Rule based detection software features criteria to

In the Matching Record Type box, choose the type of record to compare. It's also useful to be able to compare different record types. For example, you might want to compare the Email field in Contacts to the Email field in Leads. If you want the rule to consider only active records while detecting duplicates, select the Exclude inactive matching records check box.

You should also select this check box if your duplicate detection rule criteria are based on a status field. If you selected different record types for the base and matching record types, for each new criterion, in the Base Record Field column, choose Select , and then choose a field name.

In the same row, in the Matching Record Field column, choose Select , and then choose a field name. If you selected the same record types for the base and matching record types, for each new criterion, in the Field column, choose Select , and then choose a field. In the same row, in the Criteria column, choose Select , and then choose an operator. For example, select Exact Match. If you don't want the rule to consider blank fields null values as equal while identifying duplicates, select the Ignore Blank Values check box.

If the duplicate detection rule contains only one condition, blank values are ignored during duplicate detection job. The number of criteria that you can select is limited by the number of characters that can be stored in the matchcode for the record. As you add criteria, watch the Current matchcode length value shown at the bottom of the criteria list. For example, you might want to evaluate a model only if a user is remote and is attempting for the third time to create a new system account.

Compared to SIEM rules, models typically have a much simpler rule expression that triggers an alert—that is, if a behavior is observed more than a specific number of times and the confidence factor is above a predetermined value. Instead, each model the system applies adds points to that given session. If session points exceed a predetermined value, the system triggers an alert. Figure 1: A model showing a user accessing a file for the first time from the New York network zone.

You might think using models is the best way to handle all threat detection. But there are situations where correlation rules are the best and most straightforward option. Here are some examples best handled by correlation rules:.

We have much more detail in our white papers, training, and Ask-an-Expert webinar recordings. Here are some to provide you with a deeper dive into SIEM rules and models:. Rules Vs. Models Whitepaper. Models: Exabeam Ask-an Expert webinar archives. Published August 03, Author Pramod Borkar.

Modern SIEMs provide out of the box correlation rules and sophisticated models to surface a broad range of abnormal behavior and events. Ask the Experts Blog Series In the past, security information and event management SIEM technology used to rely primarily on signatures to detect undesirable behavior. Correlation rules A correlation rule, a. Simple SIEM rules detect an event type and trigger a response. For example, if a ZIP file is attached to an email, they trigger an alert.

Composite rules nest or join two or more rules to achieve a more complex behavior. For example, if seven authentication attempts to the same computer fail from the same IP address within ten minutes and use different user names, and if a successful login occurs on any computer within the network and originates from that same IP address, they trigger an alert.

Correlation rule examples Here are some examples of real-world correlation rules: If a user fails more than three login attempts on the same computer within an hour, trigger an alert. If a large number of failed login attempts is followed by one that is successful, trigger an alert. During a company-wide layoff, trigger an alert if more than ten files of specific types are copied to USB drives or sent as email attachments to non-company domains.

Models A model profiles a user or asset behavior, triggering an alert when the behavior deviates from normal behavior. Model examples Some typical model examples follow. Trigger an alert if: A user switches from their normal account to a privileged one, then performs an abnormal data transfer to or from an external service.

A user VPNs to the network from a new location for the first time, then accesses a shared file system. ML-based systems are capable of learning from streaming data and adapting to emerging fraud patterns, while rule-based systems require analysts to specify new fraud scenarios. False positives reduction.

You want to treat yourself to sightseeing, dining, and shopping. But you tried to pay for a purchase, the transaction was declined, and your card was blocked. On the other hand, the system is too straightforward in its transaction analysis. The problem of false positives — declined legitimate transactions — is relevant even for software using machine learning.

The key to accuracy in fraud detection is to assess every transaction in the broad context, going beyond location and transaction amount. For example, data scientists from MIT found the approach to reduce false positive forecasts with automated feature engineering.

