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Model Serving in Red Hat OpenShift AI (on ROSA) with F5 Distributed Cloud API Security","conversation":{"__ref":"Conversation:conversation:338353"},"id":"message:338353","revisionNum":60,"uid":338353,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:326558"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Learn how Red Hat OpenShift AI on ROSA and F5 Distributed Cloud API Security work together to protect generative AI model inference endpoints. This integration ensures robust API discovery, schema enforcement, LLM-aware threat detection, bot mitigation, sensitive data redaction, and continuous observability—enabling secure, compliant, and high-performance AI-driven experiences at scale. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":602},"postTime":"2024-12-18T12:01:43.571-08:00","lastPublishTime":"2024-12-18T12:01:43.571-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" As enterprises embrace Generative AI—particularly deploying large language models (LLMs) and other foundational AI models—production environments become increasingly complex. Organizations need an end-to-end MLOps platform that streamlines the entire lifecycle: developing, training, fine-tuning, and especially serving models at scale. \n Red Hat OpenShift AI (OpenShift AI) meets this need by providing a comprehensive, hybrid MLOps environment. When deployed on Red Hat OpenShift Service on AWS (ROSA), OpenShift AI takes advantage of AWS’s managed infrastructure services and cloud-native elasticity, enabling organizations to scale AI/ML workflows efficiently and cost-effectively. \n Yet, as models are served to end-users or integrated into downstream applications, security considerations become paramount. Inference endpoints may be targeted for unauthorized access, data exfiltration, or prompt manipulation. F5 Distributed Cloud addresses these challenges by offering robust capabilities—API discovery, schema enforcement, LLM-aware threat detection, bot mitigation, sensitive data redaction, and continuous observability—to ensure that inference endpoints remain secure, compliant, and high-performing. \n In this post, we will: \n \n Introduce OpenShift AI and its capabilities for AI/ML workloads with an emphasis on model serving. \n Discuss how running OpenShift AI on ROSA leverages AWS services for scalable, cost-effective AI/ML operations. \n Show how F5 Distributed Cloud API Security enhances the security posture of generative AI model inference endpoints—covering automated API discovery, schema enforcement, threat detection, rate limiting, and compliance measures. \n Demonstrate how to integrate these capabilities end-to-end, using Ollama Mistral-7B as an example model. \n \n What is Red Hat OpenShift AI for Generative AI Applications? \n Red Hat OpenShift AI is a hybrid MLOps platform designed to simplify the entire AI lifecycle. It empowers teams to train, fine-tune, deploy, serve, monitor, and continuously improve generative AI models. By merging IT operations, data science workflows, and application development practices into one platform, OpenShift AI accelerates innovation, fosters governance, and encourages collaboration—essential ingredients for delivering enterprise-grade AI solutions. \n Key Features: \n \n Hybrid MLOps Platform: Unified environment for data scientists, developers, and operators, whether you run on-premises, in the cloud, or in a hybrid setup. \n Distributed Workloads & Fine-Tuning: Scale model training and fine-tuning across distributed compute frameworks, adapting large language models to your domain’s requirements. \n Model Serving & Monitoring: Deploy and serve models at scale using technologies like KServe, ModelMesh, and a variety of specialized runtimes (OpenVINO™ Model Server, Caikit/TGIS, NVIDIA Triton Inference Server, etc.). Continuously monitor model performance, detect drift, and ensure ongoing model quality. \n Lifecycle Management & DevOps Integration: Seamlessly integrate data science pipelines with CI/CD workflows. Automate model deployment, rollout new versions safely, and achieve consistent delivery of AI-driven features. \n Enhanced Collaboration: Enable data scientists, developers, and IT Ops to work together using notebooks (JupyterLab), popular frameworks (TensorFlow, PyTorch), and unified governance, speeding up innovation and time-to-value. \n \n High-Level Architecture: OpenShift AI integrates workbenches, distributed workloads, data science pipelines, serving engines, and monitoring tools atop Kubernetes and OpenShift operators, leveraging GitOps, pipelines, service mesh, and serverless technologies. \n \n Figure 1: OpenShift AI High-Level Architecture \n Running OpenShift AI