From Concept to Content: A Deep Dive into Building and Critically Analyzing Labs
Putting it all together The main bulk of the development work is building the labs. This usually comprises two parts that require different skill sets; one is putting together the written portion of the lab (such as the briefing, tasks, and outcomes), and the other is implementing any technical needs for the practical side of the lab. While some labs may focus more on one component than the other, this general overview of lab development will demonstrate each step of the process. Developing written content Regardless of the lab, the written content forms the backbone of the educational material. Even with prior knowledge and planning, additional research is essential to ensure clear explanations. Once research is complete, an outline is drafted to focus on the flow, ensuring the information is presented logically and coherently. This step helps enhance the final product. The final step is turning the outline into the final written content. Everyone approaches this differently, but personally, I like to note all the points I want to cover in a bullet list before expanding on each one. This method ensures all necessary information is covered, remains concise and clear, and aligns with learning outcomes and objectives. Technical implementation For practical labs, technical setup is key. Practical tasks should reinforce the theoretical concepts covered in the written portion, helping users understand the practical application of what they’ve learned. Before implementing anything, the author decides what to include in the practical section. For a CTI lab on a vulnerability, the vulnerable software must be included, which involves finding and configuring it. For general topics, a custom script or program may be needed, especially for niche subjects. The key is ensuring the technical exercise is highly relevant to the subject matter. Balancing the difficulty of practical exercises is crucial. Too easy, and users won’t engage. Too hard, and they’ll get frustrated. Tasks should challenge users to think critically and apply their knowledge without discouraging them. This requires iterative testing and feedback to fine-tune the complexity. The goal is to bridge the gap between theoretical knowledge and real-world application, making learning effective and enjoyable. Quality assurance and finishing touches The development process is complete, but there’s still work to do before releasing the content. We take pride in polishing our content, so the final steps are crucial. Checking against expectations Before the official QA process, we review the original plan to spot any discrepancies, such as unmet learning objectives or missing topics. While deviations don’t always require changes, they must be justified. Assuring quality A thorough QA process is vital for catching grammatical errors, technical bugs, and general improvements before release. Each lab undergoes three rounds of QA, each performed by a different person – two rounds of technical QA, and one for presentation. Some of the steps taken during technical QA include: Verifying written content accuracy, flow, and completeness. Ensuring all learning objectives are covered. Identifying any critical bugs or vulnerabilities that would allow users to bypass the intended solution. Providing small tweaks or changes to tasks for clarity. Assigning relevant mappings (NICE K-numbers, MITRE tags, CWEs). After technical QA, the lab is reviewed by our quality team to ensure it meets our presentation standards. Once all labs in a collection pass rigorous QA, they are released for users. The final step occurs post-release on the platform. Gathering and implementing user feedback Users are at the heart of everything we do, and we strive to ensure our content provides real value. While our cyber experts share valuable knowledge, user feedback prevents echo chambers and highlights areas for improvement. After new releases, we conduct an evaluation stage to analyze what went well and where we can improve. User feedback We gather quantitative and qualitative feedback to help us identify root issues and solutions. Quantitative feedback involves analyzing metrics like completion rates and time taken. We also examine specific changes, such as frequently missed questions or labs where users drop out. These are important things to note, but we avoid drawing conclusions solely from this data. This is where qualitative feedback comes in. Qualitative feedback includes user opinions and experiences gathered from feedback text boxes, customer support queries, and direct conversations. These responses are stored and read by the team and provide context beyond raw numbers. Channels such as customer support queries and follow-ups with customers also help us improve our content. Post-release reviews We conduct post-release reviews at set intervals after content release to analyze quantitative and qualitative data. This review helps us assess the entire process and identify areas for improvement. These reviews allow us to update content with new features, like adding auto-completable tasks for CyberPro. The reviews ensure our content remains current and enhances user experience. Wrapping up Hopefully, this blog post has provided insight into all the care we put into building and tailoring our content for users. This process has come a long way since we started making labs in 2017! Don't forget — with our new Lab Builder feature, you can now have a go at creating your own custom labs. If there's a topic that interests you and you want to share that knowledge with your team, making your own lab is a great way to do it! If there’s any part of the process you’d like to know more about, ask in the comments. Are there any collections that made you think, “Wow, I wonder how this was made”? Let us know!From Concept to Content: Laying the Foundations of a Lab Collection
