A network security situation awareness model based on cooperative artificial immune system

Qiao, Yu; Ji, Xu

doi:10.1109/csss.2011.5972215

Cited by 1 publication

(2 citation statements)

References 8 publications

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“…(3) When only considering the parameterm, the ACC is gradually improved with the increase of m. From the experimental results of the two datasets, it can clearly be seen that three is a good number of stacked layers in the SDAE. The number of nodes in two adjacent layers are not too different, and the stability interval of m is [5][6][7][8][9][10].…”

Section: Sensitivity Of Network Structurementioning

confidence: 99%

“…For example, Meng [9] proposed a hierarchical multi-source network security threat situation assessment model based on dempster shafer (DS) evidence theory. Qiao et al [10] proposed a network security situation awareness model based on a cooperative artificial immune system. Zhang et al [11] proposed an approach to improve the awareness of network security, based on the Markov game model (MGM).…”

Section: Introductionmentioning

confidence: 99%

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SDAE+Bi-LSTM-Based Situation Awareness Algorithm for the CAN Bus of Intelligent Connected Vehicles

et al. 2021

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With a deep connection to the internet, the controller area network (CAN) bus of intelligent connected vehicles (ICVs) has suffered many network attacks. A deep situation awareness method is urgently needed to judge whether network attacks will occur in the future. However, traditional shallow methods cannot extract deep features from CAN data with noise to accurately detect attacks. To solve these problems, we developed a SDAE+Bi-LSTM based situation awareness algorithm for the CAN bus of ICVs, simply called SDBL. Firstly, the stacked denoising auto-encoder (SDAE) model was used to compress the CAN data with noise and extract the deep spatial features at a certain time, to reduce the impact of noise. Secondly, a bidirectional long short-term memory (Bi-LSTM) model was further built to capture the periodic features from two directions to enhance the accuracy of the future situation prediction. Finally, a threat assessment model was constructed to evaluate the risk level of the CAN bus. Extensive experiments also verified the improved performance of our SDBL algorithm.

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Section: Sensitivity Of Network Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%