The issue that this study addresses is the high rate of false positives, high maintenance, and lack of stability and precision that the existing network intrusion detection algorithm faces. To address this problem, we proposed a Local Outlier Factor (LOF) Algorithm that locates outliers and anomalies by comparing the deviation of one data point with respect to its neighbors. To gather data, we will use DARPA’s KDDCup99 as well as questions towards analysts. This data will help determine whether the LOF algorithm is more effective than existing solutions that are presented in the network intrusion detection space.
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Almheiri, Khalifa, "Network Traffic Analysis Using Local Outlier Factor" (2021). Thesis. Rochester Institute of Technology. Accessed from