The globalization of the Integrated Circuit (IC) supply chain has introduced the risk of Hardware Trojan (HT) insertion. We propose an unsupervised Hardware Trojan detection method based on the Enhanced Local Outlier Factor (ELOF) algorithm to detect HT efficiently. This method extracts structural and testability features and employs the scoring mechanism of the ELOF algorithm to emphasize the deviation of suspicious HT nets from clusters. Experimental results on Hardware Trojan libraries show that the method achieves an average prediction accuracy (A) of 97.36%, a True Negative Rate (TNR) of 97.81%, a precision (P) of 40.94%, and an F-measure of 49.28%, all of which outperform the Local Outlier Factor (LOF) algorithm and Cluster-Based Local Outlier Factor (CBLOF) algorithm. Notably, the method exhibits superior performance in terms of True Positive Rate (TPR), reaching 70.86%, indicating its efficiency in identifying HT and reducing false negatives. The results demonstrate that the proposed algorithm and feature combination in the approach can significantly enhance the efficiency of Trojan detection.