Technical planning At this stage, we address niche technical details not covered in initial planning but crucial for polished content. Below is an example of the question-and-answer process used for the “Web Scraping with Python” collection. Should the practical sections of the content be created using Docker, for optimal speed and modularity, or does the subject matter require the use of a full EC2 instance? As there are no unusual requirements for the technical portion of the collection (such as needing kernel-level access, network modifications, or third-party software that doesn’t run in containers), the labs can run on Docker. This is a benefit not only for the overall user experience, but also allows for image inheritance during development, which will be demonstrated a bit later on. Are there any tools, custom scripts, or system modifications that should be present across the whole piece of content? The collection is based around writing Python scripts, so ensuring that Python is installed on the containers as well as any required web scraping libraries is a must. In addition, some considerations for user experience can be made, such as installing an IDE like Visual Studio Code on the containers. How can task verification be implemented to make sure it’s both robust and non-intrusive? In the case of this collection, implementing auto-completable tasks may be difficult due to the variety of ways users can create solutions, as well as the lack of obvious traces left by web scraping. Instead, it may be more appropriate to insert task solutions into a mock website that needs to be scraped, which the user can retrieve by completing the task and providing the solution in an answer box. Understanding the technical requirements for a piece of content helps to bridge the gap between planning and development, making it a crucial step. With some of the key questions answered, it’s time to move on to implementation. Creating base images It’s finally time to put fingertips to keyboards and start programming! The first part of implementation creates what all labs in a collection will be built on – a base image. This is a skeleton that provides all the necessary tools and configuration needed for the whole collection, using a concept called image inheritance. If you're new to containerization software like Docker, don't worry – image inheritance is straightforward. Docker containers use images as blueprints to create consistent, mini-computers (containers). This is useful for labs because it allows you to quickly create a pre-configured container without the overhead of setting up a virtual machine, saving time and system resources. This is where image inheritance comes in. Docker images can inherit traits from parent images, similar to how you inherit eye color from your parents. Instead of one central image for all purposes, you create a parent image with shared requirements and then customize descendant images for specific needs. Let’s use the “Python for web scraping” collection as an example again. Think about what kind of things would need to be present in each lab: An installation of Python so the user can run scripts. A code editor to write the scripts in. A mock website for the user to test their scripts on. The first two of these requirements are essentially the same in every lab; there’s no real need to change the installation of Python or the code editor, and in fact, it would be better to have them all be identical, which would result in a smoother user experience. The third, however, does need to be changed — the specific task requirements are going to be different from lab to lab, and the website files will need to change to accommodate this. Taking into account the requirements, an inheritance structure like this can be used: Base image – Python installation and code editor present Lab 1 – Custom website files Lab 2 – Custom website files Lab 3 – Custom website files … Structuring images this way saves time, disk space, and development work by reusing shared configurations. Next time… In part three of this mini-series, you'll learn about the final stages of content development: creating labs, quality assurance, and release. To be notified when part three is released, follow The Human Connection Blog using the bell icon. Meanwhile, feel free to ask questions about the content creation process or specific collections in the replies. Have you used the Lab Builder feature to make any custom labs yet?From Concept to Content: A Deep Dive into Theorizing and Planning a Lab Collection
The decision process When creating new content, the first step is deciding what to commit to. We consider: User demand: Are users frequently requesting a specific topic? Evolving landscapes: Is there new technology or industry trends we should cover? Internal analysis: Do our cyber experts have unique insights not found elsewhere? Overarching goals: Is the content part of a larger initiative like AI security? Regulations and standards: Can we teach important regulations or standards? Cyber competency frameworks: Are we missing content from frameworks like NICE or MITRE? After considering these points, we prioritize one idea for creation and refinement. Lower-priority ideas are added to a backlog for future use. Feasibility and outcomes Having a concrete idea is just the beginning. Over the years, we’ve learned that understanding the desired outcomes is crucial in planning. Our core mission is education. We ensure that each lab provides a valuable learning experience by setting clear learning objectives and outcomes. We ask ourselves, “What should users learn from this content?” This ranges from specific outcomes, like “A user should be able to identify an SQL Injection vulnerability”, to broader skills, like “A user should be able to critically analyze a full web application”. Listing these outcomes ensures accountability and fulfillment in the final product. Setting clear learning objectives involves defining what users will learn and aligning these goals with educational frameworks like Bloom’s Taxonomy. This taxonomy categorizes learning into cognitive levels, from basic knowledge and comprehension to advanced analysis and