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Title: Sensitivity of Dynamic Network Slicing to Deep Reinforcement Learning Based Jamming Attacks
In this paper, we consider multi-agent deep reinforcement learning (deep RL) based network slicing agents in a dynamic environment with multiple base stations and multiple users. We develop a deep RL based jammer with limited prior information and limited power budget. The goal of the jammer is to minimize the transmission rates achieved with network slicing and thus degrade the network slicing agents' performance. We design a jammer with both listening and jamming phases and address jamming location optimization as well as jamming channel optimization via deep RL. We evaluate the jammer at the optimized location, generating interference attacks in the optimized set of channels by switching between the jamming phase and listening phase. We show that the proposed jammer can significantly reduce the victims' performance without direct feedback or prior knowledge on the network slicing policies.  more » « less
Award ID(s):
2221875
NSF-PAR ID:
10464791
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2023 IEEE 34th Annual Inter- national Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Data files were used in support of the research paper titled "“Experimentation Framework for Wireless
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    Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar
    Contact: krd26@drexel.edu

    ---------------------------------------------------------------------------------------------

    Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. 

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    logs:    - data logs collected from devices under test
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    parsers:    - scripts used to collect or process the log files used in the study
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    rayTracingEmulation:    - 'wirelessInsiteImages': images of model used in Wireless Insite
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    results:    - contains performance results used in paper based on parsing of aforementioned log files
     

     
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    Data files were used in support of the research paper titled “Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning" which has been submitted to the IET Communications journal.

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    All data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'

    • 'ModeSelect.py': class used to defined the antenna state selection algorithm
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    Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar
    Contact: krd26@drexel.edu

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    Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.

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    classifier_training: files used for training classifiers that are integrated into SDR platform

    • 'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)
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    post_processing_classifier: contains logs of online classifier outputs and processing script

    • 'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario
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    post_processing_mgen: contains MGEN receiver logs and parser

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    ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.

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    results: contains results obtained from study

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