on ROSA for Scalable AI/ML Solutions \n Red Hat OpenShift Service on AWS (ROSA) brings a fully managed OpenShift environment to AWS. This allows teams to focus on building and serving AI models rather than managing infrastructure. \n Key advantages: \n \n Scalability: Seamlessly scale GPU-accelerated compute, storage, and networking resources as model serving workloads grow. \n Cost Efficiency & On-Demand Resources: Leverage Amazon EC2 instances, Amazon S3, and other AWS services only as needed, paying as you go. \n Unified Management: Offload cluster operations and lifecycle management to ROSA, ensuring reliable operations and freeing your team to concentrate on AI innovation. \n \n Security Challenges in Model Serving for Generative AI \n While OpenShift AI and ROSA simplify operations, serving AI models still raises critical security concerns: \n \n Unauthorized Access & Data Leakage: External requests to inference endpoints may attempt to extract proprietary knowledge or sensitive data from the model. \n Prompt Injection & Malicious Content: LLMs can be tricked into producing harmful or confidential outputs if the prompts are manipulated. \n Bot Attacks & Performance Risks: Automated scripts can overwhelm inference endpoints, degrade performance, or escalate costs. \n Compliance & Sensitive Data Handling: AI outputs can contain PII or regulated data, necessitating encryption, redaction, and audit trails to meet compliance demands. \n Evolving Threat Landscape: The complexity and dynamism of AI models and APIs call for continuous posture management and adaptive threat detection. \n \n Enhancing Security with F5 Distributed Cloud API Security \n F5 Distributed Cloud provides a comprehensive set of capabilities tailored for securing modern AI inference endpoints. By integrating these capabilities with OpenShift AI deployments on ROSA, organizations gain: \n \n Automated API Discovery & Posture Management:\n \n Identify all inference endpoints automatically, eliminating hidden APIs. \n Enforce schemas based on observed traffic to ensure requests and responses follow expected patterns. \n Integrate “shift-left” security checks into CI/CD pipelines, catching misconfigurations before production. \n \n \n LLM-Aware Threat Detection & Request Validation:\n \n Detect attempts to manipulate prompts or break compliance rules, ensuring suspicious requests are blocked before reaching the model. \n \n \n Bot Mitigation & Adaptive Rate Controls:\n \n Differentiate between legitimate users and bots or scrapers, blocking automated attacks. \n Dynamically adjust rate limits and policies based on usage history and real-time conditions, maintaining performance and reliability. \n \n \n Sensitive Data Redaction & Compliance:\n \n Identify and mask PII or sensitive tokens in requests and responses. \n Adhere to data protection regulations and maintain detailed logs for auditing, monitoring, and compliance reporting. \n \n \n Seamless Integration & Observability:\n \n Deploy F5 Distributed Cloud seamlessly alongside OpenShift AI on ROSA without reshuffling existing architecture. \n Use centralized dashboards and analytics to monitor real-time metrics—latency, error rates, compliance indicators—to continuously refine policies and adapt to emerging threats. \n \n \n \n Example: Working with Multiple OLLAMA Models and Programmatic Inference \n In this scenario, multiple OLLAMA models have been deployed on the OpenShift cluster. For instance: \n sh-5.1$ ollama ls \nNAME ID SIZE MODIFIED \nllama2:7b 78e26419b446 3.8 GB 3 days ago \nllama3.2:1b baf6a787fdff 1.3 GB 2 weeks ago \nmario:latest 7434c42677ab 3.8 GB 12 seconds ago \nmistral:latest f974a74358d6 4.1 GB 11 days ago \norca-mini:latest 2dbd9f439647 2.0 GB About a minute ago\nphi3:latest 4f2222927938 2.2 GB 2 weeks ago \nphi3:mini 4f2222927938 2.2 GB 2 weeks ago \ntinyllama:latest 2644915ede35 637 MB 2 weeks ago \n \n We have a variety of models—ranging from Mistral-7B to tinyllama—that can be served simultaneously. While the environment is currently using CPUs for hosting these models, you could leverage GPUs for better performance in a production scenario. \n Unlike the model training and fine-tuning phase—where we worked directly within OpenShift AI Workbenches (to be covered in future blogs)— in this scenario we’re querying the LLM endpoint from a local application, where Python libraries and LangChain are installed. Instead of hitting the cluster directly, we route traffic through an F5 Distributed Cloud-managed URL (e.g., http://llm01.volt.thebizdevops.net). In a real-world deployment, the frontend could also be hosted on OpenShift or served through F5 Distributed Cloud Regional Edges (RE), providing flexible options for scaling