IGPPM: interface grouped probabilistic packet marking scheme for IP traceback

Hongwei Yang; Gang Chen; Chunqian Guo

doi:10.1117/12.3024751

3 April 2024 IGPPM: interface grouped probabilistic packet marking scheme for IP traceback

Hongwei Yang, Gang Chen, Chunqian Guo

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 130780A (2024) https://doi.org/10.1117/12.3024751
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

The Data Packet Marking method is a classic and effective approach for Internet Protocol (IP) traceback, and it has two traditional implementations - Deterministic Packet Marking (DPM) and Probabilistic Packet Marking (PPM). However, these two methods have certain shortcomings: DPM appends routing information for each hop to the packet header to achieve single-packet traceback, which results in oversized packet headers and fragmentation issues. PPM, on the other hand, includes only a single router's information in each marked packet, requiring a large number of marked packets for IP traceback. The IGPPM approach proposed in this article overcomes these limitations by marking multiple router interface numbers within a single packet. This allows recording multiple routing information within a single packet, effectively reducing the number of packets and the time required for traceback. Furthermore, it doesn't increase packet header size, avoiding fragmentation problems, and enhancing the efficiency of traceback.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongwei Yang, Gang Chen, and Chunqian Guo "IGPPM: interface grouped probabilistic packet marking scheme for IP traceback", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 130780A (3 April 2024); https://doi.org/10.1117/12.3024751

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available