Evidence　shows　that　computer　system　users,　small　companies,　and　multinational　corporations　require　Network　Forensics　Analysis　to　prevent　attacks　that　may　compromise　their　data.　To　this　purpose,　logs　automatically　generated　by　computer　systems　can　be　analyzed　to　identify,　control,　and　properly　fight　different　attacks.　However,　large　amounts　of　data　make　it　difficult　to　accurately　analyze　certain　behaviors　that　may　be　considered　risky　to　computer　systems.　This　paper　focuses　on　developing　and　training　a　Machine　Learning　(ML)　Decision　Tree　Model　to　predict　potential　malicious　attacks　originated　from　specific　networks.　The　KDD　Cup　dataset,　which　includes　a　wide　variety　of　network　intrusions　simulated　in　a　military　network　environment,　was　used.　Dataset　was　analyzed　and　subsequently　used　to　train,　test,　correct,　and　adjust　the　proposed　model.　The　used　dataset　also　showed　a　high　risk　at　the　connection　ends.　The　proposed　model　was　coded　in　Python　to　detect　malicious　connections　and　successfully　achieve　99%　accuracy.　The　subsequent　development　of　a　variant　model　using　Principal　Component　Analysis　showed　its　effectiveness　and　robustness　by　reducing　its　complexity.　This　paper　also　offers　the　possibility　of　developing　tools　to　detect　attacks　and　potential　threats　to　security　systems　automatically,　thus　suggesting　computer　administrators　use　Network　Forensics　Analysis,　data　mining,　and　machine　learning　to　provide　security　to　their　computer　system.　©　2024　IEEE.