Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Famera_Masters_Thesis.pdf (1.36 MB)

ETD Abstract Container

Abstract Header

Cross-Device Federated Intrusion Detector For Early Stage Botnet Propagation

Famera, Angela Grace
http://orcid.org/0000-0002-0397-5108
http://rave.ohiolink.edu/etdc/view?acc_num=miami167261567571847

Abstract Details

2023, Master of Science, Miami University, Computer Science and Software Engineering.
A botnet is an army of zombified computers infected with malware and controlled by malicious actors to carry out tasks such as Distributed Denial of Service (DDoS) attacks. Billions of Internet of Things (IoT) devices are primarily targeted to be infected as bots since they are configured with weak credentials or contain common vulnerabilities. Detecting botnet propagation by monitoring the network traffic is difficult as they easily blend in with regular network traffic. The traditional machine learning (ML) based Intrusion Detection System (IDS) requires the raw data to be captured and sent to the ML processor to detect intrusion. In this research, we examine the viability of a cross-device federated intrusion detection mechanism where each device runs the ML model on its data and updates the model weights to the central coordinator. This mechanism ensures the client’s data is not shared with any third party, terminating privacy leakage. The model examines each data packet separately and predicts anomalies. We evaluate our proposed mechanism on a real botnet propagation dataset called MedBIoT. In addition, we also examined whether any device taking part in federated learning can employ a poisoning attack on the overall system.
Suman Bhunia (Advisor)
Khodakhast Bibak (Committee Member)
Daniela Inclezan (Committee Member)
60 p.
Computer Science
Federated learning; malware detection; botnet; intrusion detection system; propogation; IoT

Recommended Citations

Citations

  • Famera, A. G. (2023). Cross-Device Federated Intrusion Detector For Early Stage Botnet Propagation [Master's thesis, Miami University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=miami167261567571847

    APA Style (7th edition)

  • Famera, Angela. Cross-Device Federated Intrusion Detector For Early Stage Botnet Propagation. 2023. Miami University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=miami167261567571847.

    MLA Style (8th edition)

  • Famera, Angela. "Cross-Device Federated Intrusion Detector For Early Stage Botnet Propagation." Master's thesis, Miami University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=miami167261567571847

    Chicago Manual of Style (17th edition)

miami167261567571847
324
© 2022, all rights reserved.
This open access ETD is published by Miami University and OhioLINK.