ASSESSMENT OF POULTRY DISEASE IN COMMERCIAL POULTRY FARMS IN ZARIA LOCAL GOVERNMENT AREA OF KADUNA STATE
Abstract
leveraging ArtificialInteligence (A) and Machine Learning (ML) to effectively identify and mitigate APTs. Theresearch begins by analyzing the limitations of conventional cybersecurity methods andinvestigates the application of various ML algorithms, including Decision Trees, RandomForests, and Support Vector Machines (SVMS), for enhancing APT detection. The corecontribution is the design ofa novel, three-tiered framework named MLAPT (Machine-Learning-based APT detection system). The framework operates through: (1) a DetectionPhase with eight specialized modules targeting specific APT lifecycle stages (e.g.,đisguised file detection, malicious domain
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