Mitigating OWASP Web Application Risk: Insecure Design using F5 XC platform
Overview: This article is the last part in a series of articles on mitigation of OWASP Web Application vulnerabilities using F5 Distributed Cloud platform (F5 XC). Introduction to Insecure Design: In an effort to speed up the development cycle, some phases might be reduced in scope which leads to give chance for many vulnerabilities. To focus the risks which are been ignored from design to deployment phases, a new category of “Insecure Design” is added under OWASP Web Application Top 10 2021 list. Insecure Design represents the weaknesses i.e. lack of security controls which are been integrated to the website/application throughout the development cycle. If we do not have any security controls to defend the specific attacks, Insecure Design cannot be fixed by any perfect implementation while at the same time a secure design can still have an implementation flaw which leads to vulnerabilities that may be exploited. Hence the attackers will get vast scope to leverage the vulnerabilities created by the insecure design principles. Here are the multiple scenarios which comes under insecure design vulnerabilities. Credential Leak Authentication Bypass Injection vulnerabilities Scalper bots etc. In this article we will see how F5 XC platform helps to mitigate the scalper bot scenario. What is Scalper Bot: In the e-commerce industry, Scalping is a process which always leads to denial of inventory. Especially, online scalping uses bots nothing but the automated scripts which will check the product availability periodically (in seconds), add the items to the cart and checkout the products in bulk. Hence the genuine users will not get a fair chance to grab the deals or discounts given by the website or company. Alternatively, attackers use these scalper bots to abandon the items added to the cart later, causing losses to the business as well. Demonstration: In this demonstration, we are using an open-source application “Online Boutique” (refer boutique-repo) which will provide end to end online shopping cart facility. Legitimate customer can add any product of their choice to the cart and checkout the order. Customer Page: Scalper bot with automation script: The below automation script will add products in bulk into the cart of the e-commerce application and place the order successfully. import requests import random # List of User-Agents USER_AGENTS = [ "sqlmap/1.5.2", # Automated SQL injection tool "Nikto/2.1.6", # Nikto vulnerability scanner "nmap", # Network mapper used in reconnaissance "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)", # Spoofed Search Engine Bot "php", # PHP Command Line Tool "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)", # Old Internet Explorer (suspicious outdated) "libwww-perl/6.36", # Perl-based automation, often found in attacks or scrapers "wget/1.20.3", # Automation tool for downloading files or making requests "Python-requests/2.26.0", # Python automation library ] # Function to select a random User-Agent def get_random_user_agent(): return random.choice(USER_AGENTS) # Base URL of the API BASE_URL = "https://insecure-design.f5-hyd-xcdemo.com" # Perform the API request to add products to the cart def add_to_cart(product_id, quantity): url = f"{BASE_URL}/cart" headers = { "User-Agent": get_random_user_agent(), # Random User-Agent "Content-Type": "application/x-www-form-urlencoded" } payload = { "product_id": product_id, "quantity": quantity } # Send POST request with cookies included response = requests.post(url, headers=headers, data=payload) if response.status_code == 200: print(f"Successfully added {quantity} to cart!") else: print(f"Failed to add to cart. Status Code: {response.status_code}, Response: {response.text}") return response # Perform the API request to place an order def place_order(): url = f"{BASE_URL}/cart/checkout" headers = { "User-Agent": get_random_user_agent(), # Random User-Agent "Content-Type": "application/x-www-form-urlencoded" } payload = { "email": "someone@example.com", "street_address": "1600 Amphitheatre Parkway", "zip_code": "94043", "city": "Mountain View", "state": "CA", "country": "United States", "credit_card_number": "4432801561520454", "credit_card_expiration_month": "1", "credit_card_expiration_year": "2026", "credit_card_cvv": "672" } # Send POST request with cookies included response = requests.post(url, headers=headers, data=payload) if response.status_code == 200: print("Order placed successfully!") else: print(f"Failed to place order. Status Code: {response.status_code}, Response: {response.text}") return response # Main function to execute the API requests def main(): # Add