面向服务质量感知云API推荐系统的数据投毒攻击检测方法

doi:10.11959/j.issn.1000-436x.2023161

Abstract

Abstract:

To solve the problem that existing studies usually assumed that the QoS data of cloud API recommender system was reliable, ignoring the data poisoning attack on cloud API recommender system by malicious users in open network environment, a data poisoning attack detection approach based on multi-feature fusion was proposed.Firstly, a user connected network graph was constructed based on the designed similarity function, and users’ neighborhood features were captured using Node2vec.Secondly, sparse auto-encoder was used to mine user QoS deep feature, and user interpretation feature based on QoS data weighted average deviation was designed.Furthermore, a fake user detection model based on support vector machine was established by integrating user neighborhood feature, QoS deep feature, and interpretation feature, the model parameters were learned using grid search and alternating iterative optimization strategy to complete fake user detection.Finally, the effectiveness and superiority of the proposed approach were verified through extensive experiments, realizing the poison attack defense against QoS aware cloud API recommender system at the data side.

Key words: recommender system, cloud API, quality of service, data poisoning, attack detection

CLC Number:

TP311

Zhen CHEN, Wenchao QI, Taiyu BAO, Limin SHEN. Data poisoning attack detection approach for quality of service aware cloud API recommender system[J]. Journal on Communications, 2023, 44(8): 155-167.

Figures/Tables 11

方法		随机攻击			潮流攻击			均值攻击
方法	准确率	召回率	F1值	准确率	召回率	F1值	准确率	召回率	F1值
NQI-N	0.565	0.500	0.529	0.617	0.667	0.635	0.577	0.594	0.580
NQI-S	0.841	0.817	0.829	0.927	0.951	0.941	0.902	0.947	0.927
NQI-W	0.972	0.951	0.972	0.941	0.918	0.928	0.958	0.922	0.948
NQI-NS	0.554	0.482	0.512	0.524	0.584	0.552	0.590	0.549	0.552
NQI-NW	0.594	0.569	0.573	0.561	0.538	0.545	0.561	0.527	0.548
NQI-SW	0.958	0.945	0.950	0.932	0.910	0.924	0.945	0.962	0.955
NQI	$0 . 989$	$0 . 964$	$0 . 955$	$0 . 975$	$0 . 959$	$0 . 968$	$0 . 970$	$0 . 982$	$0 . 979$

方法		随机攻击			潮流攻击			均值攻击
方法	准确率	召回率	F1值	准确率	召回率	F1值	准确率	召回率	F1值
NQI-N	0.732	0.754	0.742	0.695	0.752	0.617	0.715	0.744	0.729
NQI-S	0.941	0.964	0.955	0.849	0.869	0.854	0.874	0.832	0.857
NQI-W	0.964	0.970	0.966	0.957	0.922	0.941	0.958	0.967	0.962
NQI-NS	0.533	0.630	0.574	0.585	0.562	0.542	0.523	0.583	0.554
NQI-NW	0.583	0.659	0.611	0.570	0.551	0.561	0.614	0.655	0.639
NQI-SW	0.961	0.957	0.958	0.953	0.948	0.955	0.938	0.920	0.928
NQI	$0 . 978$	$0 . 985$	$0 . 980$	$0 . 964$	$0 . 982$	$0 . 975$	$0 . 972$	$0 . 955$	$0 . 961$

References 24

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攻击模型	A_S	A_F	A_Ф	A_T
随机攻击	∅	r_random	∅	r_max/min
潮流攻击	流行的云API	r_random	∅	r_max
均值攻击	∅	r_average	∅	r_max/min

数据	值
响应时间范围/s	(0,20]
平均响应时间/s	0.908
响应时间数据集用户数量/个	339
响应时间数据集云API数量/个	5 818
吞吐量范围/(kbit·s^-1)	(0,1 000]
平均吞吐量/(kbit·s^-1)	47.561
吞吐量数据集用户数量/个	339
吞吐量数据集云API数量/个	5 801

Data poisoning attack detection approach for quality of service aware cloud API recommender system

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