Summary of Nids Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and Its Generalization Capability, by Anton Raskovalov et al.
NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability
by Anton Raskovalov, Nikita Gabdullin, Ilya Androsov
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to network intrusion detection systems (NIDS) using neural networks that operate on flow data preprocessed with a time window. The method requires only eleven features that can be easily obtained from conventional flow collectors, making it a scalable solution. The proposed architecture employs KAN-inspired trainable activation functions to achieve high accuracy with simpler network structures, achieving training accuracies exceeding 99% with as little as twenty neural network input features. Additionally, the paper studies the generalization capability of NIDS using CICIDS2017 and a custom dataset, showing that performance metrics decline significantly when changing datasets due to differences in signatures. The results highlight the importance of stable and well-tuned activation functions for achieving high accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to detect network intrusions using neural networks. It works by looking at flow data over time, which helps identify patterns that are hard to find with other methods. This approach requires only 11 features that can be easily collected from normal network monitoring tools. The proposed method achieves high accuracy rates of over 99% even with simple networks and few inputs. The paper also shows how different datasets can have different signatures for the same type of flows, making generalization challenging. It suggests that neural networks with well-tuned activations are more stable and accurate. |
Keywords
» Artificial intelligence » Generalization » Neural network
