Intrusion detection is an ongoing and never-ending challenge in the era of networking. Coping up with evolution and technology, intruders tirelessly invade personal and organizational workspace intending to crack their system disrupting the CIA (confidentiality, Integrity and Availability) triad. Though numerous attempts have been made to detect and prevent intrusion, attackers find ways to deceive users with evolved types of attacks. Hence, there is a need for faster and more efficient intrusion detection algorithms to classify the normal and attack traffic accurately. This paper presents an unsupervised approach for intrusion detection based on Percentage Split Clustering (PSC). The proposal exploits Spectral Graph Theory, leveraging the Laplacian Matrix, and applies Percentage Split Clustering exhibiting normalized cut for clustering into normal and abnormal traffic. The performance of the proposed method is evaluated with the KDD cup 1999 dataset, and the results show that the application of PSC is promising in terms of silhouette measure, accuracy, detection rate, and false positive rates.