Malicious　domains　are　crucial　vectors　for　attackers　to　conduct　malicious　activities.　With　the　increasing　numbers　in　domain-based　attack　activities　and　the　enhancement　of　attacker　evasion　methods,　the　detection　of　malicious　domains　has　become　critical　and　increasingly　difficult.　Statistical　feature-based　and　graph　structure-based　detection　methods　are　mainstream　technical　approaches.　However,　highly　hidden　domains　can　escape　feature　detection,　and　the　detection　range　of　graph　structure-based　methods　is　limited.　Based　on　these,　we　propose　a　malicious　detection　method　called　HANDOM.　HANDOM　combines　statistical　features　and　graph　structural　information　to　neutralize　their　limitations,　and　uses　the　Heterogeneous　Attention　Network　(HAN)　model　to　jointly　handle　both　information　to　achieve　high-performance　malicious　domain　classification.　We　conduct　experimental　evaluations　on　real-world　datasets　and　compare　HANDOM　with　machine　learning　methods　and　other　malicious　detection　methods.　The　results　present　that　HANDOM　has　superior　and　robust　performance,　and　can　identify　highly　hidden　domains.　©　2022