On this Homepage you will be presented with a brief overview, of the project Hive-Anticheat.


English The project aims to provide our school with reliable cheat detection software to ease the burden on teachers and supervisors. In our fast-changing society, with constantly rising standards and expectations, there is increasing pressure. We aim to provide a certain degree of stability for our education staff. This is achieved by using a daemon to capture key data, which helps to determine the likelihood of any student engaging in fraudulent activities. The captured data is sent to a Spring-Boot server and then forwarded to a database located within the school. There, it is clustered and stored for later evaluation. After the test period ends, the saved data is clustered and visualized on a web page designed for teachers' use. Teachers receive scores indicating the likelihood of a student cheating. Since no algorithm is perfect, teachers are encouraged to review these scores and make corrections if necessary.

Our project is being processed and created at HTL Mössingerstraße.

Deutsch Das Projekt zielt darauf ab, unserer Schule eine zuverlässige Betrugserkennungssoftware zur Verfügung zu stellen, um die Last für Lehrer und Aufsichtspersonal zu verringern. In unserer sich schnell verändernden Gesellschaft mit ständig steigenden Standards und Erwartungen wächst auch der Druck. Wir möchten eine gewisse Stabilität für unser Bildungspersonal bieten. Dies wird erreicht, indem ein Daemon verwendet wird, um Schlüsseldaten zu erfassen, die helfen, die Wahrscheinlichkeit von betrügerischen Aktivitäten seitens der Schüler zu bestimmen. Die erfassten Daten werden an einen Spring-Boot-Server gesendet und dann an eine in der Schule befindliche Datenbank weitergeleitet. Dort werden sie gebündelt und für spätere Auswertungen gespeichert. Nach Ende der Testphase werden die gespeicherten Daten gebündelt und auf einer für Lehrer konzipierten Webseite visualisiert. Lehrer erhalten Bewertungen, die aufzeigen, ob ein Schüler betrogen hat oder nicht. Da kein Algorithmus perfekt ist, werden Lehrer ermutigt, diese Bewertungen zu überprüfen und gegebenenfalls Korrekturen vorzunehmen.



Structure plan

The project was divided into two parts. The green part focuses on capturing essential data. This data is then sent to the Spring Boot server using REST and stored in a MongoDB database. The orange part deals with analyzing this data to identify and detect potential cheaters. Subsequently, these data are visualized for the teacher through a React website. This enables a secure environment.

terminal Packet capture


The daemon has the power to capture network packets. These network packets contain data such as TCP and UDP (repetition for KSN). It also captures certain Windows Key Events and mouse and keyboard strokes. To categorize these data, there are four different entities within the daemon (Network Entity, Windows Events Entity, Mouse Counter Entity, Key Counter Entity). All data is sent directly to the server when there are new pieces of information.


Analyzing and identifying data

The data stored in the database are prepared for a website that is exclusively accessible to teachers. On this website, the data are analyzed and identified using data clustering. Subsequently, the results are visualized, allowing users to view the data and, if necessary, manually evaluate the results.

Used Tools

outgoing_mailData Transmission


RestTemplate ensures the connection between daemon and server.



MongoDB was the final choice for our database, chosen for its user-friendliness and ease of use, attributed to its schemaless nature

web_asset Apps


React allows for a interactive,easy to use and intuitive user-interface.


Spring Boot

A Spring Boot Server enables the communication between Daemon, Database and user-interface.

analyticsClustering Algorithm


K-Means insures that the Data is clustered and ready to be analyzed.

travel_explorenetwork packet capture library


The daemon uses the Java Library Pcap4J to sniff Network Packages.

I guess Our Awesome Team

Nedim Cokoja

terminalResponsible for the Daemon and Data transmition

"I could go one step farther if I wanted to."

Hanno Hunger

analyticsResponsible for the Database, Dataclustering, Teacher-Interface including Datavisualization and option for Evaluation

"There are only 2 things certain in life, death and the fact that i'd rather be somewhere else right now."

Prof. Dipl.-Ing. Benjamin Makula


"Besser spät als nie"