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Intrusion detection has emerged as an important approach to security problems. This paper proposes an effective anomaly detection method based on Unix shell commands to learn patterns. By looking upon each short shell commands sequence as an instance and each observable symbol as a bag that contains some instances, the task of detecting abnormal behaviors can be mapped as multiple-instance learning. KNN algorithm and Euclidean distances are selected as learning approach and a new kernel method is proposed to calculate the deviation between normal and intrusive bags. The algorithm is simple and can be directly applied. Experiments demonstrate that the method can construct accurate and concise discriminator to detect intrusive actions.