Few-Shot Network Intrusion Detection Using Discriminative Representation Learning with Supervised Autoencoder

被引:15
|
作者
Iliyasu, Auwal Sani [1 ]
Abdurrahman, Usman Alhaji [2 ]
Zheng, Lirong [2 ]
机构
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
[2] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 05期
关键词
network intrusion detection; few-shot learning; deep learning; discriminative autoencoder; SPARSE AUTOENCODER; SVM;
D O I
10.3390/app12052351
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Recently, intrusion detection methods based on supervised deep learning techniques (DL) have seen widespread adoption by the research community, as a result of advantages, such as the ability to learn useful feature representations from input data without excessive manual intervention. However, these techniques require large amounts of data to generalize well. Collecting a large-scale malicious sample is non-trivial, especially in the modern day with its constantly evolving landscape of cyber-threats. On the other hand, collecting a few-shot of malicious samples is more realistic in practical settings, as in cases such as zero-day attacks, where security agents are only able to intercept a limited number of such samples. Hence, intrusion detection methods based on few-shot learning is emerging as an alternative to conventional supervised learning approaches to simulate more realistic settings. Therefore, in this paper, we propose a novel method that leverages discriminative representation learning with a supervised autoencoder to achieve few-shot intrusion detection. Our approach is implemented in two stages: we first train a feature extractor model with known classes of malicious samples using a discriminative autoencoder, and then in the few-shot detection stage, we use the trained feature extractor model to fit a classifier with a few-shot examples of the novel attack class. We are able to achieve detection rates of 99.5% and 99.8% for both the CIC-IDS2017 and NSL-KDD datasets, respectively, using only 10 examples of an unseen attack.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] An Intrusion Detection Method Using Few-Shot Learning
    Yu, Yingwei
    Bian, Naizheng
    IEEE ACCESS, 2020, 8 (08): : 49730 - 49740
  • [2] Semi-supervised Few-shot Network Intrusion Detection based on Meta-learning
    Liu, Yao
    Zhou, Le
    Liu, Qiao
    Lan, Tian
    Bai, Xiaoyu
    Zhou, Tinghao
    2023 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS, ITHINGS IEEE GREEN COMPUTING AND COMMUNICATIONS, GREENCOM IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING, CPSCOM IEEE SMART DATA, SMARTDATA AND IEEE CONGRESS ON CYBERMATICS,CYBERMATICS, 2024, : 495 - 502
  • [3] A Novel Self-supervised Few-shot Network Intrusion Detection Method
    Zhang, Jing
    Shi, Zhixin
    Wu, Hao
    Xing, Mengyan
    WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS (WASA 2022), PT I, 2022, 13471 : 513 - 525
  • [4] Intrusion Detection Using Few-shot Learning Based on Triplet Graph Convolutional Network
    Wang, Yue
    Jiang, Yiming
    Lan, Julong
    JOURNAL OF WEB ENGINEERING, 2021, 20 (05): : 1527 - 1552
  • [5] Few-Shot Class-Incremental Learning for Network Intrusion Detection Systems
    Di Monda, Davide
    Montieri, Antonio
    Persico, Valerio
    Voria, Pasquale
    De Ieso, Matteo
    Pescape, Antonio
    IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 2024, 5 : 6736 - 6757
  • [6] A Few-Shot Class-Incremental Learning Method for Network Intrusion Detection
    Du, Lei
    Gu, Zhaoquan
    Wang, Ye
    Wang, Le
    Jia, Yan
    IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2024, 21 (02): : 2389 - 2401
  • [7] A few-shot network intrusion detection method based on mutual centralized learning
    Congyuan Xu
    Fan Zhang
    Ziqi Yang
    Zhihao Zhou
    Yuqi Zheng
    Scientific Reports, 15 (1)
  • [8] Boosting Few-shot Object Detection with Discriminative Representation and Class Margin
    Shi, Yanyan
    Yang, Shaowu
    Yang, Wenjing
    Shi, Dianxi
    Li, Xuehui
    ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2024, 20 (03)
  • [9] A Survey of Few-Shot Learning: An Effective Method for Intrusion Detection
    Duan, Ruixue
    Li, Dan
    Tong, Qiang
    Yang, Tao
    Liu, Xiaotong
    Liu, Xiulei
    SECURITY AND COMMUNICATION NETWORKS, 2021, 2021
  • [10] A Few-shot Deep Learning Approach for Improved Intrusion Detection
    Chowdhury, Md Moin Uddin
    Hammond, Frederick
    Konowicz, Glenn
    Xin, Chunsheng
    Wu, Hongyi
    Li, Jiang
    2017 IEEE 8TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (UEMCON), 2017, : 456 - +