False Positive: Understanding the Concept and Its Impact

Quick Summary: False positives are capable of causing significant damage to web applications that make organizations suffer for a long time. However, what is false positive and how it takes place is something that needs to be understood before working on it. This blog covers AtoZ insights about false positives, from its detailed definition and calculation methods to its mitigation practices that help businesses rescue their web apps from them. Read this blog to thoroughly learn about false positives.

Have you ever been terrified about any intimidating incident that turns out to be null when noticed? Yes, that's precisely what we call a false alarm.

Well, such scenarios do occur in the security domain, too. With a slight difference in terms, we address such events as false positives in the cyber security domain.

Just like false alarms are a big NO-NO, false positives in cyber security are equally unacceptable because of their unwelcome impact on businesses.

According to HELP NET SECURITY's research, 33% of companies experienced delays in responding to cyberattacks due to addressing false positives, and 63% spend over 208 hours annually managing these false positives.

So, why not find a permanent solution to eliminate the issues of false positives?

In this blog, we have covered all the imperative details related to the concept of false positives to help you get rid of it. Read this blog till the end to unwrap essential information that will enable you to prevent false positives in the best possible way and allow you to assess actual potential vulnerabilities.

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What is a False Positive?

In the cyber security domain, a false positive takes place when a security system of web apps mistakenly considers a benign or sanctioned activity as a threat or malicious activity. For example, an antivirus program might flag an authorized application as a virus, or a network monitoring tool might indicate regular traffic as suspicious. Such false alarms can lead to unwanted alerts, overloading security teams with unfounded panic and potentially causing disruptions, time consumption, or inefficiencies in operations. Working on false positive reduction is critical for maintaining adequate security operations and ensuring actual vulnerabilities and threats are addressed on an immediate basis.

How to Calculate a False Positive?

Let’s learn the process of calculating false positives step-by-step:

1. Understand the Terminology

• True Positive: Correctly predicted positive cases.
• False Positive: Incorrectly anticipated positive cases (the ones you are trying to calculate).
• True Negative: Correctly predicted negative cases.
• False Negative: Incorrectly anticipated negative cases.

2. Obtain the Confusion Matrix

The confusion matrix is a table that summarizes the performance of a classification model. It includes TP, FP, TN, and FN.

Example of a confusion matrix:

3. Calculate False Positives

From the confusion matrix the number of false positives is directly provided. It’s the count of cases where the model predicted positive, but the actual outcome was negative.

Formula:

False Positives (FP)=Number of instances where the model predicted Positive, and the actual was Negative

4. Calculate False Positive Rate

If you also want to calculate the False Positive Rate (FPR), which measures the proportion of actual negatives that were incorrectly classified as positive, you can use this formula:

This allows you to know how often the model incorrectly labels negative cases as positive.

Example of Calculation:

Suppose you have the following confusion matrix:

• True Positives (TP): 50
• False Positives (FP): 10
• True Negatives (TN): 100
• False Negatives (FN): 5

To calculate false positives:

False Positives (FP) = 10

To calculate false positive rate:

So, the false positive rate is more or less 9.1%.

These calculations make it easier for you to understand the efficiency of your model’s performance and where it might make mistakes, especially in distinguishing between classes.

6 Common Types of False Positives in Cyber Security

Here, we are mentioning the most prominent types of false positives that often lead to wasted resources, alert fatigue, and affect efficiency in security operations.

1. Intrusion Detection Systems (IDS)

Suspicious Network Activity: Intrusion detection systems (IDS) consistently monitor network traffic to assess abnormal activities. Sometimes, it mistakenly labels legitimate activities suspicious, such as software updates or routine system scans. For example, a network scan by an IT administrator could be mistaken for a hacker's probe.

2. Antivirus Software

Incorrect Threat Detection: Antivirus programs scan files and processes for malware detection. Sometimes, these tools may flag bona fide files or programs as potential threats. For example, a new software application might be falsely flagged as a virus, possibly because of its resemblance to some common malware.

3. Web Application Firewalls (WAF)

Blocked Legitimate Traffic: Firewalls monitor incoming and outgoing traffic on the basis of standard rules. If these rules are too restrictive, broad, or not properly configured, the firewall might block legitimate connections if it mistakes connections for anomalies. For instance, a firewall might block a safe, essential update from a trusted software provider.

4. Security Information and Event Management (SIEM)

Misinterpreted Logs: SIEM systems collect and examine data from multiple sources for security testing and vulnerability assessment. Sometimes, these tools are mistakenly considered malicious or suspicious when observing something out of context. For instance, a large data transfer could be flagged as a potential data breach, even though it's just a routine backup.

