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MODLSTM: A Method to Recognize DoS Attacks on Modbus/TCP

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成果类型:
会议论文
作者:
Zhang, Hao;Min, Yuandong;Liu, Sanya;Tong, Hang;Li, Yaopeng
通讯作者:
Zhang, H
作者机构:
[Zhang, H; Tong, Hang; Liu, Sanya; Li, Yaopeng; Zhang, Hao; Min, Yuandong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.
[Zhang, H; Tong, Hang; Liu, Sanya; Li, Yaopeng; Zhang, Hao; Min, Yuandong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构:
[Zhang, H ] C
Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.
Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Modbus;DoS;Deep Learning;Fine-grained Classification
期刊:
IEEE International Conference on Performance, Computing, and Communications
ISSN:
1097-2641
年:
2022
卷:
2022-November
页码:
319-324
会议名称:
IEEE International Performance, Computing, and Communications Conference (IPCCC)
会议论文集名称:
IEEE International Performance Computing and Communications Conference (IPCCC)
会议时间:
NOV 11-13, 2022
会议地点:
Austin, TX
会议主办单位:
[Zhang, Hao;Min, Yuandong;Liu, Sanya;Tong, Hang;Li, Yaopeng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.^[Zhang, Hao;Min, Yuandong;Liu, Sanya;Tong, Hang;Li, Yaopeng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-6654-8018-5
基金类别:
National Natural Science Foundation of China [62077024]
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
With the rapid development of technology, the scale of traffics in industrial control networks is increasing day by day. More malicious traffics brought terrible impacts on industrial areas. Modbus plays a momentous role in the communications of Industrial Control Systems (ICS), but it's vulnerable to Denial of Service attacks(DoS). Traditional methods cannot perform well on fine-grained detection tasks which could contribute to locating targets of DoS and preventing the destruction. Considering the temporal locality and high dimension of malic...

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