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author:

Li, Xinghua (Li, Xinghua.) [1] | Xu, Mengfan (Xu, Mengfan.) [2] | Vijayakumar, Pandi (Vijayakumar, Pandi.) [3] | Kumar, Neeraj (Kumar, Neeraj.) [4] | Liu, Ximeng (Liu, Ximeng.) [5]

Indexed by:

EI

Abstract:

The increasingly sophisticated cyber attacks have become a serious challenge for Industrial Internet of Things (IIoT), which presents two new characteristics: low frequency and multi-stage. That is, hackers could gain authority to attack industrial equipment/infrastructure gradually in a long interval through lurking, lateral intrusion and privilege escalation. While, the existing Machine Learning (ML) based intrusion detection schemes all require the participation of expert knowledge, so it is difficult to adaptively select an attack interval and a retraining period of the detection model in IIoT, resulting in poor detection performance. To solve above problems, a bidirectional long and short-Term memory network with multi-feature layer (B-MLSTM) is designed. Firstly, sequence and stage feature layers are introduced in the model training phase model which can learn the corresponding attack interval from historical data, so that the model can effectively detect attacks with different intervals. Then, a double-layer reverse unit is introduced to update the detection model. By collecting information from test data and making association analysis with historical data, the retraining period is adaptively selected to match the new attack interval. Compared with the previous works, our proposed scheme has a lower false positive rate than existing schemes by at least 46.79%, and the false negative rate is reduced by at least 79.85%, which are carried out on three classic IIoT datasets. © 1967-2012 IEEE.

Keyword:

Industrial internet of things (IIoT) Intrusion detection Network security Personal computing

Community:

  • [ 1 ] [Li, Xinghua]School of Cyber Engineering, Xidian University, Xi'an, China
  • [ 2 ] [Xu, Mengfan]School of Cyber Engineering, Xidian University, Xi'an, China
  • [ 3 ] [Vijayakumar, Pandi]Department Computer Science Engineering, Thapar Institute of Engineering and Technology, Patiala, India
  • [ 4 ] [Kumar, Neeraj]Department Computer Science Engineering, Thapar Institute of Engineering and Technology, Patiala, India
  • [ 5 ] [Liu, Ximeng]College of Mathematics and Computer Science, Fuzhou University, Fu'zhou, China

Reprint 's Address:

  • [xu, mengfan]school of cyber engineering, xidian university, xi'an, china

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Source :

IEEE Transactions on Vehicular Technology

ISSN: 0018-9545

Year: 2020

Issue: 8

Volume: 69

Page: 8820-8831

5 . 9 7 8

JCR@2020

6 . 1 0 0

JCR@2023

ESI HC Threshold:132

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 48

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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