With　the　recent　developments　in　the　Internet　of　Things　(IoT),　the　amount　of　data　collected　has　expanded　tremendously,　resulting　in　a　higher　demand　for　data　storage,　computational　capacity,　and　real-time　processing　capabilities.　Cloud　computing　has　traditionally　played　an　important　role　in　establishing　IoT.　However,　fog　computing　has　recently　emerged　as　a　new　field　complementing　cloud　computing　due　to　its　enhanced　mobility,　location　awareness,　heterogeneity,　scalability,　low　latency,　and　geographic　distribution.　However,　IoT　networks　are　vulnerable　to　unwanted　assaults　because　of　their　open　and　shared　nature.　As　a　result,　various　fog　computing-based　security　models　that　protect　IoT　networks　have　been　developed.　A　distributed　archi-tecture　based　on　an　intrusion　detection　system　(IDS)　ensures　that　a　dynamic,　scalable　IoT　environment　with　the　ability　to　disperse　centralized　tasks　to　local　fog　nodes　and　which　successfully　detects　advanced　malicious　threats　is　available.　In　this　study,　we　examined　the　time-related　aspects　of　network　traffic　data.　We　presented　an　intrusion　detection　model　based　on　a　two　-layered　bidirectional　long　short-term　memory　(Bi-LSTM)　with　an　attention　mechanism　for　traffic　data　classification　verified　on　the　UNSW-NB15　bench-mark　dataset.　We　showed　that　the　suggested　model　outperformed　numerous　leading-edge　Network　IDS　that　used　machine　learning　models　in　terms　of　accuracy,　precision,　recall　and　F1　score.