A Fascinating Journey in Cheat Player Identification using Confluent and AWS
A Fascinating Journey in Cheat Player Identification using Confluent & AWS
- July 26, 2023
- 5 Min Read
- Amrutha Sista
A Fascinating Journey in Cheat Player Identification using Confluent and AWS (Amazon Web Services)
Unmasking Cheaters in the Virtual World
Welcome, gaming enthusiasts, to a gripping exploration of how innovative Confluent and AWS technologies and clever architecture are being deployed to unmask cheaters in the virtual world. In this blog, we will take you on an interactive journey through a sophisticated project that leverages cloud computing and real-time data processing to detect and prevent unfair gameplay.
Gaming has become more immersive than ever before, offering players thrilling experiences and breathtaking adventures. However, with the rise of online multiplayer games, cheating has also found its way into the gaming universe. Cheaters utilize various hacks and exploits to gain an unfair advantage over other players, disrupting the balance and integrity of the gameplay.
But fret not! Game developers and tech wizards have teamed up to devise a state-of-the-art system to catch these deceitful players and ensure a fair and enjoyable gaming environment for everyone.
High-Level Architecture: Unraveling the Mystery Behind Cheat Player Identification
Let us embark on our journey of exploring how we utilized the Confluent and AWS technologies to address the following real-time gaming cheat player identification use cases:
- Use case 1: Detecting Unusual Fast Movement
- Use case 2: Preventing Bug Exploits.
- AWS Lambda and MySQL Instances as Producers
Our journey begins with the first building block of the cheat player identification system: AWS Lambda and MySQL instances. These intelligent producers generate data related to player stats and player account information, and real-time player data and store it in different topics for further analysis.
- Topics: Organizing the Realm of Information
Think of topics as magical scrolls that contain information about players, their actions, and their performance. The “Player” topic houses real-time player data, including their position information, skillfully generated by AWS Lambda.
The “Player Stats” and “Account” topics hold historical performance data and player account information, respectively, enriching the analysis process.
- KSQL Cluster: The Powerful of Real-Time Data Processing and Querying Engine
Enter the KSQL cluster – the heart of our cheat player detection system. This powerful engine aggregates and queries data from the four topics, bringing it all together for analysis. With real-time data processing, KSQL quickly identifies players exhibiting suspicious behavior.
- Use Case 1 – Detecting Unusually Fast Movement
Prepare to be amazed as the KSQL cluster unleashes its magic on the data! For our first use case, we focus on detecting unusually fast movement. By retrieving player data and calculating rolling average speeds, the system swiftly identifies fast-moving players and stores the related data in a new topic called “Cheat Player-Travel Speed”.
- Use Case 2 – Preventing Bug Exploits
Now, let us dive into the second exciting use case – preventing bug exploits. The KSQL cluster once again works its magic, this time tracking player positions and comparing them against known cheat positions in the lookup table. If players cross a cheat position, they are marked as cheaters. The data related to this use case is stored in a new topic called “Cheat Player-Bug Exploits”.
- Player Stats: Embracing Fair Competition
Our quest does not end with catching cheaters! The KSQL cluster, ever diligent, gathers and processes player performance information from the “Player Stats” topic. These valuable insights, such as ranks and kill-to-death ratios, ensure fair competition among players, encouraging them to improve their skills and climb the ranks through hard work and dedication.
- Amazon S3 and Quick Sight: Unveiling the Insights
Our journey reaches its thrilling climax with Amazon S3 and Quick Sight, the tools that bring the entire analysis to life. The enriched topics, “Cheat Player-Travel Speed,” “Cheat Player-Bug Exploits,” and “Player-Stats,” find their way to Amazon S3 Glacier via the “Amazon S3 sink connector.”
From the icy depths of the Glacier, the data emerges into the brilliant light of Quick Sight. With captivating visualizations and engaging insights, this dynamic duo presents a visual narrative of cheating players caught in the act and the triumph of fair gameplay.
Are You Ready to Identify the Cheat Players in Your Gaming Realm?
At iLink Digital, we are passionate about creating fair and enjoyable gaming experiences for players worldwide. Our cheat player identification system is just one example of how we leverage innovative technologies like Confluent and AWS to safeguard the integrity of virtual worlds.
Reach out to us! for a free consultation. Remember, when it comes to fair play, iLink Digital is your ally in the quest for gaming excellence. Let us level up together!
About Author
Amrutha is a Director at the Data BU within iLink focusing on presales and enablement across the unit. In the past Amrutha has been responsible for data engineering and BI efforts on Databricks, Azure and Power BI for several industries like healthcare & retail. She is currently enabling the Confluent practice at iLink Digital and is responsible for Confluent workshop curation efforts alongside Confluent stakeholder. Amrutha is based out of Canada and is a Computer Science graduate. She is a GCP certified professional. She enjoys classical music and loves to go on hikes during her free time.
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