Improving Login Security with a Rule Engine

Published on: 2024-08-10 18:29:56

A rule engine gives teams a clear way to protect login systems against account takeover and bot attacks.

It lets users define custom rules and conditions from historical data, such as session information, IP addresses, and browser types. With that decision logic in place, a rule engine can identify and block suspicious login attempts, and escalate login requirements for flagged sessions.

There are clear benefits to using a rule engine for login security. Below are examples of logical rules that teams can implement.

What are the Benefits of Using a Rule Engine for Login Security

A rule engine enables fast detection and response to security threats by triggering actions automatically when specific events occur.

For example, a rule can monitor login attempts from IP addresses with multiple failed logins in a short time. That pattern can indicate a possible bot attack.

With this rule in place, the system can block the login attempt without manual review.

How escalating requirements can improve account security

Another benefit of using a rule engine is that it supports escalating login requirements based on rule outcomes.

It can also use results from Private State Tokens (PSTs) to improve security.

By using PSTs, a rule engine can identify suspicious login attempts more accurately and trigger added checks, such as an OTP code or other challenges.

This helps prevent attackers from accessing accounts, even if they have obtained part of the user's login information.

Preventing Account Takeover and Bot Attacks with Logical Rules

Examples of logical rules that can be implemented with a rule engine to prevent account takeover and bot attacks include:

  • Requiring additional authentication for login attempts from unfamiliar or suspicious IP addresses or devices.
  • Blocking or escalating login attempts from IP addresses linked to previous cyberattacks or multiple failed login attempts.
  • Requiring additional authentication for login attempts during unusual hours or from unfamiliar locations or devices.
  • Blocking login attempts that use simple or common password phrases.
  • Verifying the presence of a Private State Token from a trusted issuer to confirm that the user is authorized to access the account.

Tailoring Login Security to Regional Risks with a Rule Engine

One of the main advantages of using a rule engine for login security is its ability to target specific IP addresses or combinations of attributes that may be the source of an attack. For example, if an IP address is identified as being linked to a bot attack, the rule engine can automatically block login attempts from that IP address and require additional authentication for future login attempts from the same source. This helps prevent attacks and protect user accounts.

Another advantage of using a rule engine is that teams can adjust logical rules to fit the needs and risks of a specific region. For example, a rule engine could apply stricter authentication requirements for login attempts from countries with a high rate of cyberattacks, or require added authentication for login attempts from IP addresses associated with known malicious actors. This helps align the login system with regional risks.

Conclusion

Overall, the main advantage of using a rule engine for login security is its ability to add and adjust logical rules quickly to protect against account takeover and bot attacks. With a rule engine, users can define custom rules and conditions based on historical data, and target specific IP addresses or combinations of attributes that may be the source of an attack. This helps improve login security and protect user accounts.

 

Related Articles

  • How to implement an automated decision strategy that keeps working under failure

    Automated decision strategy is not just about getting the happy path right. It also needs to keep making safe, explainable decisions when data is missing, providers time out, or a complex decision flow fails. This article shows how to design decision logic with failover and missing data strategies built in from the start.

  • Building Anti-Fraud Competency Without Losing Control of Your Data

    Anti-fraud is not a product you install once and forget. It is an operating capability built from data, decision logic, traceability, and continuous tuning across the customer journey. Companies can buy parts of that stack, but if they outsource the core logic and enriched data, they often create a lock-in problem that is hard to unwind later.

  • Advanced decision management systems vs BRMS

    Traditional BRMS can execute rules. Modern decision management systems handle more. This article explains why cloud-based decision management is a better fit for fintech, insurance, and e-commerce, with faster decisioning, more flexibility, and stronger support for compliance and real-time data.

  • 5 Important Components of Rule Engine Architecture

    This article explains the five components of rule engine architecture that shape decision logic and operational control. It covers rule repositories, rule management and deployment interfaces, execution, and historical versioning for traceability and compliance.

  • Subdomain Verification for Email Authentication - Decisimo

    Email validation often misses the difference between corporate addresses and junk, which leads to false positives and false negatives. This article shows how to detect corporate domains by checking for subdomains with the SecurityTrails Subdomains API. It also outlines rules to implement that check in a fraud rule engine.