product to cart product_id = "OLJCESPC7Z" quantity = 10 print("Adding product to cart...") add_to_cart_response = add_to_cart(product_id, quantity) # If the add_to_cart request is successful, proceed to checkout if add_to_cart_response.status_code == 200: print("Placing order...") place_order() # Run the main function if __name__ == "__main__": main() To mitigate this problem, F5 XC is providing the feasibility of identifying and blocking these bots based on the configuration provided under HTTP load balancer. Here is the procedure to configure the bot defense with mitigation action ‘block’ in the load balancer and associate the backend application nothing but ‘evershop’ as the origin pool. Create origin pool Refer pool-creation for more info Create http load balancer (LB) and associate the above origin pool to it. Refer LB-creation for more info Configure bot defense on the load balancer and add the policy with mitigation action as ‘block’. Click on “Save and Exit” to save the Load Balancer configuration. Run the automation script by providing the LB domain details to exploit the items in the application. Validating the product availability for the genuine user manually. Monitor the logs through F5 XC, Navigate to WAAP --> Apps & APIs --> Security Dashboard, select your LB and click on ‘Security Event’ tab. The above screenshot gives the detailed info on the blocked attack along with the mitigation action. Conclusion: As you have seen from the demonstration, F5 Distributed Cloud WAAP (Web Application and API Protection) has detected the scalpers with the bot defense configuration applied on the Load balancer and mitigated the exploits of scalper bots. It also provides the mitigation action of “_allow_”, “_redirect_” along with “_block_”. Please refer link for more info. Reference links: OWASP Top 10 - 2021 Overview of OWASP Web Application Top 10 2021 F5 Distributed Cloud Services F5 Distributed Cloud Platform Authentication Bypass Injection vulnerabilitiesDelivering Secure Application Services Anywhere with Nutanix Flow and F5 Distributed Cloud
Introduction F5 Application Delivery and Security Platform (ADSP) is the premier solution for converging high-performance delivery and security for every app and API across any environment. It provides a unified platform offering granular visibility, streamlined operations, and AI-driven insights — deployable anywhere and in any form factor. The F5 ADSP Partner Ecosystem brings together a broad range of partners to deliver customer value across the entire lifecycle. This includes cohesive solutions, cloud synergies, and access to expert services that help customers maximize outcomes while simplifying operations. In this article, we’ll explore the upcoming integration between Nutanix Flow and F5 Distributed Cloud, showcasing how F5 and Nutanix collaborate to deliver secure, resilient application services across hybrid and multi-cloud environments. Integration Overview At the heart of this integration is the capability to deploy a F5 Distributed Cloud Customer Edge (CE) inside a Nutanix Flow VPC, establish BGP peering with the Nutanix Flow BGP Gateway, and inject CE-advertised BGP routes into the VPC routing table. This architecture provides us complete control over application delivery and security within the VPC. We can selectively advertise HTTP load balancers (LBs) or VIPs to designated VPCs, ensuring secure and efficient connectivity. Additionally, the integration securely simplifies network segmentation across hybrid and multi-cloud environments. By leveraging F5 Distributed Cloud to segment and extend the network to remote locations, combined with Nutanix Flow Security for microsegmentation within VPCs, we deliver comprehensive end-to-end network security. This approach enforces a consistent security posture while simplifying segmentation across environments. In this article, we’ll focus on application delivery and security, and explore segmentation in the next article. Demo Walkthrough Let’s walk through a demo to see how this integration works. The goal of this demo is to enable secure application delivery for nutanix5.f5-demo.com within the Nutanix Flow Virtual Private Cloud (VPC) named dev3. Our demo environment, dev3, is a Nutanix Flow VPC with a F5 Distributed Cloud Customer Edge (CE) named jy-nutanix-overlay-dev3 deployed inside: *Note: CE is named jy-nutanix-overlay-dev3 in the F5 Distributed Cloud Console and xc-ce-dev3 in the Nutanix Prism Central. eBGP peering is ESTABLISHED between the CE and the Nutanix Flow BGP Gateway: On the F5 Distributed Cloud Console, we created an HTTP Load Balancer named jy-nutanix-internal-5 serving the FQDN nutanix5.f5-demo.com. This load