JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (3): 28-36.doi: 10.6040/j.issn.1671-9352.4.2020.152

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Adversarial examples generation method for network intrusion detection data

Bin XIE1,2,3(),Qing-yang LI1,Xin-yu DONG1,2   

  1. 1. College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, 050024, Hebei, China
    2. Hebei Provincial Key Laboratory of Network & Information Security, Hebei Normal University, Shijiazhuang, 050024, Hebei, China
    3. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, Hebei Normal University, Shijiazhuang, 050024, Hebei, China
  • Received:2020-06-19 Online:2021-03-20 Published:2021-03-16

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Abstract:

This paper proposes to add directional perturbations having no impact on results to attack characteristics with Deepfool and JSMA algorithms. Adversarial samples are generated by white-box attacks so that they can interfere with the judgements of models to bypass feature detection. Our work provides intrusion detection models with more training samples. As a result, the robustness of intrusion detection models is improved.

Key words: network intrusion detection, neural network, adversarial example, KDD Cup99

CLC Number: 

  • TP399

Fig.1

Adversarial examples space[10]"

Fig.2

Process of network intrusion detection based on machine learning[11]"

Fig.3

Multi-layer neural network model"

Fig.4

An overview on deep autoencoding Gaussian mixture model[14]"

Fig.5

The disturbance vector in Deepfool"

Table 1

Adversarial examples generated by Deepfool"

训练的模型 扰动特征(变化前→变化后) 范数 DAGMM预测值 KNN预测值
1 num_root (0→3) L0=1，L2=23.67 改变判断(7.085→19.374) 不变
2 hot (1→0) L0=3，L2=2.26 改变判断(7.085→12.611) 不变
num_root (0→1)
dst_host_count (70→7)
3 duration (0→360) L0=6，L2=1.55 不改变判(7.085→7.043) 不变
dst_bytes (7300→26604)
srv_diff_host_rate (0→0.05)
dst_host_count (70→29)
srv_diff_host_rate (70→188)
dst_host_rerror_rate(0.2→0.13)

Table 2

Adversarial examples generated by JSMA"

训练的模型 扰动特征(变化前→变化后) 范数 DAGMM预测值 KNN预测值
1 num_root (0->3) L0=1，L2=23.67 改变判断(7.085->19.374) 不变
2 num_file_creations (0→2) L0=2，L2=23.67 改变判断(7.085→7.671) 不变
dst_host_diff_srv_rate (0→0.1)
3 num_root(0→15) L0=1，L2=22.88 改变判断(7.085→20.076) 不变

Table 3

Results for adversarial experiments contain 1 000 attack connections"

对抗算法 平均迭代次数 平均更改特征数(直接生成不调整) 平均对抗成功率/%
Deepfool 3 18 94.4
JSMA 4 12 97.9

Fig.6

Quantitative distribution of changing features by Deepfool"

Fig.7

Quantitative distribution of changing features by JSMA"

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