A　static　VAR　compensator　(SVC)　is　a　critical　component　for　reactive　power　compensation　in　electric　arc　furnaces　(EAFs)　that　is　used　to　relieve　the　flicker　impacts　and　maintain　the　voltage　level.　A　weak　voltage　profile　can　not　only　reduce　the　power-quality　services,　but　can　also　result　in　system　instability　in　severe　cases.　The　cybersecurity　of　EAFs　is　becoming　a　significant　concern　due　to　their　cyber-physical　structure.　The　reliance　of　SVC　controllers　on　reactive　power　measurement　and　network　communications　has　resulted　in　a　cyber-vulnerability　point　for　unauthorized　access　to　the　EAF,　which　can　affect　its　normal　operation.　This　paper　addresses　concerns　about　cyber　attacks　on　EAFs,　which　can　cause　network　communication　issues　in　measurement　data　for　SVCs.　Three　significant　and　different　types　of　cyber　attacks　that　are　launched　on　SVC　controllers-a　replay　attack,　delay　attack,　and　false　data　injection　attack　(FDIA)-were　simulated　and　investigated.　In　order　to　stop　the　activities　of　cyber　attacks,　a　secured　anomaly　detection　model　(ADM)　based　on　a　prediction　interval　is　proposed.　The　proposed　model　is　dependent　on　a　support　vector　regression　and　a　new　smooth　cost　function　for　constructing　the　optimal　and　symmetrical　intervals.　A　modified　algorithm　based　on　teaching-learning-based　optimization　was　developed　to　adapt　the　ADM＇s　parameters　during　training.　The　simulation＇s　outcomes　on　a　genuine　dataset　showed　the　strong　capability　of　the　proposed　model　against　cyber　attacks　in　EAFs.