balancer distributes workloads across hybrid multicloud environments and is protected by a WAF policy named nutanix-demo: We advertised this HTTP Load Balancer with a Virtual IP (VIP) 10.10.111.175 to the CE jy-nutanix-overlay-dev3 deployed inside Nutanix Flow VPC dev3: The CE then advertised the VIP route to its peer via BGP – the Nutanix Flow BGP Gateway: The Nutanix Flow BGP Gateway received the VIP route and installed it in the VPC routing table: Finally, the VMs in dev3 can securely access nutanix5.f5-demo.com while continuing to use the VPC logical router as their default gateway: F5 Distributed Cloud Console observability provides deep visibility into applications and security events. For example, it offers comprehensive dashboards and metrics to monitor the performance and health of applications served through HTTP load balancers. These include detailed insights into traffic patterns, latency, HTTP error rates, and the status of backend services: Furthermore, the built-in AI assistant provides real-time visibility and actionable guidance on security incidents, improving situational awareness and supporting informed decision-making. This capability enables rapid threat detection and response, helping maintain a strong and resilient security posture: Conclusion The integration demonstrates how F5 Distributed Cloud and Nutanix Flow collaborate to deliver secure, resilient application services across hybrid and multi-cloud environments. Together, F5 and Nutanix enable organizations to scale with confidence, optimize application performance, and maintain robust security—empowering businesses to achieve greater agility and resilience across any environment. This integration is coming soon in CY2026. If you’re interested in early access, please contact your F5 representative. Related URLs Simplifying and Securing Network Segmentation with F5 Distributed Cloud and Nutanix Flow | DevCentral F5 Distributed Cloud - https://www.f5.com/products/distributed-cloud-services Nutanix Flow Virtual Networking - https://www.nutanix.com/products/flow/networking
App Delivery & Security for Hybrid Environments using F5 Distributed Cloud
As enterprises modernize and expand their digital services, they increasingly deploy multiple instances of the same applications across diverse infrastructure environments—such as VMware, OpenShift, and Nutanix—to support distributed teams, regional data sovereignty, redundancy, or environment-specific compliance needs. These application instances often integrate into service chains that span across clouds and data centers, introducing both scale and operational complexity. F5 Distributed Cloud provides a unified solution for secure, consistent application delivery and security across hybrid and multi-cloud environments. It enables organizations to add workloads seamlessly—whether for scaling, redundancy, or localization—without sacrificing visibility, security, or performance.Securing Applications using mTLS Supported by F5 Distributed Cloud
Introduction Mutual Transport Layer Security (mTLS) is a process that establishes encrypted and secure TLS connection between the parties and ensures both parties use X.509 digital certificates to authenticate each other. It helps to prevent the malicious third-party attacks which will imitate the genuine applications. This authentication method helps when a server needs to ensure the authenticity and validity of either a specific user or device. As the SSL became outdated several companies like Skype, Cloudfare are now using mTLS to secure business servers. Not using TLS or other encryption tools without secure authentication leads to ‘man in the middle attacks.’ Using mTLS we can provide an identity to a server that can be cryptographically verified and makes your resources more flexible. mTLS with XFCC Header Not only supporting the mTLS process, F5 Distributed Cloud WAF is giving the feasibility to forward the Client certificate attributes (subject, issuer, root CA etc..) to origin server via x-forwarded-client-cert header which provides additional level of security when the origin server ensures to authenticate the client by receiving multiple requests from different clients. This XFCC header contains the following attributes by supporting multiple load balancer types like HTTPS with Automatic Certificate and HTTPS with Custom Certificate. Cert Chain Subject URI DNS How to Configure mTLS In this Demo we are using httpbin as an origin server which is associated through F5 XC Load Balancer. Here is the procedure to deploy the httpbin application, creating the custom certificates and step-by-step process of configuring mTLS with different LB (Load Balancer) types using