DDOS attacks detection based on attention-deep learning and local outlier factor

被引：2
作者：
Dairi, Abdelkader [1 ]
Khaldi, Belkacem [2 ]
Harrou, Fouzi [3 ]
Sun, Ying [3 ]
机构：
[1] Univ Sci & Technol Mohamed Boudiaf Oran, Comp Sci Dept, Bir El Djir, Algeria
[2] Ecole Superieure Informat, Sidi Bel Abbes, Algeria
[3] King Abdullah Univ Sci & Technol, CEMSE Div, Thuwal, Saudi Arabia
来源：
2022 SEVENTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING, FMEC | 2022年
关键词：
Cybersecurity; Distributed Denial of Service; autoencoder; self-attention; recurrent neural network;
D O I：
10.1109/FMEC57183.2022.10062705
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
One of the most significant security concerns confronting network technology is the detection of distributed denial of service (DDOS). This paper introduces a semi-supervised datadriven approach to the detection of DDOS attacks. The proposed method employs normal events data without labeling to train the detection model. Specifically, this approach introduces an improved autoencoder (AE) model by incorporating a Gated Recurrent Unit (GRU) based on the attention mechanism (AM) at the encoder and decoder sides of the AE model. GRU enhances the AE's ability to learn temporal dependencies, and the AM enables the selection of relevant features. For DDOS attacks detection, the local outlier factor (LOF) anomaly detection algorithm is applied to extracted features from the improved AE model. The performance of the proposed approach has been verified using DDOS publically available datasets.
引用
收藏
页码：125 / 128
页数：4
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