Risk detection has been challenging in the networking domain and requires stringent security addressing. Thetrend in recent times for users to progressively adopt evolving information technologies has become a keymeasure in combating challenging risk in the network space. In this research, a hybridized frameworkcombining Decision Tree and Genetic Algorithms known as the GA-DTA was developed to detect risk innetwork traffic environment. A genetic algorithm fitness function was used and fitness values computed toobased on accuracy, simplicity, support and attribute gain ratio to generate optimal features for the decision treeoptimization. A classifier, C4.5 algorithm is used to train KDD Cup 99 dataset and generate a streamlineddecision trees shown as ordered lists of rule based policy on network. Object- oriented analysis and designmethodology (OOADM) was used as methodology while implementation was done with Java programminglanguage. Experimental phases were tested using WEKA open-source tool to compare the proposed hybridapproach with other techniques. The obtained results showed that compared to C4.5, Random Forest, and GAthat the GA-DTA hybrid algorithm performed better with improved detection accuracy of 99.59%, and reducedfalse value rate of 6.45% as lowest. The hybridized model has proven that risk detection using the combinedalgorithms fared better than other existing models