F5 XC. Deploying HttpBin Application Here is the link to deploy the application using docker commands. Signing server/leaf cert with locally created Root CA Commands to generate CA Key and Cert: openssl genrsa -out root-key.pem 4096 openssl req -new -x509 -days 3650 -key root-key.pem -out root-crt.pem Commands to generate Server Certificate: openssl genrsa -out cert-key2.pem 4096 openssl req -new -sha256 -subj "/CN=test-domain1.local" -key cert-key2.pem -out cert2.csr echo "subjectAltName=DNS:test-domain1.local" >> extfile.cnf openssl x509 -req -sha256 -days 501 -in cert2.csr -CA root-crt.pem -CAkey root-key.pem -out cert2.pem -extfile extfile.cnf -CAcreateserial Note: Add the TLS Certificate to XC console, create a LB(HTTP/TCP) and attach origin pools and TLS certificates to it. In Ubuntu: Move above created CA certificate (ca-crt.pem) to /usr/local/share/ca-certificates/ca-crt.pem and modify "/etc/hosts" file by mapping the VIP(you can get this from your configured LB -> DNS info -> IP Addr) with domain, in this case the (test-domain1.local). mTLS with HTTPS Custom Certificate Log in the F5 Distributed Cloud Console and navigate to “Web APP & API Protection” module. Go to Load Balancers and Click on ‘Add HTTP Load Balancer’. Give the LB Name (test-mtls-cust-cert), Domain name (mtlscusttest.f5-hyd-demo.com), LB Type as HTTPS with Custom Certificate, Select the TLS configuration as Single Certificate and configure the certificate details. Click in ‘Add Item’ under TLS Certificates and upload the cert and key files by clicking on import from files. Click on apply and enable the mutual TLS, import the root cert info, and add the XFCC header value. Configure the origin pool by clicking on ‘Add Item’ under Origins. Select the created origin pool for httpbin. Click on ‘Apply’ and then save the LB configuration with ‘Save and Exit’. Now, we have created the Load Balancer with mTLS parameters. Let us verify the same with the origin server. mTLS with HTTPS with Automatic Certificate Log in the F5 Distributed Cloud Console and navigate to “Web APP & API Protection” module. Goto Load Balancers and Click on ‘Add HTTP Load Balancer’. Give the LB Name(mtls-auto-cert), Domain name (mtlstest.f5-hyd-demo.com), LB Type as HTTPS with Automatic Certificate, enable the mutual TLS and add the root certificate. Also, enable x-forwarded-client-cert header to add the parameters. Configure the origin pool by clicking on ‘Add Item’ under Origins. Select the created origin pool for httpbin. Click on ‘Apply’ and then save the LB configuration with ‘Save and Exit’. Now, we have created the HTTPS Auto Cert Load Balancer with mTLS parameters. Let us verify the same with the origin server. Conclusion As you can see from the demonstration, F5 Distributed Cloud WAF is providing the additional security to the origin servers by forwarding the client certificate info using mTLS XFCC header. Reference Links mTLS Insights Create root cert pair F5 Distributed Cloud WAFF5 Distributed Cloud (XC) Custom Routes: Capabilities, Limitations, and Key Design Considerations
This article explores how Custom Routes work in F5 Distributed Cloud (XC), why they differ architecturally from standard Load Balancer routes, and what to watch out for in real-world deployments, covering backend abstraction, Endpoint/Cluster dependencies, and critical TLS trust and Root CA requirements.Detection of Malicious Users using F5 Distributed Cloud WAAP – Part III
Introduction We have already discussed the advantages that the F5 Distributed cloud’s solution for malicious users’ brings to the table as well as how simple it is to configure and monitor those events using an interactive UI dashboard of F5 Distributed Cloud Console. Below are the links for parts 1 and 2 of this article: Detection of Malicious Users using F5 Distributed Cloud WAAP – Part I Detection of Malicious Users using F5 Distributed Cloud WAAP – Part II In this article, we will go over a few more test scenarios covering the detection and mitigation of malicious user events. Demonstration (using Multi Load Balancer ML config) Scenario 1: In this scenario, we will monitor and mitigate detected malicious users for forbidden access attempts. Step1: Enable malicious user detection using Multi Load Balancer ML config as mentioned in the document. Step2: Configure a policy that prevents users from accessing a specific path. From the Console homepage, click Web App & API Protection. Click Manage -> Service Policies -> Service Policies. Click 'Add service policy,' give it a name, and set the rules as needed. Here, we are prohibiting access to the path '/delete,' as illustrated in the screenshot below. As a result, users will be unable to