creation. This ensures our content meets users at their level and helps them advance. Turning big topics into bite-sized chunks Once a topic is selected, we must figure out how to break down huge subject areas into digestible chunks. This is a fine balance; trying to cram too much information into one lab can be overwhelming, while breaking the subject down too much can make it feel disjointed. One good approach is to examine the learning objectives and outcomes set out in the first step, map them out to specific subtopics, and finally map those to labs or tasks. For example, consider this theoretical set of learning outcomes for a Web scraping with Python lab collection. A user should understand what web scraping is and when it’s useful. A user should be able to make web requests using Python. A user should be able to parse HTML using Python. A user should understand what headless browsers are and when to use them. A user should be able to use a headless browser to parse dynamic content on a webpage. These outcomes can be mapped into two categories: theory outcomes (“A user should understand”) and practical outcomes (“A user should be able to”). Understanding the difference between these two is useful, as a few things can be derived from it – for example, whether to teach a concept in a theory (heavy on theoretical knowledge without providing a practical task) or practical (teaching a concept and exercising it in a practical environment) lab. Using this, the outline for a lab collection can start to take shape, as seen in the table below. Learning outcome Knowledge Type Suggested Lab Title Suggested Lab Content A user should understand what web scraping is and when it is useful. Theory Web scraping with Python – Introduction A theory lab showing the basics of web scraping, how it works, and when it is useful. A user should be able to make web requests using Python. Practical Web scraping with Python – Making web requests A practical lab where the user will write a Python script that makes a web request using the “requests” library. A user should be able to parse HTML using Python. Practical Web scraping with Python – Parsing HTML A practical lab where the user will write a Python script that parses HTML using the “beautifulsoup” library. A user should understand what headless browsers are and when they should be used. Theory Web scraping with Python – Understanding headless browsers A theory lab explaining why dynamic content can’t be scraped using previous methods, and how headless browsers can solve the issue. A user should be able to use a headless browser to parse dynamic content on a webpage. Practical Web scraping with Python – Using headless browsers A practical lab where the user will write a Python script that scrapes dynamic content from a website using the “puppeteer” library. All Demonstrate Web scraping with Python – Demonstrate your skills A demonstrate lab where the user will complete a challenge that requires knowledge from the rest of the collection. Each learning objective is assigned to a lab to ensure thorough and user-friendly coverage. Often, multiple objectives are combined into one lab based on subtopic similarity and the total number of labs in a collection. The above example illustrates the process, but extensive fine-tuning and discussion are needed before finalizing content for development. Next time… In part two of this mini-series, you’ll read about the next stage of the content development process, which involves laying the technical foundations for a lab collection. Don't miss the Series… You can opt to receive an alert when part two of this series is released, by “following” activity in The Human Connection Blog using the bell at the top of this page. In the meantime, feel free to drop any questions about the content creation process in the replies. Are there any parts of the planning process you want to know more about?Feature Focus: Introducing Drag and Drop, Free Text Questions, and Instructional Tasks in the Lab Builder
I’m excited to announce the latest updates to the Lab Builder. Today, we’ve introduced three new task types: Drag and drop Free-text questions Informational/instructional These exciting new task features will enhance the flexibility and interactivity of your labs, offering even more engaging learning experiences. The new tasks can be added to your lab as usual via the Tasks library. They’re live now, so you can start adding them to your labs right away. Drag and drop Drag-and-drop is a dynamic, interactive task. Designed to challenge the user's recognition and matching abilities, it’s perfect for testing their knowledge in various subjects. This task type consists of text-based items and targets. Users need to drag the items to the correct corresponding targets. It’s easy to add and edit items and targets in the Lab Builder quickly. You can have a minimum of two items and a maximum of 12. You could use the drag-and-drop task type for questions and answers, completing sentence fragments, or matching terms with definitions. Once added to your lab, the new task will appear as follows: Free-text questions This task type requires the user to manually enter text to answer a question. For this task type, you need to write a question and provide at least one possible answer – but there can be multiple correct answers. You can configure this easily in the Lab Builder. Fuzzy matching automatically detects answers that are close enough to the correct answer. For example, if the user submits the right answer with a minor spelling error, it’ll still be accepted. This is designed to reduce user frustration and is enabled by default. You can disable fuzzy matching by turning off the toggle at the bottom. Finally, you can also provide feedback to users if they get an answer wrong, sort of like a hint. This is useful if you want to help point your user in the right direction and prevent them from getting stuck. Instructional tasks This task type is designed to provide users with vital information, guidelines, or instructions. In the Lab Builder, they