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With the considerable growth of cybersecurity risks in modern automobiles, cybersecurity issues in the in-vehicle network environment have attracted significant attention from security researchers in recent years. Enhancing the cybersecurity ability of in-vehicle networks while considering the computing resource and cost constraints become an urgent issue. To address this problem, a novel information entropy-based method is proposed in this paper, which uses a fixed number of messages as sliding windows. By improving the sliding window strategy and optimizing the decision conditions, the detection accuracy is increased and the false positive rate is reduced. Experimental results demonstrate that the proposed method can provide real-time response to attacks with a considerably improved detection precision for intrusion detection in the in-vehicle network environment.