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Along with the increasing use of computers, business activities and data storage have also begun to be transferred, from the initial use of document files, now changed to computing activities and digital data stored on the computer. The increasing importance of the computer certainly raises its own challenges and risks. One of the risks and challenges is the security of the computer system and network. Various ways have been done to improve computer network security, one of which is using the Intrusion Detection System (IDS). IDS is one of the security management that is able to detect attacks on a computer network in real-time, for example Denial of Service (DoS) attacks. To maximize the IDS detection function that is able to detect unknown attacks, a data mining approach is used to create an IDS that uses anomaly based detection techniques. In this study, a sequential pattern mining technique, the CM-SPADE algorithm, is used to generate IDS rules that can detect DoS attacks. Modeling and rules are made by applying the CM-SPADE algorithm to the KDD Cup 1999 data set. The results of this study reveal that the implementation of the CM-SPADE algorithm is able to produce IDS rules that can detect DoS attacks with an accuracy rate of 97.976%.