and delivering the application. \n This ensures that all requests pass through F5 Distributed Cloud’s security layers, applying policies, detecting threats, and protecting sensitive data before they reach the LLM endpoint hosted in OpenShift AI on ROSA. \n \n Figure 2: Integrated Architecture with F5 Distributed Cloud and OpenShift AI on AWS \n F5 Distributed Cloud Capabilities in Action \n To illustrate the key F5 Distributed Cloud features, we’ve divided them into distinct capabilities and included screen captures in the F5 Distributed Cloud console. These captures provide a visual reference to understand how each capability enhances the security and compliance posture of your LLM inference endpoints. \n 1. API Discovery & Schema Enforcement \n What It Does: F5 Distributed Cloud automatically identifies all exposed inference endpoints for your AI/LLM models. It then derives schemas from real traffic, enforcing expected request and response formats. By blocking malformed inputs early, your model stays protected, ensuring consistent, reliable, and trustworthy inferences. \n \n Figure 3: API Discovery & Schema Enforcement (Refer to the annotated image above showing multiple discovered endpoints, shadow APIs, and downloadable OpenAPI specifications derived from actual traffic patterns.) \n 2. LLM-Aware Threat Detection & Request Validation \n What It Does: This feature identifies potential threats to your LLM endpoints by enforcing strict OpenAPI-based validation on incoming requests. By catching invalid or suspicious inputs early, you can adjust policies to block them in the future, ensuring that malicious attempts—whether aiming to exploit the LLM’s behavior or break compliance rules—never reach the inference logic. \n \n Figure 4: Security Analysis for a Non-Compliant Request (Here, the request triggered an OpenAPI validation failure. Although currently “allowed,” policies can be easily configured to “block” these events going forward, preventing non-compliant or potentially harmful requests from impacting your LLM models.) \n \n \n Figure 5: API Inventory Validation Configuration (This image illustrates the corresponding configuration settings for OpenAPI Validation. By validating both requests and responses at multiple layers—headers, body, and content-type—F5 Distributed Cloud ensures that LLM prompts remain safe, compliant, and free from injection attacks.) \n 3. Bot Mitigation & Rate Limiting \n What It Does: Differentiates legitimate user traffic from automated bots or scrapers bot mitigation, and ensures fair usage of resources (rate limiting). By differentiating legitimate requests from bot-driven abuse and enforcing request thresholds, F5 Distributed Cloud protects inference endpoints from performance degradation while maintaining a positive user experience. \n \n Figure 6: Bot Defense in Action (This image shows how F5 Distributed Cloud identifies and classifies automated traffic as “Bad Bots,” blocking them to preserve endpoint availability and prevent resource exhaustion.) \n \n \n Figure 7: Configuring Rate Limits (This image illustrates the setup of request thresholds for a specific endpoint, ensuring no single client overwhelms the inference service.) \n \n \n Figure 8: Enforced Rate Limit (429 Too Many Requests) (This image demonstrates a client exceeding the above-configured request limit and receiving a 429 response, confirming that F5 Distributed Cloud’s rate limiting is actively maintaining fair resource allocation.) \n 4. Sensitive Data Redaction & Compliance Logging \n What It Does: Identifies and masks personally identifiable information (PII) or other sensitive data—such as credit card numbers, emails, and phone numbers—within model responses. New Sensitive Data Exposure Rules allow you to customize and enforce policies to block or redact sensitive fields dynamically. This ensures compliance with frameworks like HIPAA, GDPR., and other regulatory mandates while capturing detailed logs for auditing. \n \n Figure 9: Adding Sensitive Data Exposure Rules (This image shows how you can add custom rules to detect and control exposure of sensitive fields, such as card-expiration dates, phone numbers, and credit-card details, ensuring model responses comply with organizational security policies.) \n \n \n Figure 10: Sensitive Data Detection Across APIs (Here, sensitive data types—like social-security numbers and phone numbers—are automatically detected across API responses. Built-in and custom rules flag potential exposures, empowering teams to enforce redaction and maintain compliance.) \n \n \n Figure 11: Service Policy for Model Validation (This image shows a service policy in action, blocking an inference request that doesn’t meet defined model