| Title | Resilience of multi-object density fusion against cyber-attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Gao, Lin, Battistelli, Giorgio, Chisci, Luigi |
| Conference Name | 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS) |
| Keywords | Automation, Behavioral sciences, Clutter, control theory, cyberattack, Human Behavior, human factors, numerical analysis, Numerical models, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | Recently, it has been proposed to deal with fusion of multi-object densities exploiting the minimum information loss (MIL) rule, which has shown its superiority over generalized covariance intersection (GCI) fusion whenever sensor nodes have low detection probability. On the contrary, GCI shows better performance than MIL when dense clutter is involved in the measurements. In this paper, we are going to study the behavior of multi-object fusion with MIL and, respectively, GCI rules in the situation wherein the sensor network is exposed to cyber-attacks. Both theoretical and numerical analyses demonstrate that MIL is more robust than GCI fusion when the multi-sensor system is subject to a packet substitution attack. |
| Notes | ISSN: 2475-7896 |
| DOI | 10.1109/ICCAIS56082.2022.9990117 |
| Citation Key | gao_resilience_2022 |
Resilience of multi-object density fusion against cyber-attacks