Anomaly　detection　over　HTTP　traffic　has　attracted　much　attention　in　recent　years,　which　plays　a　vital　role　in　many　domains.　This　article　proposes　an　efficient　machine　learning　approach　to　detect　anomalous　HTTP　traffic　that　addresses　the　problems　of　existing　methods,　such　as　data　redundancy　and　high　training　complexity.　This　algorithm　draws　on　natural　language　processing　(NLP)　technology,　uses　the　Word2vec　algorithm　to　deal　with　the　semantic　gap,　and　implements　Term　Frequency-Inverse　Document　Frequency　(TF-IDF)　weighted　mapping　of　HTTP　traffic　to　construct　a　low-dimensional　paragraph　vector　representation　to　reduce　training　complexity.　Then　we　employs　boosting　algorithm　Light　Gradient　Boosting　Machine　(LightGBM)　and　Categorical　Boosting　(CatBoost)　to　build　an　efficient　and　accurate　anomaly　detection　model.　The　proposed　method　is　tested　on　some　artificial　data　sets,　such　as　HTTP　DATASET　CSIC　2010,　UNSW-NB15,　and　Malicious-URLs.　Experimental　results　reveal　that　both　the　boosting　algorithms　have　high　detection　accuracy,　high　true　positive　rate,　and　low　false　positive　rate.　Compared　with　other　anomaly　detection　methods,　the　proposed　algorithms　require　relatively　short　running　time　and　low　CPU　memory　consumption.