Although　academia　has　recognized　the　importance　of　explicitly　specifying　security　requirements　in　early　stages　of　system　developments　for　years,　in　reality,　many　projects　mix　security　requirements　with　other　types　of　requirements.　Thus,　there　is　a　strong　need　for　precisely　and　efficiently　classifying　such　security　requirements　from　other　requirements　in　requirement　specifications.　Existing　studies　leverage　lexical　evidence　to　build　probabilistic　classifiers,　which　are　domain-dependent　by　design　and　cannot　effectively　classify　security　requirements　from　different　application　domains.　In　this　paper,　we　propose　an　ontology-driven　learning　approach　to　automatically　classify　security　requirements.　Our　approach　consists　of　a　conceptual　layer　and　a　linguistic　layer,　which　understands　security　requirements　based　on　not　only　lexical　evidence　but　also　conceptual　domain　knowledge.　In　particular,　we　apply　a　systematic　approach　to　identify　linguistic　features　of　security　requirements　based　on　an　extended　security　requirements　ontology　and　linguistic　knowledge,　connecting　the　conceptual　layer　with　the　linguistic　layer.　Such　linguistic　features　are　then　used　to　train　domain-independent　security　requirements　classifiers　by　using　machine　learning　techniques.　We　have　carried　out　a　series　of　experiments　to　evaluate　the　performance　and　generalization　ability　of　our　proposal　against　existing　approaches.　The　results　of　the　experiments　show　that　the　proposed　approach　outperforms　existing　approaches　with　a　significant　increase　of　Fi　score　(0.63　VS.　0.44)　when　the　training　dataset　and　the　testing　dataset　come　from　different　application　domains,　i.e.,　the　classifiers　trained　by　our　approach　can　be　generalized　to　classify　security　requirements　from　different　domains.　(C)　2020　Elsevier　Inc.　All　rights　reserved.