Domain　generation　algorithms　(DGA)　provide　methods　that　use　specific　parameters　as　random　seeds　to　generate　a　large　number　of　random　domain　names　for　preventing　malicious　domain　name　detection,　which　greatly　increases　the　difficulty　of　detecting　and　defending　botnets　and　malware.　State-of-the-art　models　for　detecting　algorithmically　generated　domain　names　are　generally　based　on　the　principle　of　analyzing　the　statistical　characteristics　of　the　domain　name　and　building　a　classifier　to　locate　the　algorithmically　generated　ones.　However,　most　current　models　have　problems　of　requiring　the　manual　construction　of　feature　sets　for　classification,　as　they　are　sensitive　to　the　imbalance　of　the　sample　distribution　in　the　domain　name　dataset　and　are　difficult　to　adapt　to　frequent　changes　of　the　domain-name　algorithm.　To　address　this　issue,　we　propose　a　hybrid　model　that　combines　a　convolutional　neural　network　(CNN)　and　a　bidirectional　long-term　memory　network　(BLSTM).　First,　to　solve　the　problem　of　the　number　of　domain　names　generated　by　DGAs　being　relatively　small　and　the　sample　distribution　being　unbalanced,　which　consequently　decreases　detection　accuracy,　the　borderline　synthetic　minority　over-sampling　technique　is　employed　to　optimize　the　sample　balance　of　the　domain　name　dataset.　Second,　a　hybrid　deep　neural　network　that　combines　CNN　and　BLSTM　is　introduced　to　extract　the　semantic　and　context-dependency　features　from　the　domain　names.　The　experimental　results　from　different　domain-name　datasets　demonstrate　that　the　proposed　model　achieves　significant　improvement　over　state-of-the-art　models　with　regard　to　precision　and　robustness.　©　2019,　Springer　Nature　Singapore　Pte　Ltd.