Its a IDS Presentation

1. Dynamic Multi-Scale Topological
Representation For Enhancing Network
Intrusion Detection

2. Abstract
Network intrusion detection systems (NIDS) play a crucial role in maintaining network
security.
In this paper, we propose a novel dynamic multi-scale topological representation
(DMTR) method for improving network intrusion detection performance.
Our DMTR method achieves the perception of multi-scale topology and exhibits
strong robustness. It provides accurate and stable representations even in the
presence of data distribution shifts and class imbalance problems.
Experiments on four publicly available network traffic datasets demonstrate the
feasibility and effectiveness of the proposed DMTR method in handling class
imbalanced and highly dynamic network traffic.

3. Contributions
In concrete, our contributions can be summarized as follows:
1) We propose DMTR, a novel dynamic multi-scale topological repre-sentation
method for network intrusion detection, which achieves topological awareness of
network traffic.
2) Our DMTR method is able to adapt to dynamic network traffic, al-lowing for
dynamic changes in data behaviour while still capturing multi-scale topological
features.
3) DMTR method preserves the topological information of data in the original space,
providing more valuable features for the detection model.
4) We conduct a detailed experimental evaluation of our proposed method on four
traffic datasets. The experimental results verify the effectiveness of DMTR in
revealing topological data and obtaining discriminative representations in a simple

4. Main Focus
We focus on the topics of deep learning-based representations and topological
structure-based representations, which are closely related to our work.

5. Mapper Algorithm
The Mapper algorithm reveals the high-dimensional topological structure of a dataset
and maps it to a lower-dimensional space for visu-alization. The process of the
Mapper algorithm consists of the following steps:

6. Architecture OF DMTR:

8. Experimental settings and evaluation
metrics
The proposed DMTR method was developed using the “python pro-
gramming language” on Linux Ubuntu 22.04.1 with 128 GB RAM
and
an Intel(R) Xeon(R) Silver 4208 CPU processor.

15. our DMTR method provides a more detailed and multi-
dimensional perspective for high-dimensional data. As for future
work,
exploring more new applications that can benefit from dynamic multi-
scale topological representation will be an interesting and promising
research direction.
Conclusion