This　paper　investigates　data-driven　event-triggered　tracking　control　for　switched　systems　with　dual　unreliable　communication　links.　Consider　modeling　constraints　with　unknown　dynamics,　a　data-driven　model-free　adaptive　control　(MFAC)　method　which　relies　on　system　input　and　output　data　is　adopted.　To　reduce　system　resource　consumption　caused　by　continuous　calculation　of　control　execution,　an　event-triggering　scheme　is　applied.　A　radial　basis　function　(RBF)　neural　network　is　constructed　to　approximate　the　unknown　disturbance.　Accordingly,　the　disturbance　estimation　and　triggered　system　data　are　respectively　transmitted　through　dual　network　channels,　which　are　vulnerable　to　malicious　denial-of-service　(DoS)　attacks.　Then,　a　data　dependent　anti-attack　mechanism　and　an　event-triggered　MFAC　with　disturbance　compensation　are　proposed,　respectively.　Moreover,　in　view　of　the　stability　analysis　of　the　MFAC　switched　systems,　by　introducing　Lyapunov　functional　and　average　dwell　time　technique,　a　set　of　novel　sufficient　conditions　is　derived　for　ensuring　the　boundedness　of　tracking　error.　Finally,　simulation　examples　verify　the　effectiveness　of　the　proposed　method.　©　2021　Elsevier　B.V.