State　estimation　is　a　very　critical　component　in　smart　grid,　a　typical　energy-based　cyber-physical　system.　Kalman　filter　has　been　widely　used　in　the　dynamic　state　estimation　of　power　systems.　Although　a　large　number　of　research　efforts　have　been　made　on　the　robustness　and　filtering　effectiveness,　little　effort　has　been　conducted　on　cyber　attacks　against　Kalman　filtering.　To　address　this　issue,　in　this　paper　we　systematically　compare　three　representative　Kalman　filtering　techniques　and　formalize　the　problem　of　anomaly　detection　against　false　data　injection　attacks　in　Kalman　filter.　On　the　basis　of　our　modeling　results,　we　investigate　five　novel　attack　approaches　that　can　bypass　the　anomaly　detection.　To　defend　against　those　attacks,　we　develop　two　countermeasures:　the　enhancement　of　Kalman　filtering　and　the　temporal-based　detection　algorithm.　We　conduct　extensive　performance　evaluation　and　our　data　validates　our　theoretical　finding　well.　Copyright　(c)　2013　John　Wiley　&　Sons,　Ltd.