have the same configuration options as the Briefing panel. Instructional tasks are particularly useful in explaining what the user is expected to do in a following task, presenting story details, or providing a learning journey for users as they go through the lab. You may want to remind users about specific information they need to answer some tasks or tell them to log into an application. The example below reminds users to refer to a specific part of the briefing panel before answering the next questions. Why are these new features useful? Increased engagement: These new question types introduce a gamified element to your custom labs, making learning more interactive and enjoyable. Versatile content creation: These features expand the possibilities for creating diverse and engaging labs, allowing you to tailor your content to your organization's unique needs. Enhanced learning: Drag and drop encourages active recall and association, while free text questions promote critical thinking and deeper understanding. Go and build some engaging labs! Explore the possibilities and build labs that truly engage your users! For more guidance, visit our Help Center, where there’s ample documentation on using the Lab Builder in more detail.Human Connection Challenge: Season 1 – Scanning Walkthrough Guide (Official Version)
Time’s Up! Congratulations to everyone who completed Lab 2: Scanning from the Human Connection Challenge: Season 1. In this walkthrough, I'll share some strategies for efficiently completing the lab, based on my perspective as the author. Remember, there are often multiple ways to approach a challenge, so if you used a different method and succeeded, that's perfectly fine! The goal is to learn, and I hope these notes help clarify any steps and reinforce key concepts for the next challenge. This challenge has now ended, but the lab remains available for practice. While prizes are no longer up for grabs, you can still complete the lab and use this walkthrough guide for support if needed. I’ve also used placeholders in some of the commands that would give away an answer directly, so if you see anything enclosed in angle brackets, such as <name server>, please make sure you replace it with the actual value, such as nameserver. With all that considered, let's get started. Overview Task: Identify the name server records of tinytown.bitnet. 1. What is the IP of the first name server for tinytown.bitnet? You’ll first need to open a Terminal on the Kali desktop. Next, you’ll need to query the DNS Server IP (found in the Machines panel) about the tinytown.bitnet domain using the nslookup (Name Server Lookup) tool. You’re specifically looking for NS (Name Server) records, so you can use the -type=ns parameter with nslookup to specify this: nslookup -type=ns tinytown.bitnet [DNS Server IP] The output of this command will return two name servers for the domain labelled with 1 and 2. Your next step is to identify what IP address is associated with the first name server (1). To do this, you can use nslookup along with the name server, domain, and DNS Server IP: nslookup <name server>1.tinytown.bitnet [DNS Server IP] This command will then return an IP address for the name server. 2. What is the IP of the second name server for tinytown.bitnet? As you’ve already identified both name servers, you’ll just need to run the previous command, except with the second (2) name server: nslookup <name server>2.tinytown.bitnet [DNS Server IP] You’ll then find the IP address associated with it. Task: Identify port service information for Target 1. 3. What service version is running on port 53? A network scanning tool like Nmap can help you identify the service version running on a specific port. To do this with Nmap, you can use the -sV option for service detection: nmap -sV [Target 1 IP Address] The output will show what service version is running on port 53. 4. What is the full service banner of port 22? There are a couple of ways to find the full service banner of port 22 – such as with Nmap or Netcat. If you’re using Nmap, you can modify the previous command to include the “banner” script along with the port number: nmap -sV -script=banner [Target 1 IP Address] -p22 The command line will then display the service banner from port 22. You can alternatively use netcat to manually connect to the SSH server. When a client connects, Netcat may present a banner that contains version information. To use Netcat, you’ll need the nc command along with the Target 1 IP address and specify you want to connect to port 22: nc [Target 1 IP Address] 22 When you run this command, the banner appears before the terminal hangs. Task: Identify a token on one of the ports. 5. What is the token? With the previous Nmap command, you initially found that three ports were open on Target 1. However, you’ll need to do a more thorough network scan to find another open port, one not initially found with the previous scans. To do this, you can expand your port scan to cover a much wider range by using Netcat to scan for open ports from 1 through 9000: nc -zvn <Target 1 IP Address> 1-9000 Here, -z will scan for listening services but won’t send any data, -v is verbose mode, which provides more detailed information, and -n tells Netcat not to resolve hostnames via DNS. This command will reveal a fourth open port. Now, you can use Netcat to connect to this port: nc <Target 1 IP Address> <open port> The token will then be displayed in the terminal. Task: Scan the TLS configuration on Target 2. 6. How many protocols are enabled? To scan for SSL/TLS configurations, you can use the sslscan tool. By default, sslscan scans port 443 and will return supported server ciphers, certificate details, and more. You can use sslscan like this: sslscan <Target 2 IP Address> The returned output will be verbose, but you can find and count the number of enabled protocols under the SSL/TLS Protocols subheading. 7. Name an enabled protocol. Using the previous output, name one of the enabled protocols. 