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    Network and Traffic Management Laboratory

    A tantárgy neve magyarul / Name of the subject in Hungarian: Hálózat- és forgalommenedzsment laboratórium

    Last updated: 2024. november 5.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics

    Informatics Engineering

    BSc
    Course ID Semester Assessment Credit Tantárgyfélév
    VIHIAC12 6 0/0/2/f 3  
    3. Course coordinator and department Dr. Pekár Adrián,
    4. Instructors Dr. Adrián Pekár
    5. Required knowledge Kommunikációs hálózatok, Intelligens hálózat- és forgalomirányítás
    6. Pre-requisites
    Kötelező:
    Szakirany("AMIN22-INTHÁL/HIT", _) ÉS
    TárgyEredmény( "BMEVIHIAC11" , "aláírás" , _ ) = -1

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

    Ajánlott:
    Rendszermodellezés,
    Információs rendszerek üzemeltetése
    7. Objectives, learning outcomes and obtained knowledge 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.


    8. Synopsis 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)

    9. Method of instruction 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.

    10. Assessment 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).
    11. Recaps 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.

    12. Consultations Based on individual arrangements with the lab instructors.
    13. References, textbooks and resources 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.
    14. Required learning hours and assignment
    Contact hours
    		28
    Preparation for practices28
    Preparation for the mid-term test0
    Homework0
    Processing related literature34
    Preparing for the exam0
    Total90
    15. Syllabus prepared by Dr. Adrián Pekár, associate professor, HIT
    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 score 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.