access the endpoint "https://<domain>/delete". Go to Home -> Web App & API Protection -> Manage -> Load Balancers -> HTTP Load Balancers, and add the created service policy to the LB Step3: Configure app setting object to detect malicious user activity based on forbidden access requests Go to Home->WAAP->Manage->AI&ML->App Settings, click ‘Add App Setting’. Enter a name and go to ‘AppType’ Settings section. Click ‘Add item’. Click on the ‘App Type’ drop-down and select the app type configured in the LB while executing Step1. Click ‘Configure’ in ‘Malicious User Detection’, tune the settings as per your need. Here, we have set the threshold limit for forbidden access requests to 10, beyond which the system will flag the user as malicious. Apply and add the configurations and then click ‘Save and Exit’ to create the app settings object. Step4: Configure automatic mitigation for malicious users Go to your LB and click ‘Edit Configuration’ Scroll down to ‘Common Security Controls’ section Enable 'Malicious User Mitigation And Challenges'. Set the ‘Malicious User Mitigation Settings’ as ‘Default’. click Save & Exit. Step5: Generate requests (more than the configured threshold value in Step3) to forbidden path (https://<domain>/delete). Note: Here generating requests indicates attempts of an attacker to bypass 403 forbidden error response. For example, trying different HTTP request methods, manipulating endpoint by appending sequences to it like {%2e}, {%2f}, {%5c} or by applying some other technique manually or through script. Step6: Go to Home->Web App & API Protection->Overview->Dashboards->Security Dashboard, select your LB and switch to Malicious Users tab, monitor the activity. Note: You can also use manual configuration for mitigation if automatic mitigation is not applied by simply clicking on ‘Block User’ on the top right side and adding detected malicious user's IP address to the deny list. Scenario 2: In this scenario, we will set the configuration to detect malicious users based on requests from potentially High-Risk IPs and block them by configuring default automatic mitigation action. Step1: Enable malicious user detection using Multi Load Balancer ML config as mentioned in the document. Step2: In app settings object configuration, make sure 'IP Reputation' is enabled (follow points in Step3 from Scenario1). Apply, Save & Exit. Step3: Follow Step4 in Scenario 1 to enable default automatic malicious user mitigation action . Step4: Generate 20+ requests in a minute from Tor browser. At the end follow Step6 from Scenario1 to monitor the malicious user activity Note: Tor is a free and open-source software developed to hide its user’s identity and activities over the Internet and make them anonymous. Conclusion This brings us to the end of this article series. We have seen how F5 Distributed Cloud WAAP’s security solution for malicious users aids in the identification and mitigation of suspicious activities. Alert fatigue, long investigation times, missed attacks, and false positives are all common issues for security teams. However, by utilizing malicious user detection, security teams can effectively filter out noise and identify actual risks and threats without the need for manual intervention. Suspicious actions such as Forbidden access attempts, login failures, and so on create a timeline of events that suggests the possibility of malicious user activity. Users who exhibit such behavior can be blocked manually or automatically based on their threat levels, and exceptions can be made using allow lists.Detection of Malicious Users using F5 Distributed Cloud WAAP – Part I
Introduction As people embraced the Internet as a part of their daily lives, businesses all over the world discovered an easier way to reach a large customer base that is not restricted by geographical boundaries. While that is important, it has also provided an open platform for malicious users to look for potential security loopholes in order to break into the system and cause severe damages. As a result, safeguarding business applications from such malicious user events becomes extremely critical. F5 Distributed Cloud WAAP (Web App & API Protection) offers a solution for monitoring such security events as well as the means to mitigate them. In this series of articles, we will demonstrate enabling, configuring, monitoring, and mitigating malicious users using F5 Distributed Cloud console. Configuration There are two ways to enable malicious user detection: Using Single Load Balancer ML configuration. Using Multi Load Balancer ML configuration. Using Single Load Balancer ML Configuration: In this mechanism, detection is enabled as part of the load balancer