Availability　of　suitable　and　validated　data　is　a　key　issue　in　multiple　domains　for　imple-menting　machine　learning　methods.　Higher　data　dimensionality　has　adverse　effects　on　the　learning　algorithm＇s　performance.　This　work　aims　to　design　a　method　that　preserves　most　of　the　unique　information　related　to　the　data　with　minimum　number　of　features.　Address-ing　the　feature　selection　problem　in　the　domain　of　network　security　and　intrusion　detection,　this　work　contributes　an　enhanced　Genetic　Algorithm　(GA)-based　feature　selection　method,　named　as　GA-based　Feature　Selection　(GbFS),　to　increase　the　classifiers＇　accuracy.　Securing　a　network　from　the　cyber-attacks　is　a　critical　task　and　needs　to　be　strengthened.　Machine　learning,　due　to　its　proven　results,　is　widely　used　in　developing　firewalls　and　Intrusion　Detec-tion　Systems　(IDSs)　to　identify　new　kinds　of　attacks.　Utilizing　machine　learning　algorithms,　IDSs　are　able　to　detect　the　intruder　by　analyzing　the　network　traffic　passing　through　it.　This　work　presents　parameter　tuning　for　the　GA-based　feature　selection　along　with　a　novel　fit-ness　function.　The　present　work　develops　an　enhanced　GA-based　feature　selection　method　which　is　tested　over　three　benchmark　network　traffic　datasets,　namely,　CIRA-CIC-DOHBrw-2020,　UNSW-NB15,　and　Bot-IoT.　A　comparison　is　also　performed　with　the　standard　feature　selection　methods.　Results　show　that　the　accuracies　improve　using　GbFS　by　achieving　a　maximum　accuracy　of　99.80%.　(c)　2021　Elsevier　Ltd.　All　rights　reserved.