2022年7月1日 · To address these issues, we propose DFAID, a Density-aware and Feature-deviated Active Intrusion Detection framework over network traffic streams. We first design the mask density score and the feature deviation score to maximize the effectiveness of labeled instances, effectively detecting novel attack classes and the concept drift when ...

2022年7月1日 · We propose a density-aware and feature-deviated active intrusion detection framework over network traffic streams, termed DFAID, and its variation incorporated domain knowledge of attacks, termed DFAID-DK.

为了解决这些问题，我们建议dfaid，一种基于网络流量流的密度感知和特征偏差主动入侵检测框架。 我们首先设计了掩码密度分数和特征偏差分数，以最大限度地提高标记实例的有效性，有效地检测新的攻击类和存在相似类时的概念漂移。

To address these issues, we propose DFAID, a Density-aware and Feature-deviated Active Intrusion Detection framework over network traffic streams. We first design the mask density score and the feature deviation score to maximize the effectiveness of labeled instances, effectively detecting novel attack classes and the concept drift when ...

Contribute to hyunokoh/dfaid development by creating an account on GitHub.

2022年4月1日 · To address these issues, we propose DFAID, a Density-aware and Feature-deviated Active Intrusion Detection framework over network traffic streams.

To address these issues, we propose DFAID, a Density-aware and Feature-deviated Active Intrusion Detection framework over network traffic streams. We first design the mask density score and the feature deviation score to maximize the effectiveness of labeled instances, effectively detecting novel attack classes and the concept drift when ...

DFAID: Density‐aware and feature‐deviated active intrusion detection over network traffic streams (Q115046566) From Wikidata. Jump to navigation Jump to search. scientific article published in 2022. edit. Language Label Description Also known as; English:

DFAID: Density‐aware and feature‐deviated active intrusion detection over network traffic streams Published: 2022-07 Issue: Volume: 118 Page: 102719 ISSN: 0167-4048

Extensive experiments on two well-known benchmarks, CIC-IDS2017 and ISCX-2012, demonstrate that DFAID and its variation DFAID-DK both achieve significant improvement compared with related methods in terms of f1-score (21.7%, 22.7%) on average, and its running speed is an order of magnitude faster.