configuration and is only applicable to the load balancer on which it is configured. Using Multi Load Balancer ML Configuration: In this mechanism, detection is enabled as part of the app type configuration and is valid for all LBs configured with the same app type label. In both of the mentioned ways, detection is dependent on the ML configuration derived from the app settings object, with the difference that in single load balancer ML config values are not configurable and are set to default, whereas in multi load balancer ML config values can be configured according to the need. Once malicious user events have been identified, the next stage is to prioritize mitigation. The following are two ways of mitigating detected malicious user events: Using Load Balancer Security Monitoring. Using Load Balancer Advanced Security Configuration. Using Load Balancer Security Monitoring This is a manual way of configuring mitigation in which malicious user IPs are added to the allow/deny list. Using Load Balancer Advanced Security Configuration This is an automatic way of enabling mitigation in which the platform will apply the corresponding configured mitigation action for the specific threat levels. The default identifier configured for addressing malicious user events is the client IP address but in the ever-evolving world of attacks spoofing identity is not a difficult task to perform and to uniquely identify a user we should have a set of other identification mechanisms, keeping that in mind F5 Distributed Cloud console also provides you with the option to configure other parameters of identification like cookie name, header name, query parameter, ASN, TLS Fingerprint and combination of IP-header name & IP-TLS Fingerprint. Follow the documentation for step-by-step configuration instructions Demonstration (Using Single Load Balancer ML Configuration) In this demonstration we will enable malicious user detection, configure a WAF policy with enforcement mode as monitoring, configure malicious user mitigation actions for medium and high threat levels and at the end monitor the XC logs for malicious user activity. Step1: Enable malicious user detection using Single Load Balancer ML config as mentioned in the document. Step2: Create an App Firewall policy. Select WAAP service from the home page then go to Manage->App Firewall and click on 'Add App Firewall'. Add name and customize the fields as needed, Save & Exit. Step3: Configure mitigation actions. Go to WAAP->Manage->Shared Objects->Malicious User Mitigation and click on Add Malicious User Mitigation. Add a name, set threat level and associated actions accordingly. Add Item, Save & Exit. Step4: Attach the WAF policy and add the malicious user mitigation settings to the LB. From the Console homepage, Go to Load Balancers->Manage->Load Balancers->HTTP Load Balancers, select ‘Manage Configuration’ as an ‘Action’ to your LB and click ‘Edit Configuration’. Scroll down to Web Application Firewall (WAF), enable it and set the waf policy created in Step 2, Save & Exit. Scroll down to 'Common Security Controls' enable 'Malicious User Mitigation And Challenges', set 'Malicious User Mitigation Settings' as ‘custom’ and add the mitigation rule created in Step 3, Apply the changes. (Note: Here we have provided the flexibility to configure custom malicious user mitigation setting. However, users can also select default, which is a recommended setting). Step5: Generate XSS attack (20+ requests in a minute) e.g., https://<domain>?a=<script> Step6: Monitor the malicious user activity. Go to WAAP -> Overview -> Dashboards->Security Dashboard, scroll down and select your LB. Select Malicious Users tab. On top of the above dashboard, F5 XC console also provides a seperate malicious users dashboard which shows a global view of potential malicious users interacting with the application load balancers in a specific namespace giving a better visibility and greater context about the malicious traffic and ease the process of tracking and mitigating possible attacks with quick assessment. Below are a few screenshots of the same. To view this dashboard navigate to Home -> Web App & API Protection -> Overview -> Threat Insights -> Malicious Users As you can see from the demonstration, even though the waf policy is set to monitoring mode, in the background, malicious user activity is continued to be tracked and the threat level kept increasing with the number of attacks being performed, and once the threat level reached ‘High’, configured mitigation action got triggered. (Note: Based on malicious user mitigation settings different threat levels will have different mitigation actions, for example: in default settings