In　this　article,　an　accurate　secured　framework　to　detect　and　stop　data　integrity　attacks　in　wireless　sensor　networks　in　microgrids　is　proposed.　An　intelligent　anomaly　detection　method　based　on　prediction　intervals　(PIs)　is　introduced　to　distinguish　malicious　attacks　with　different　severities　during　a　secured　operation.　The　proposed　anomaly　detection　method　is　constructed　based　on　the　lower　and　upper　bound　estimation　method　to　provide　optimal　feasible　PIs　over　the　smart　meter　readings　at　electric　consumers.　It　also　makes　use　of　the　combinatorial　concept　of　PIs　to　solve　the　instability　issues　arising　from　the　neural　networks.　Due　to　the　high　complexity　and　oscillatory　nature　of　the　electric　consumers＇　data,　a　new　modified　optimization　algorithm　based　on　symbiotic　organisms　search　is　developed　to　adjust　the　NN　parameters.　The　high　accuracy　and　satisfying　performance　of　the　proposed　model　are　assessed　on　the　practical　data　of　a　residential　microgrid.　©　2005-2012　IEEE.