validation criteria. Such policies can also be tied to compliance mandates, ensuring non-compliant responses are never returned to clients.) \n \n \n Figure 12: API Compliance & Sensitive Data Detection (Here, sensitive fields like credit-card or phone-number are automatically identified, and associated compliance frameworks (HIPAA, GDPR) are recognized. This empowers you to enforce data redaction, maintain regulatory compliance, and produce audit-ready logs without revealing sensitive details.) \n 5. Centralized Observability & Continuous Policy Updates \n What It Does: Offers dashboards and analytics tools to monitor request volumes, latency, errors, and compliance metrics across your AI inference endpoints. Security teams can leverage these observations to continuously refine their policies, enhance schema validations, and recalibrate rate limits as threats evolve or model usage grows. \n \n Figure 13: Endpoint-Level Metrics Dashboard (This example shows an LLM endpoint /api/generate with available metrics including error rate, latency, request rate, request size, response size, and throughput. By monitoring these trends, teams can quickly identify performance bottlenecks, detect anomalies, and apply targeted policy changes to maintain optimal efficiency and security.) \n The Outcome: Secure, Compliant, and Performant LLM Serving \n By combining Red Hat OpenShift AI on ROSA with F5 Distributed Cloud, organizations can: \n \n Confidently serve multiple LLMs at scale, handling diverse use cases and workloads. \n Securely expose inference endpoints, ensuring that requests from external clients are validated, sanitized, and protected against prompt injection, unauthorized access, or excessive traffic. \n Maintain compliance and privacy, redacting sensitive data and logging requests for auditing and reporting purposes. \n Continuously adapt to evolving threats, leveraging real-time observability and agile policy management for persistent security improvements. \n \n This powerful combination enables generative AI models to be woven into complex enterprise workflows—such as insurance claims processing—without sacrificing trust, governance, or user satisfaction. \n Conclusion \n Red Hat OpenShift AI on ROSA, bolstered by F5 Distributed Cloud API Security, provides a robust, scalable, and secure foundation for running generative AI workloads in production. Together, they address the nuanced security challenges of exposing LLM inference endpoints to external clients. \n Whether you are working with a single LLM or managing a portfolio of models like OLLAMA’s Mistral, Phi3, and TinyLlama, this combination ensures that your users—connecting from anywhere—can trust the quality, security, and compliance of the AI services they rely on. 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munity.f5.com/t5/s/zihoc95639/images/bS0zMjgxMDktZlVPbFhn?revision=15","title":"SecureMCN.jpeg","associationType":"BODY","width":3324,"height":1941,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjgxMDktbXZWMTBx?revision=15\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjgxMDktbXZWMTBx?revision=15","title":"GenAI Architectures - Scenario4_RE_CE_only.jpeg","associationType":"BODY","width":3605,"height":1296,"altText":""},"TkbTopicMessage:message:328109":{"__typename":"TkbTopicMessage","subject":"Protect multi-cloud and Edge Generative AI applications with F5 Distributed Cloud","conversation":{"__ref":"Conversation:conversation:328109"},"id":"message:328109","revisionNum":15,"uid":328109,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:305638"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" F5 Distributed Cloud capabilities allows customers to use a single platform for connectivity, application delivery and security of GenAI applications in any cloud location and at the Edge, with a consistent and simplified operational model, a game changer for streamlined operational experience for DevOps, NetOps and SecOps. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":998},"postTime":"2024-03-11T09:44:59.846-07:00","lastPublishTime":"2024-06-07T12:58:53.662-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n The release of ChatGPT in 2022 saw Generative AI and Large Language Models (LLMs) move from a theoretical field of study to a driving force for an increasing number of real applications. Bloomberg is estimating the GenAI market to reach a size of $1.3 trillion in 2032, an explosive increase of over $40 billion in 2022. The same research points to the synergy between rolling out new GenAI applications and the ongoing move of workloads to the public clouds. \n \n The public cloud providers (AWS, Google, and Microsoft) seem very well positioned to support the massive demand