5. Email Security

Misidentified Phishing: Email security systems consistently scan for malicious attachments or phishing attempts. In this process, sometimes, legitimate emails are also flagged as malicious ones. Here's an example: an organization's internal newsletter is being marked as a suspicious email, even though it's safe and expected.

6. Vulnerability Scanners

False Vulnerabilities: Vulnerability scanners detect security weaknesses in systems and applications. They might report issues that aren't threats in actuality, like a misconfigured scanner reporting a security flaw that has already been worked upon or doesn't apply to the organization's environment.

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What is the Impact of False Positive in Web Apps?

Let's track down the impact of false positive on web app security in order to enforce equally robust mitigation practices.

1. Wasted Resources

Security teams get occupied in investigating threats that turn out to be false positives on addressing. This diverts resources from investing time and effort in addressing the actual security threats.

Resources such as staff time, computing power, and network bandwidth are consumed during the assessment of false positives, which leads to increased operational costs.

2. Alert Fatigue

If false positives start taking place often, it can lead to alert fatigue, where security teams become overwhelmed by the sheer volume of alerts. This leads to decreased attention to each alert and an increased risk of missing genuine threats.

Also, security professionals become less likely to pay the required attention to a high number of false positives because of their increased frequency. This leads to the avoidance of actual vulnerability detection, which can be hazardous to overall web app security.

3. Increased Cost

As security teams investigate vulnerabilities before coming across their actual state, this incurs additional costs, including labor, training, and the use of additional tools or services.

False positives affect the entire budget as organizations generally intend to spend on the basis of predetermined costs.

6 Best Practices to Prevent False Positives in Web Applications

Let's learn in detail about the best practices for preventing false positives in web apps and enhancing the accuracy of security measures while reducing the risk of missing genuine threats.

1. Customize Rules

Implement the rules and policies of web application firewalls (WAFs) and intrusion detection systems (IDS) to fit your specific application and traffic patterns. Avoid the usage of overly broad or default rules that may not go well with your environment.

Moreover, the practice of updating rules and signatures uniformly on the basis of prevalent threat intelligence and application changes should be regularly maintained to remain relevant and practical.

2. Implement Contextual Analysis

Optimize behavioral analysis methodology to comprehend normal user behavior and detect deviations that might signal a real potential security threat rather than a false positive.

Incorporate context about the application's functionality and user roles to easily find the difference between legitimate and abnormal activities.

3. Optimize Machine Learning and AI

Enforce machine learning and AI-based solutions in cyber Security that can adapt to evolving patterns and learn from false positives to enhance accuracy over a period of time.

Incorporate machine learning models to capture anomalies in web traffic and user behavior to decrease the chances of flagging sanctioned activities as threats.

4. Implement Whitelisting

Maintain whitelists for known secured IP addresses, users, and applications to prevent them from triggering needless notifications.

Generate whitelists for approved and authenticated web apps to prevent false positives related to bona fide software or services.

5. Optimize Log Analysis

Use log filtering methodologies to separate noise from valuable security data. This approach enables organizations to focus on actual security events and decrease the volume of false positives.

Log correlation rules should also be implemented that consider different data sources and patterns to enhance the validity of assessing real web app security threats.

6. Implement Advanced Threat Detection

Integrate threat intelligence feed to not just improve the approach of detecting vulnerabilities and threats, but to ensure overall web app security. This also helps reduce false positives by correlating them with known threat indicators.

Utilize a risk-based analysis approach to prioritize vulnerabilities based on their potential impact and likelihood of threat. This allows teams to detect and work on potential threats on high priority.

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Scan Verified Vulnerabilities with ZeroThreat to Prevent False Positives

False positives are a sheer waste of time, and businesses cannot afford to increase their frequency consistently. Hence, in this blog, we have covered how to calculate false positives along with mitigation practices to avoid them altogether. We are pretty sure that with the calculation method and enforcing recommended practices, you will be able to get rid of them gradually.

Moreover, optimizing a tool that scans all kinds of vulnerabilities by ensuring their authenticity and potential impact relatively reduces the chances of false positives in web apps.

ZeroThreat is one such tool that successfully performs advanced vulnerability scans and web app security testing. It not only detects all types of bona fide vulnerabilities, but it also suggests mitigation practices that help organizations quickly fix them. It's scalable to use for executing advanced vulnerability scans and assessments without configuration setup and also enables you to save ample time by reducing pen testing by 90%. Sign in to ZeroThreat to modernize your Security with automation and excellence.