Disentangled Dynamic Intrusion Detection | JoVE Visualize

Area of Science:

Cybersecurity
Network Security
Machine Learning

Background:

Network-based Intrusion Detection Systems (NIDS) are crucial for defending information infrastructures against escalating cyber threats.
Current NIDS methods exhibit inconsistent performance across different attack types and significantly underperform in few-shot intrusion detection scenarios.
Entangled distributions of network flow features are identified as the primary cause for the limitations of existing NIDS.

Purpose of the Study:

To propose DIDS-MFL, a novel disentangled intrusion detection approach designed for diverse and challenging network security scenarios.
To address the performance inconsistencies and poor few-shot detection capabilities of current NIDS by disentangling network traffic features.
To enhance the detection of known, unknown, and few-shot threats, particularly within encrypted network traffic.

Main Methods:

Developed a double Disentanglement-based Intrusion Detection System (DIDS) that utilizes non-parameterized optimization to differentiate and disentangle complex traffic features, highlighting attack-specific characteristics.
Incorporated a novel graph diffusion method within DIDS for dynamic fusion of network topology, enabling spatial-temporal aggregation of evolving data streams.
Introduced a plug-and-play Multi-scale Few-shot Learning-based (MFL) module employing an alternating optimization framework to address entangled representations in few-shot traffic threats, capturing multi-scale information and optimizing disentanglement.

Main Results:

DIDS-MFL demonstrates significant improvements, achieving a 71.91% - 125.19% increase in average F1-score for few-shot NIDS compared to 14 baseline methods.
The proposed approach shows versatility, proving effective across multiple baseline models and diverse detection tasks.
Experimental results validate the superiority of DIDS-MFL in effectively identifying various attacks, including sophisticated and previously unseen threats.

Conclusions:

DIDS-MFL represents a pioneering advancement in disentangled dynamic intrusion detection, capable of handling diverse attack scenarios.
The method effectively overcomes the limitations of entangled features, offering robust detection for known, unknown, and few-shot intrusions.
DIDS-MFL provides network administrators with a powerful tool for enhanced security, particularly in the challenging domain of encrypted traffic analysis.