for computation power required by GenAI and there is already stiff competition between them to attract developers and enterprises by expanding their GenAI-supporting features. Customers wanting to leverage the best tool and functionalities from each cloud provider may end up deploying their applications in a distributed way, across multiple cloud providers. \n \n This also has some drawbacks, the complexity of utilizing different environments makes it more difficult to find the diverse skills needed, the lack of unified visibility hinders operations and inconsistent policy enforcement can lead to potential security vulnerabilities. \n \n Securing distributed GenAI workloads with F5 Distributed Cloud \n F5’s response with Distributed Cloud is to simplify connectivity and security across clouds. It can serve both legacy and modern applications, ensuring a consistent SaaS experience. It abstracts away the application delivery and security layers from the underlying infrastructure, preventing vendor lock-in and facilitating workload migrations between the public cloud providers. It also seamlessly integrates with an extensive partner ecosystem, allowing 3rd party service insertion and avoiding its lock-in. \n \n \n \n \n \n \n As a testament to the speed of development in this area, a new direction is already being explored: running GenAI at the Edge. This move is partially driven by the power consumption (and therefore cost) projected to be needed in case GenAI models will keep following the existing trend of being deployed mainly in data centers, see Tirias Research’s “Generative AI Breaks The Data Center Part 1 and 2”. \n \n Generation latency, security, and privacy regulations might be other reasons to consider deploying GenAI models at the Edge, at least for inference and potentially fine-tuning while the training may remain on the cloud. For example, research papers like “An Overview on Generative AI at Scale with Edge-Cloud Computing” show some potential future directions for architecting GenAI applications. \n \n Research has also been carried out on the environmental impact of GenAI, for example, “Reducing the Carbon Impact of Generative AI Inference (today and in 2035)”, one of the mitigation measures being the intelligent distribution of requests to improve the carbon footprint but also maintain user experience, by minimizing user-response latency. \n \n Edge computing has the potential to offer low latency, custom security, privacy compliance, and better cost management. The downsides are similar to the multi-cloud scenario, where multi-vendor complexity is driving up the total cost of ownership and increasing time to market. \n \n F5’s Distributed Cloud AppStack offers a fully integrated stack that enables a consistent deployment model (on-prem or public/private cloud), lowering TCO and shortening TTM. \n \n \n \n F5 can protect LLMs wherever they are deployed. In a scenario where a private LLM needs to be protected, NGINX App Protect can provide API Security by enforcing the OpenAPI spec, ensuring that only the compliant requests are submitted to the LLM: \n \n \n In a scenario where the LLM and the GenAI front-end application are being deployed in different locations (such as the case of a multi-cloud deployment), F5 Distributed Cloud can provide seamless connectivity across any environment (with its AppConnect feature) and also protect the connection with the WAF function: \n \n \n Where \"Inference at the Edge\" is needed, either due to security, regulatory or latency concerns, F5 Distributed Cloud can easily provide an unified deployment environment, portable across different sites, that can also benefit from the full security stack available at the Regional Edge level. For more information on various ways to deploy F5 Distributed Cloud and implementation examples (both manual through the SaaS console and automation), you can consult the “Deploy WAF on any Edge with F5 Distributed Cloud” DevCentral article. \n \n \n For a demo on how NGINX App Protect and F5 Distributed Cloud MultiCloud Networking can secure GenAI workloads, including protection against OWASP’s Sensitive Information Disclosure (LLM06), you can check the following recording: \n \n \n \n For more details on the step-by-step procedure to setup these demos through the Distributed Cloud console, as well as the corresponding automation scripts, you can check the \"F5 Distributed Cloud Terraform Examples\" GitHub repository. \n As shown in these demos, F5 Distributed Cloud enables GenAI applications to be distributed across multiple public clouds (as well as on-prem and private clouds), seamlessly connecting their components with a unitary, single pane of glass, Multicloud Networking (MCN) solution. F5 