for low threat level, JavaScript Challenge will be applied, for medium threat level, Captcha Challenge will be applied and for high threat level, users will be temporarily blocked). In this scenario, Customers can block attackers in real-time with very low risk of False Positives, as actions are taken based on observed user behavior over time. Conclusion In this article, we discussed how to enable malicious user detection and mitigation and how you can block attackers with a very low risk of False Positives. In future articles, we will discuss other scenarios. So please stay tuned. For further information or to get started: F5 Distributed Cloud Platform (Link) F5 Distributed Cloud WAAP Services (Link)Detection of Malicious Users using F5 Distributed Cloud WAAP – Part II
Introduction This is an extension of the already published article Detection of Malicious Users using F5 Distributed Cloud WAAP – Part I an introductory article which highlights the configurations available for detecting and mitigating malicious user activity and includes a demonstration focused on detecting and mitigating malicious clients based on WAF security events. In part II of this series of articles, we will demonstrate a few more scenarios covering insights of malicious user detection and mitigation feature of F5 Distributed Cloud platform. Demonstration (Using Multi Load Balancer ML Configuration) In this demonstration, we will set the threshold limit for failed login attempts in the app settings configuration to mark any subsequent requests as a malicious user event and apply mitigation rules to restrict access, as well as we will detect the clients based on various user identifier types provided by the F5 Distributed cloud console. Step1: Enable malicious user detection using Multi Load Balancer ML config as mentioned in the document. Step2: Add malicious user mitigation rule to the LB In ‘Common Security Controls’, enable ‘Malicious User Mitigation And Challenges’, set 'Malicious User Mitigation Settings' as ‘Custom’, if the rule is already created select and apply the custom mitigation rule, Save & Exit or click on 'Add Item', add a name, set the rules (threat level and associated actions) accordingly, click continue, apply, Save & Exit. (Note: You can also configure the 'Default' malicious user mitigation settings, which has already defined mitigation rules and is a recommended setting). Step3: Go to Home->WAAP->Manage->AI&ML->App Settings, click ‘Add App Setting’. Step4: Enter a name and click on 'Add Item' to go to the ‘AppType’ settings section. Click ‘Add item’. Step5: Click on the ‘Select Item’ drop-down and select the app type configured in the LB while executing Step1. Step6: Click ‘Configure’ ‘Malicious User Detection’, tune the settings as per your need. For the demonstration purpose we are setting the threshold value for Failed Login Activity to 5. Step7: Apply and add the configurations and then click ‘Save and Exit’ to create the app settings object. Note: Identifying users uniquely on the Internet is a critical task because it aids in the creation of a perception by learning from the activities they perform on the application. Step8: Go to Home->WAAP->Manage->Shared Objects->User Identifications, click ‘Add User Identification’ Add a name, click ‘Configure’ on ‘User Identification Rules’, click ‘Add Item’ Set and apply the user identifier type and add the created user identification policy to the LB. Step9: Generate requests more than the configured threshold limit for failed login attempts in your application; it should return response code as 401. Available User Identifier Types: By default, the user identifier type is set to ‘Client IP Address’. As in the previous article, we have already seen IP address as a client identifier. In this demo, we will set other options available, follow the steps mentioned above to generate failed login events and verify that the users are getting detected based on the configured user identification policy. Below are the screenshots for configured user identification rules and UI dashboards displaying the results of associated configurations: Query Parameter Key HTTP Header Name Cookie Name Client Autonomous System TLS Fingerprint Client IP and HTTP Header Name Client IP and TLS Fingerprint Conclusion In this article, we demonstrated how simple it is to configure your LB to respond to multiple unauthorised access attempts by detecting them using various client identification type options and mitigating them automatically at the same time with a very low risk of false positives. For further information or to get started F5 Distributed Cloud Platform (Link) F5 Distributed Cloud WAAP Services (Link)
