IoT　traffic　classification　is　an　important　step　in　network　management.　Efficient　and　accurate　IoT　traffic　classification　helps　Internet　Service　Providers　provide　high-quality　services　to　network　users.　At　present,　popular　IoT　traffic　classification　methods　are　using　traditional　machine　learning　or　deep　learning　algorithm,　which　relies　on　a　large　amount　of　labeled　traffic　to　construct　the　traffic-level　fingerprinting.　However,　it　is　worth　noting　that　some　classes　of　IoT　devices　only　generate　limited　labeled　traffic　when　they　are　working,　and　this　limited　labeled　traffic　is　insufficient　for　the　aforementioned　classification　methods.　In　this　letter,　we　propose　Festic,　a　few-shot　learning　based　approach　to　IoT　traffic　classification.　Festic　can　accurately　classify　IoT　traffic　under　conditions　of　insufficient　labeled　traffic.　We　evaluate　Festic　on　two　publicly　available　datasets,　and　the　experimental　results　show　that　Festic　has　excellent　classification　accuracy　and　outperforms　the　state-of-the-art　traffic　classification　methods.