This method entails extracting more than detailed features — behavior patterns — for each transaction. Efficient fraud protection solutions analyze hundreds of indicators like historical data on user buying habits and current transaction details, use device fingerprinting to provide as accurate predictions on order outcomes as possible.

Real-time operations tracking and reporting. Fraud detection software includes dashboards, so customers can monitor their key performance indicators in real time, for instance, track orders and learn about their status approved or declined and additional information like payment method, location, channel, etc.

Reporting capabilities usually include daily, weekly, or monthly reports on suspicious activity or a total number of transactions.

Investigation teams may use visualizations of fraud patterns to better understand interconnections between user behavior and fraud attempts. You never know what approach to stealing fraudsters may use in a particular case.

The cybersecurity system should be comprehensive to cover all information systems within the organization without a single exception, should be universal to be able to handle all types of data and highly-performing to process massive data flows.

The system should be able to automatically learn from data to detect not only well-known but also new types of fraud and cyber threats, adds the specialist. Fraud analyst Avivah Litan from Gartner Group has suggested a five-layer approach to fraud detection and prevention. Each of the levels represents a specific type of customer activity and behavior:. Five-level approach to fraud protection described by Avivah Litan from Gartner Group.

Picture source: SAS. Gartner defines systems that support all activity layers as enterprise fraud management EFM software. So, one of the options to evaluate a product is to learn about the layers of fraud protection it considers for the analysis. You should also learn about average deployment time and ease of deployment.

Some websites have discussion sections e. Gartner Peer Insights where users share their feedback on the software and some are review sites themselves e. Capterra , G2crowd , and FinancesOnline. Make sure to check the reviews to learn more about deployment pitfalls and common issues. Another factor to consider is integration.

For instance, if you run an online store, ensure that a solution is compatible with your eCommerce platform. For instance, those who accept card payments have to ensure that solutions meet the PCI Standard. Have a look at our article with advice on how to comply with GDPR if you work in the travel industry.

Fraud detection software providers suggest various pricing models. Some vendors have a number of fixed subscription plans; others allow for flexible pricing that depends on business size and industry, annual sales volume, etc.

Providers may also charge per transaction only. Generally, companies share pricing information on request. Shoshanah Posner from NoFraud notes that the price for ML-based software depends on the level of support one expects from the software. Infographics, surveys, articles on the fraud detection field, video tutorials, and a frequently-asked-question section may also help customers use a software to its full potential and keep current with industry trends.

It would be useful to understand how a solution validates transactions. Also, find out how the vendor team and software handles cases of false declines from clients that surely are legitimate. How does the service guarantee an optimal approval rate? What does that look like? What if the solution declines an order I think is valid? How do I challenge a decline? Can you provide some case studies of real results?

People shop more and more using mobile devices. For example, million customers bought retail goods via mobile devices in This figure is projected to exceed 1 billion this year.

And mCommerce fraud is another pain point for businesses. According to the True Cost of Fraud Study by LexisNexis Risk Solutions, mCommerce merchants selling digital goods are under greater fire than retailers selling physical goods only or those without mobile sales support.

So, our advice is to make sure solutions also track activity from the mobile channel using multi-layer user authentication with device identification, for example. While some of them are designed solely for eCommerce, others work across industries. NoFraud is an eCommerce fraud prevention system that combines machine learning and human intelligence. The tool screens transactions in real time using advanced machine learning algorithms, allowing merchants to concentrate on their primary tasks and goals — fulfilling orders, interacting with customers, and expanding their business in general.

Only high-risk and questionable transactions are selected for a manual review by the NoFraud team. In these cases, specialists reach out to a cardholder to confirm the legitimacy of a transaction. NoFraud uses thousands of data points in its decision-making process. The system takes into account historical customer data, current transaction data, and also analyzes customer behavior.

NoFraud also checks transaction velocity — the number of payments made with a credit card, from a specific account, device and IP address during a certain timespan. NoFraud provides chargeback protection when accepting fraudulent transactions.