8. What exploit are the protocols NOT vulnerable to? Using the same output, scroll down through the results until you find a subheading that’s named after a vulnerability and contains a similar string to: <Protocol> not vulnerable to <vulnerability name> The vulnerability has the same name as the subheading. Task: Identify and extract information from an SMB share on Target 3. 9. What Disk shared directory can you access? To extract information from an SMB (Server Message Block) share, you can use the smbclient tool. First, you’ll need to list the SMB shares on the target using the -L flag (the list/lookup option) with: smbclient -L //<Target 3 IP> You’ll then be prompted for a password, but you can press Enter to skip this. A list of SMB shares will then be displayed, three of which are shown to be a Disk type, so you know the answer will be one of these. You can now begin to go through the list and try to connect to the shares with: smbclient //<Target 3 IP>/<Sharename> However, this time when you’re prompted for a password and you press Enter, you might encounter a message when you try and connect to a share: NT_STATUS_ACCESS_DENIED If you attempt to connect to all shares, you’ll find you can connect to one share without a password. You’ll then be greeted with the following prompt to show the successful connection: smb: \> 10. What is the token stored in the directory? Now that you’re connected, you can execute commands to interact with the SMB share. If you run ls, you’ll find a token.txt file in the current directory. You can then download the file from the share onto your local machine with: get token.txt On the Kali desktop, open the Home folder and the token.txt will be inside. Open this file and find the token. 11. What is the username stored in the directory? After you’ve run ls in the SMB share, you’ll find not only token.txt, but also a file named creds.txt. Use the same command as you just did previously to download the file onto your machine: get creds.txt This file will also be downloaded to the Home folder, where you can find a username and password. Task: Identify open services on Target 3. Task: Connect to Target 3 with the previously found credentials. 12. What is the token stored in the user's /Documents directory? For this final task, you first need to scan the target using Nmap. You’ll find that if you attempt to scan the target without using the -Pn flag, you’ll get a response saying that the host seems down. However, if you run Nmap with -Pn, you’ll find some ports are open: nmap -Pn <Target 3 IP Address> However, the ports returned from this command don’t offer a way to connect to the target. You’ll also need to scan the 6000 most popular ports: nmap -Pn --top-ports 6000 <Target 3 IP Address> These results will now show two additional ports are open regarding the Web Services Management (Wsman) protocol, which is used to communicate with remote machines and execute commands. One of the tools that implement this protocol is Windows Remote Management (WinRM) which is Microsoft’s implementation of Wsman. Knowing this, you can now use Metasploit to interact with the target. In your terminal, run: msfconsole Once loaded, you can use the the following auxiliary module to connect to a system with WinRm enabled and execute a command with: set cmd ls You’ll then need to set the following options, using the credentials you found in the creds.txt file: set username <username> set password <password> set rhosts <Target 3 IP Address> Next, you need to set the cmd option with the command you want to run. If you use the ls command, you’ll be able to find what out files are in the directory you connect to: set cmd ls With all the options set, you can now run the module: run The results of the executed command will be printed on the screen and also saved to a directory, but both show the existence of a token.txt file in the current directory. You can now set the cmd option to type token.txt in Metasploit: set cmd type token.txt Once set, use the run command to send the updated command: run The contents of token.txt will then be displayed on the screen and outputted to a file. Tools For this challenge, you’ll use a range of tools including: Nslookup Nmap Netcat Sslscan Smbclient Metasploit Tips You can use different tools and parameters within those tools to scan for and find information, so don’t be afraid to try out a few different things! If you want to learn more about some of the tools within this lab, take a look at the following collections: Reconnaissance Nmap Infrastructure Hacking Introduction to Metasploit Post Exploitation with Metasploit Conclusion The steps I’ve laid out here aren’t the only way to find the answers to the questions, as long as you find the answer, you did it – well done! If you found another way to find some of these answers and think there’s a better way to do it, please post them in the comments below! I hope you enjoyed the challenge and I’ll see you for the next one.The Human Connection Challenge Lab 1: Basic OS Skills – Walkthrough Guide (Community Version)
This is a walkthrough guide written by one of our community members, who offered to give their perspective on the challenge. Interestingly, they approached this challenge by completing some of the tasks in the graphical user interface (GUI) instead of the command line.The Human Connection Challenge Lab 1: Basic OS Skills - Walkthrough Guide (Official Version)
In this walkthrough, I'll share some strategies for efficiently completing the lab, based on my perspective as the author. Remember, there are often multiple ways to approach a challenge, so if you used a different method and succeeded, that's perfectly fine! The goal is to learn, and I hope these notes help clarify any steps and reinforce key concepts for the next challenge.Introducing The Human Connection Challenge: Season 1
Starting today we will begin releasing a series of all-new Challenge Labs. Each month you’ll be given the chance to showcase your cybersecurity skills across a range of topics and climb the Season 1 Leaderboard, with the chance to win kudos and rewards along the way.