XC MCN solution employs Customer Edge sites as \"portals\" between different environments, allowing services from one environment to be exposed in the other. In the demo above, the LLM remote service from AWS/EKS is being advertised as local to GCP/GKE, to be used by the GenAI application. Since the service is exposed through an HTTP Loadbalancer XC object, a wide range of security features can be enabled for this service, helping secure the MCN connection. F5 XC Secure MCN (S-MCN) is therefore a complete solution, connecting and securing multicloud and on-prem deployments, regardless of their location. \n API Discovery and enforcement is one of the critical features of F5 Distributed Cloud in this context. Another would be API Rate Limiting, enabling protection against OWASP’s Model Denial of Service (LLM04). You can check the “Protect LLM applications against Model Denial of Service” for an implementation example. \n To help accelerate LLM execution speeds, F5 Distributed Cloud can leverage GPU resources available on Distributed Cloud sites where such hardware is avalable and also supports Virtual GPU (vGPU) applications on Distributed Cloud VMware sites with NVIDIA Tesla T4 vGPU software. \n \n Conclusion \n F5 Distributed Cloud capabilities allow customers to use a single platform for connectivity, application delivery, and security of GenAI applications in any cloud location and at the Edge, with a consistent and simplified operational model, a game changer for streamlined operational experience for DevOps, NetOps, and SecOps. \n \n Resources \n How F5 can help mitigate threats against Generative AI applications \n Deploy WAF on any Edge with F5 Distributed Cloud \n F5 XC Terraform examples GitHub repository \n F5 Hybrid Security Architectures GitHub repository \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"7367","kudosSumWeight":3,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjQuMTF8Mi4xfG98MjV8X05WX3wx","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjgxMDktQ3ZSbTlJ?revision=15\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjQuMTF8Mi4xfG98MjV8X05WX3wy","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjgxMDktSzVZY1hZ?revision=15\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjQuMTF8Mi4xfG98MjV8X05WX3wz","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjgxMDktRDhrbFRw?revision=15\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjQuMTF8Mi4xfG98MjV8X05WX3w0","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjgxMDktZlVPbFhn?revision=15\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjQuMTF8Mi4xfG98MjV8X05WX3w1","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjgxMDktbXZWMTBx?revision=15\"}"}}],"totalCount":5,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:326309":{"__typename":"Conversation","id":"conversation:326309","topic":{"__typename":"TkbTopicMessage","uid":326309},"lastPostingActivityTime":"2024-03-14T10:15:26.056-07:00","solved":false},"User:user:419633":{"__typename":"User","uid":419633,"login":"Koichi","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS00MTk2MzMtMjUxMTJpODRENkE1RkUxRjBDNkI2QQ"},"id":"user:419633"},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjYzMDktODlaSVB6?revision=7\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjYzMDktODlaSVB6?revision=7","title":"417.jpg","associationType":"BODY","width":3017,"height":949,"altText":""},"TkbTopicMessage:message:326309":{"__typename":"TkbTopicMessage","subject":"Security for IoT Devices and Why It’s Important","conversation":{"__ref":"Conversation:conversation:326309"},"id":"message:326309","revisionNum":7,"uid":326309,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:419633"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":163},"postTime":"2024-02-21T13:30:17.504-08:00","lastPublishTime":"2024-03-14T10:15:26.056-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n IoT \n We do use Personal Computers (PC) and server/client computers that run Windows, Linux, etc. PCs are used for helping human activity, thus the computer network is basically for displaying/output info/video/audio to humans and input by humans, so it is Human to Human (H2H) network. When we talk about CyberSecurity, that refers to those server/client PCs and TCP/IP security. \n Other than PC,s there are Micro Computers/Embedded Computers that are used for Home Electronics, Factory machines, live cameras, or small devices. Most of them were used to work for in-house tasks and were not connected to the Internet with TCP/IP protocol until recently. \n By the way, I think the iPhone is the most successful computer device that uses the Internet in the history of computers. However, the iPhone (and iPod touch) is not the first handheld device