Subject » BMEVIHIAC12
Network and Traffic Management Laboratory
Hálózat- és forgalommenedzsment laboratórium
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Hálózat- és forgalommenedzsment laboratórium
Network and Traffic Management Laboratory
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVIHIAC12 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 3 | ||||||||||||
| Subject coordinator |
Dr. Pekár Adrián
position: egyetemi docens
contact:
pekar.adrian@vik.bme.hu
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| Responsible department |
Hálózati Rendszerek és Szolgáltatások Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
The topics of the measurements to be carried out in the course are as follows:
1. Orientation (information and rules regarding the semester's lab measurements)
2. Basics of Python (introduction, data types, operators, flow control, conditional expressions, loops, functions/scripts, visualization)
3. Basics of data collection (packet measurement, flow measurement, Wireshark/tshark, Linux networking toolkit, nfstream)
4. Application classification using supervised machine learning on network flows.
5. Anomaly detection using unsupervised machine learning on network flows.
6. Visualization of data from network operation and service performance (Elasticsearch, Grafana, Kibana)
7. Basics of stream-driven processing (network telemetry, Apache Kafka, Stream Processing)
1. Orientation (information and rules regarding the semester's lab measurements)
2. Basics of Python (introduction, data types, operators, flow control, conditional expressions, loops, functions/scripts, visualization)
3. Basics of data collection (packet measurement, flow measurement, Wireshark/tshark, Linux networking toolkit, nfstream)
4. Application classification using supervised machine learning on network flows.
5. Anomaly detection using unsupervised machine learning on network flows.
6. Visualization of data from network operation and service performance (Elasticsearch, Grafana, Kibana)
7. Basics of stream-driven processing (network telemetry, Apache Kafka, Stream Processing)
The aim of the course is to deepen the knowledge acquired in the Network and Traffic Management course, with further practice through laboratory measurements and individual assignments.
A student who successfully completes the course:
• (K2) Understands and comprehends the possibilities of applying and evaluating simple statistical methods.
• (K2) Is familiar with the key technical and legal issues related to network measurements, as well as the associated methods and software tools.
• (K3) Is capable of measuring and analyzing the basic characteristics of network traffic, and drawing conclusions from the results.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
The teaching is conducted through 6 sessions of 4-hour laboratory classes, covering the topics listed in the previous section. Students must prepare for the measurements using electronically available materials. The preparation will be checked at the start of each session in the form of a quiz. If the preparation is inadequate, the student will be instructed to perform a make-up session. During the measurements, the instructors will be continuously available to assist students with any questions.
Upon completing the measurements, a lab report must be submitted, and grading will be based on the submitted report.
Tanulástámogató anyagok
Online források
The supplementary materials and measurement guides are available on the course website. The measurement guides may include additional references to publicly available technical documentation or professional articles in Hungarian or English; if clearly indicated, these are also considered part of the required home preparation.
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Kommunikációs hálózatok, Intelligens hálózat- és forgalomirányítás
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Kommunikációs hálózatok, Intelligens hálózat- és forgalomirányítás
General rules
Requirements:
To receive credit for the semester, each measurement must be completed with at least a passing grade. The final grade for the semester will be the rounded average of the grades received for the quizzes and the individual measurements, in accordance with the regulations (TVSz).
Additional possibilities:
A make-up session can be arranged during the semester at an agreed time within the academic period.
Students have the opportunity to complete up to 2 make-up or improvement sessions during the semester.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
IMSc program:
Separate homework assignments will be issued to IMSc students, and successful completion of these tasks will earn IMSc points.
Additional advanced tasks can be completed during laboratory sessions for IMSc points.
IMSc points:
A maximum of 15 IMSc points can be earned per student as follows:
• Successfully completed optional homework assignments: up to 8 IMSc points.
• Successfully completed additional tasks in laboratory sessions: up to 7 IMSc points. The additional tasks will only be evaluated if the student has received an excellent grade for the regular tasks in the respective measurement.
IMSc points can also be earned by students not participating in the IMSc program under the same conditions.
Recommended courses
Rendszermodellezés,
Információs rendszerek üzemeltetése
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.