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Detection and restoration times are often ignored when modeling network reliability. In this paper, we develop Markov Regenerative Reward M o dels (MRRM) to capture the e ects of detection and restoration phases of network recovery. States of the MRRM represent conditions of network resources while state transitions represent occurrences of failure, repair, detection and restoration. Reward r ates, assigned to states of the MRRM are c omputed b ased o n a p erformance m o del that accounts for contention. We compare our model with ones that ignore these parameters and show signi cant di erences, in particular for transient measures.