of Apple. Apple released the “Newton” in 1993 which can perform a variety of tasks like the iPhone. However, the sale of the Newton was not good and the project was canceled. \n We are not concerned here with the question of Newton's failure, however, what the iPhone has and what Newton hasn't - is the ability to connect to the Internet. \n As a result of the mass production of iPhone and other smartphones, microcomputers, GPS receivers, and small sensor devices became inexpensive and mass-produced, and Internet connectivity became possible with inexpensive components. \n Naturally, Micro Computers/Embedded Computers are combined with cheap network access devices - the combination of these microcomputers and networks is called Internet of the Things (IoT). \n In most cases, IoT devices relay data from sensors which is attached to the IoT devices, and it relays to other IoT devices to aggregate the data for processing – the IoT devices are connected, and the communications are done among IoT (machines): so it is Machine to Machine (M2M) network. So the IoT devices are also to be M2M network devices. \n One of the well-known examples of IoT devices is the Arduino board which is an open-source electronics platform based on easy-to-use hardware and software. Arduino is easy to program, and deploy, and can use many types of sensors, and the cost is quite low, thus it has been used in thousands (or more) of projects and applications. \n As IoT devices are much cheaper and easier to produce compared to the PC, the number of IoT devices increases rapidly, and no limitation on the producing number. In contrast, the demand for PCs (and smartphones) is limited by the number of humans. \n The number of IoT devices that are connected (cite from statista) \n \n \n \n \n 2019 \n 8.6 billion \n \n \n 2023 \n 15.14 billion \n \n \n \n \n As the number of IoT devices increases, the network connecting IoT devices (M2M) increases geometrically. \n By the way, BigIP LTM has a profile to use one of the protocols which is used for IoT networks ( Local Traffic ›› Profiles : Services : MQTT ). \n \n Security Concerns \n What problems will arise from IoTs? \n Attack surface \n First, unlike servers and PCs, these IoT devices are small and can be transported anywhere (laptop PC is portable, but limited to the space where human goes). A good example is drones – which has microcomputers and network connecting devices thus IoT - that can move autonomously from place to place, even to the place humans can’t go. Therefore, they are not restricted access by physical location and can be connected to the Internet wherever they are located. Therefore, unlike servers and PCs, it is difficult to know the location of all IoT devices in the world. The problem is that IoT devices are directly accessible by malicious attackers: they can be everywhere and the number of devices are too many to track all of them. \n From the network perspective, servers and PCs are always protected because they have limited physical access points = Attack Surface, and even if they become the source of an attack, the locations of the malicious attacker’s host can be determined to some extent. The CVSS metrics score low when the PC/Servers are not accessible via the network. \n \n In contrast, since it is hard to know all the locations of IoT devices and their M2M network connection points, the Attack Surface is everywhere. If an IoT device that is located in an unknown (physical) place is compromised and becomes a jump host, it might be difficult for a defender to know where the attack is coming from. In other words, being everywhere because it is convenient also means not knowing where they are. We need to think about expanding the attack surface. \n You may recall the Mirai botnet - Mirai's primarily targets IoT devices such as IP cameras and home routers - IP cameras can be placed on the outside of the building so easy to access for the malicious attacker. \n In the Keynote speech of BlackHatAsia 2019, the presenter of the speech said that “IoT is next asbestos!” \n Not only physical access, the range of the network is going to be expanding. As the number of network devices increases, the amount of network connecting them also increases geometrically, so the network that needs to be monitored will also expand. \n Network protocol \n Second, many IoT devices use protocols other than TCP/IP. Since they were used in the closed network before the IoT era like in the factory or car, the design and implementation of such protocols do not need to consider security and privacy. However, once it connects to the Internet, attacks against them are often easy due to the nature of those protocols, which are not security-conscious, and