The rapid growth of computers transformed the way in which information and data was storedrnand transmitted. With this new paradigm of data access, comes the threat of this informationrnbeing exposed to unauthorized and unintended users. Because of this the integrity,rnconfidentiality, and availability of data in a network become the most challenging issue. Manyrnsystems have been developed which scrutinize the data for deviation from the normal behavior orrnsearch for a known signature within the data. These systems are termed as Intrusion DetectionrnSystems (IDS). IDSs employ different techniques varying from statistical methods to machinernlearning algorithms. rnThis paper evaluates the performance of different intrusion detection algorithms using KDDâ€™99rndataset and explores if certain algorithms perform better for certain attack classes andrnconsequently, if a multi-expert classifier design can deliver desired performance measure. Thernalgorithms detection performance is compared by using Detection Rate (DR) and False AlarmrnRate (FAR) evaluation metrics. rnThe experiment performed shows that those algorithms did in fact have different detectionrnperformance for different attack types and no single algorithm exceeds in detecting all attackrntypes. Based on this evaluation results, best algorithms for each attack category is chosen and anrnoptimized hybrid algorithm called K-Means Clustering and Random Forest Based HybridrnIntrusion Detection Algorithm (KRHA) is proposed. rnThe proposed algorithm classifies DoS, Probe, U2R and R2L attacks with 99.12%, 99.06 %,rn89.79% and 78.63% accuracy respectively. This is an improvement from Fuzzy Logic which hasrnhigh detection rate for probe with 98.51% and Random Forest for U2R with 85.6% and K-meansrnclustering algorithm for R2L with 72.04% detection rate.