We　employ　the　adaptive　dynamic　programming　(ADP)　approach　with　a　resilient　event-triggering　mechanism　to　handle　the　optimal　control　problems　for　a　switched　system,　which　is　constrained　by　network　resources　and　vulnerable　to　denial-of-service　(DoS)　attacks.　The　proposed　approach　is　able　to　guarantee　satisfactory　system　performances　even　when　the　data　transmission　is　interrupted　intermittently.　A　key　step　is　to　develop　an　updating　method　for　the　neural　network　(NN)　weights　of　ADP　in　response　to　the　triggered　events　and　experienced　attacks.　The　consideration　of　lowering　computational　cost　and　mitigating　attack　influences　is　integrated　into　the　design　of　the　system　switching　law　for　which　a　cost　function　is　utilized　to　reduce　unnecessary　switching.　In　fact,　the　optimal　control　policy　and　optimal　switching　policy　can　be　obtained　as　the　outcome　of　the　converging　ADP　iterative　process.　Furthermore,　the　uniformly　ultimately　boundedness　of　system　state　and　NN　weights　is　proven;　more　importantly,　we　illustrate　in　the　dynamical　processes,　how　the　event-triggering,　system　switching,　and　DoS　attacks　affect　one　another.　Finally,　a　numerical　example　is　provided　to　verify　the　effectiveness　of　the　proposed　method.　IEEE