the lack of research that has been done on them may make these attacks possible. \n For example, the CAN network is used for in-vehicle (i.e. car) networks, which have no destination or source address. Therefore, it is difficult to identify and block the source of an attack through the CAN network. \n Some IoT devices communicate through TCP/IP and HTTP protocol, but not HTTPS – it is because those devices are designed to be using small memory and narrow network bandwidth. Of course, it is not secure. \n The amplified DDOS attack \n As mentioned above, the number of the PC is limited while the IoT devices are limitless. And the protocol has some security weaknesses – so the IoT devices could be compromised and used as jump hosts for DDOS campaigns. The jump hosts which is used for the DDOS attack will be amplified - more than that of the H2H network, and it make it difficult to defend. \n Mirai botnet is one of the most famous IoT attacking bots. Quote from What was the Mirai botnet? \"In late 2016 in France, telecom company OVH was hit by a distributed denial-of-service (DDoS) attack. Experts were struck by how the assault was 100 times larger than similar threats.\" This incident happened in 2016 - as shown above, we have more and more IoT devices. \n \n Defense \n IoT devices behave differently than PC/Servers and require their own cybersecurity rules depending on how they operate. To reduce the risks of IoT devices, some system standards and security guidelines for IoT have been published by governments and non-profit organizations. \n USA IoT Cyber Security Improvement Act of 2020 https://www.govinfo.gov/content/pkg/COMPS-15863/pdf/COMPS-15863.pdf \n NIST IoT Device Cybersecurity Guidance for the Federal Government \n https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-213.pdf \n USA DHS : Strategic Principles for Securing the Internet of Things https://www.dhs.gov/securingtheIoT \n IEEE IoT security best practices white paper \n https://standards.ieee.org/wp-content/uploads/import/documents/other/whitepaper-internet-of-things-2017-dh-v1.pdf \n To mitigate the risk, IoT devices need to consider implementing security measures when it is designed. For example, access control feature and user(machine) authorization to change the config, implement encryption (while it is a performance trade-off), monitor the abnormality, and use Edge computer to filter the data and proxy the connection. \n And, of course, not using default user/Password is essential – the Mirai botnet will use them. \n In most cases the data from IoT devices will be uploaded to cloud service and processed on the application layer – thus application firewall can be one of the protection measures. \n \n Future \n Think about the future – the number of IoT devices, the M2M network, and the amount of data on the M2M network will be exponentially increased, thus humans might be overwhelmed to monitor and analyze the network. Recently generative AI has been used for large amounts of data, so AI will also be used for IoT security. As discussed in another article, the security of AI is also important, and, if AI and IoT are united, things need to be considered might be more. \n \n Further study \n Thesis of IoT security in arxiv: \n A Survey of the Security Challenges and Requirements for IoT Operating Systems https://arxiv.org/abs/2310.19825 \n A Unified Taxonomy and Evaluation of IoT Security Guidelines https://arxiv.org/abs/2310.01653 \n Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future https://arxiv.org/abs/2309.02707 \n A Large-Scale Study of IoT Security Weaknesses and Vulnerabilities in the Wild https://arxiv.org/abs/2308.13141 \n Unleashing IoT Security: Assessing the Effectiveness of Best Practices in Protecting Against Threats https://arxiv.org/abs/2308.12072 \n IoT and Man-in-the-Middle Attacks https://arxiv.org/abs/2308.02479 \n 5G Networks and IoT Devices: Mitigating DDoS Attacks with Deep Learning Techniques https://arxiv.org/abs/2311.06938 \n IoT in the Era of Generative AI: Vision and Challenges https://arxiv.org/abs/2401.01923 \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"9835","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjQuMTF8Mi4xfG98MjV8X05WX3wx","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMjYzMDktODlaSVB6?revision=7\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"CachedAsset:text:en_US-components/customComponent/CustomComponent-1728320186000":{"__typename":"CachedAsset","id":"text:en_US-components/customComponent/CustomComponent-1728320186000","value":{"errorMessage":"Error rendering component id: {customComponentId}","bannerTitle":"Video provider requires cookies to play the video. 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