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In a contemporary data center, Linux applications often generate a large quantity of real-time system call traces, which are not suitable for traditional host-based intrusion detection systems deployed on every single host. Training data mining models with system calls on a single host that has static computing and storage capacity is time-consuming, and intermediate datasets are not capable of being efficiently handled. It is cumbersome for the maintenance and updating of host-based intrusion detection systems (HIDS) installed on every physical or virtual host, and comprehensive system call analysis can hardly be performed to detect complex and distributed attacks among multiple hosts. Considering these limitations of current system-call-based HIDS, in this article, we provide a review of the development of system-call-based HIDS and future research trends. Algorithms and techniques relevant to system-call-based HIDS are investigated, including feature extraction methods and various data mining algorithms. The HIDS dataset issues are discussed, including currently available datasets with system calls and approaches for researchers to generate new datasets. The application of system-call-based HIDS on current embedded systems is studied, and related works are investigated. Finally, future research trends are forecast regarding three aspects, namely, the reduction of the false-positive rate, the improvement of detection efficiency, and the enhancement of collaborative security.
Liu et al. (Mon,) studied this question.