
The patent segments the detection process into multiple independent analysis components: (1) collecting authentication events and dividing them into candidate spray events and non-candidate events, (2) analyzing temporal patterns of failed authentication attempts, (3) examining geographic distribution of attack sources, (4) evaluating user behavior baselines. Each segment processes specific aspects of authentication data independently, then combines results to reduce false positives while maintaining detection accuracy.
2Reliability
If comprehensive access failure analysis is performed on all user accounts, then detection thoroughness is improved, but system complexity and processing time increase
Solution Approach 1:
The patent extracts only the critical and relevant features from the comprehensive authentication event data, rather than analyzing all raw data. It extracts: (1) temporal patterns of failed attempts, (2) geographic distribution information, (3) user-specific baseline behaviors, (4) candidate spray event characteristics. This extraction approach maintains detection thoroughness while significantly reducing system complexity by focusing only on the most discriminative features.
3Speed
If real-time analysis of all authentication events is implemented, then attack detection speed is improved, but computational resources and processing time are consumed
Solution Approach 1:
The patent applies partial action by analyzing only the subset of authentication events that exhibit characteristics of potential spray attacks, rather than processing all authentication events in real-time. It identifies candidate spray events based on initial criteria (multiple failed attempts, temporal concentration) and applies sophisticated analysis only to these candidates. This approach achieves fast detection of actual attacks while conserving computational resources by avoiding exhaustive analysis of normal authentication traffic.
Data Source
AI summary
Cybersecurity is enhanced to detect credential spray attacks. Accounts with access failure events are divided into buckets B1 . . . BN based on access failure count ranges R1 . . . RN. For instance, accounts with one logon failure may go in B1, accounts with two failures in B2, etc. Buckets will thus have account involvement extents E1 . . . EN, which are compared to thresholds T1 . . . TN. An intrusion detection tool generates an alert when some Ei hits its Ti. Detection may spot any credential sprays, not merely password sprays. False positives may be reduced by excluding items from consideration, such as logon attempts using old passwords. False positives and false negatives may be balanced by tuning threshold parameters. Breached accounts may be found. Detection may also permit other responses, such as attack disruption, harm mitigation, and attacker identification. Credential spray attack detection may be combined with other security mechanisms for defense in depth of cloud and other network accounts.