基于XGBoost的混合模式门级硬件木马检测方法

张颖; 李森; 陈鑫; 姚嘉祺; 毛志明

doi:10.11999/JEIT200874

基于XGBoost的混合模式门级硬件木马检测方法

doi: 10.11999/JEIT200874

南京航空航天大学电子信息工程学院南京 211106

基金项目: 国家自然科学基金(61701228, 61106029)，模拟集成电路重点实验室基金(61428020304)，航空科学基金(20180852005)

详细信息

作者简介:
张颖：女，1977年生，博士，讲师，研究方向为集成电路设计、验证与测试、硬件安全

李森：男，1995年生，硕士生，研究方向为集成电路验证与测试、硬件安全

陈鑫：男，1982年生，博士，副教授，研究方向为数字集成电路设计

姚嘉祺：男，1996年生，硕士生，研究方向为集成电路验证与测试、硬件安全

毛志明：男，1997年生，硕士生，研究方向为集成电路验证与测试

通讯作者:
张颖　tracy403@nuaa.edu.cn

中图分类号: TP309.5; TN47
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- 被引次数: 0
出版历程
- 收稿日期: 2020-10-12
- 修回日期: 2021-07-20
- 网络出版日期: 2021-07-30
- 刊出日期: 2021-10-18

Hybrid Multi-level Hardware Trojan Detection Method for Gate-level Netlists Based on XGBoost

College of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds: The National Natural Science Foundation of China (61701228, 61106029), The Science and Technology on Analog Integrated Circuit Laboratory (61428020304), The AeronauticalScience Foundation of China (20180852005)

摘要

摘要: 针对恶意的第三方厂商在电路设计阶段中植入硬件木马的问题，该文提出一种基于XGBoost的混合模式门级硬件木马检测方法。该检测方法将电路的每个线网类型作为节点，采用混合模式3层级的检测方式。首先，基于提取的电路静态特征，利用XGBoost算法实现第1层级的检测。继而，通过分析扫描链的结构特征，对第1层级分离得到的正常电路继续进行第2层级的面向扫描链中存在木马电路的静态检测。最后，在第3层级采用动态检测方法进一步提升检测的准确性。Trust-Hub基准测试集的实测结果表明，该方法与现有的其他检测方法相比具有较优的木马检测率，可达到94.0%的平均真阳率(TPR)和99.3%的平均真阴率(TNR)。
- 硬件木马检测 /
- XGBoost算法 /
- 门级网表 /
- 静态检测 /
- 动态检测
Abstract: A hybrid multi-level hardware Trojan detection method based on XGBoost algorithm is proposed for the problem of hardware Trojans implanted by malicious third-party manufacturers. The detection method treats each wire in gate-level netlist as a node and detects Trojans in three levels. Firstly, the effective static features of the circuit are extracted and the XGBoost algorithm is applied to detect the suspicious Trojan circuits. Common circuits distinguished at the first level continued to be detected at the second level by analyzing scan chain structural features. Finally, dynamic detection is used to increase further the accuracy of Trojans detection. Experimental results on Trust-hub benchmark show that this method has a higher accuracy compared with other existing detection methods. This detection method can finally achieve 94.0% average True Positive Rate (TPR) and 99.3% average True Negative Rate (TNR).
- Hardware Trojan detection /
- XGBoost /
- Gate-level netlists /
- Static detection /
- Dynamic detection

HTML全文

图 1 3级触发网络特征示意图

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图 2 环形振荡器结构特征示意图

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图 3 基于XGBoost的混合多层级硬件木马检测框图

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图 4 扫描链中木马电路结构特征

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图 5 特征有效性箱型图结果对比

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图 6 两种方法检测结果比较

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表 1 各层级检测结果详细参数

层级电路		第1层级						第2层级						第3层级
测试电路		TN	FP	FN	TP	TPR	TNR	TN	FP	FN	TP	TPR	TNR	TN	FP	FN	TP	TPR	TNR
Trust-Hub	s38417-T100	5461	10	4	7	0.636	0.998	5461	10	4	7	0.636	0.998	5461	10	0	11	1	0.998
	s38417-T200	5462	9	0	11	1	0.998	5462	9	0	11	1	0.998	5462	9	0	11	1	0.998
	s38417-T300	5467	1	4	43	0.915	0.999	5467	1	3	44	0.936	0.999	5467	1	2	43	0.956	0.999
	s35932-T100	5867	0	0	17	1	1.000	5867	0	0	17	1	1	5867	0	0	17	1	1
	s35932-T200	5857	9	4	8	0.667	0.999	5857	9	2	10	0.833	0.999	5857	9	2	10	0.833	0.999
	s35932-T300	5862	4	2	34	0.944	0.999	5862	4	0	36	1	0.999	5862	4	0	36	1	0.999
	s15850-T100	2122	58	11	15	0.577	0.973	2122	58	9	17	0.654	0.973	2122	58	9	17	0.654	0.973
	RS232-T1000	238	2	0	37	1	0.992	238	2	0	37	1	0.992	238	2	0	37	1	0.992
	RS232-T1100	242	7	6	32	0.842	0.972	242	7	6	32	0.842	0.972	242	7	6	32	0.842	0.972
	RS232-T1200	252	1	3	30	0.909	0.996	252	1	3	30	0.909	0.996	252	1	3	30	0.909	0.996
	RS232-T1300	251	2	0	27	1	0.992	251	2	0	27	1	0.992	251	2	0	27	1	0.992
	RS232-T1400	237	2	0	44	1	0.992	237	2	0	44	1	0.992	237	2	0	44	1	0.992
	RS232-T1500	245	2	0	38	1	0.992	245	2	0	38	1	0.992	245	2	0	38	1	0.992
	RS232-T1600	250	2	3	22	0.880	0.992	250	2	3	22	0.880	0.992	250	2	1	24	0.960	0.992
	平均值	TPR:88.4% TNR:99.3%						TPR:90.6% TNR:99.3%						TPR:94.0% TNR:99.3%
DeTrust	DT-1	575	53	4	17	0.810	0.916	575	53	4	17	0.810	0.916	575	40	4	19	0.826	0.920
	DT-2	570	49	5	18	0.783	0.921	570	49	5	18	0.783	0.921	570	47	4	18	0.818	0.924
	DT-3	552	51	4	15	0.789	0.915	552	51	4	15	0.789	0.915	552	46	4	17	0.810	0.923
	DT-4	582	55	5	19	0.792	0.914	582	55	5	19	0.792	0.914	582	52	5	21	0.808	0.918
	DT-5	563	49	3	15	0.833	0.920	563	49	3	15	0.833	0.920	563	46	3	16	0.842	0.924
	平均值	TPR:80.1% TNR:91.7%						TPR:80.1% TNR:91.7%						TPR:82.1% TNR:92.2%
平均值		TPR:84.2% TNR:95.5%						TPR:85.3% TNR:95.5%						TPR:88.1% TNR:95.8%

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