Compliance management for procurement internal auditing has been a major challenge for public sectors due to its tedious period of manual audit history and large-scale paper-based repositories. Many practical issues and potential risks arise during the manual audit process, including a low level of efficiency, accuracy, accountability, high expense and its laborious and time consuming nature. To alleviate these problems, this paper proposes a continuous compliance awareness framework (CoCAF). It is defined as an AI-based automated approach to conduct procurement compliance auditing. CoCAF is used to automatically and timely audit an organisation’s purchases by intelligently understanding compliance policies and extracting the required information from purchasing evidence using text extraction technologies, automatic processing methods and a report rating system. Based on the auditing results, the CoCAF can provide a continuously updated report demonstrating the compliance level of the procurement with statistics and diagrams. The CoCAF is evaluated on a real-life procurement data set, and results show that it can process 500 purchasing pieces of evidence within five minutes and provide 95.6% auditing accuracy, demonstrating its high efficiency, quality and assurance level in procurement internal audit.

Traditional Intrusion Detection Systems (IDS) cannot cope with the increasing number and sophistication of cyberattacks such as Advanced Persistent Threats (APT) . Due to their high false-positive rate and the required effort of security experts to validate them, incidents can remain undetected for up to several months. As a result, enterprises suffer from data loss and severe financial damage. Recent research explored data provenance for Host-based Intrusion Detection Systems (HIDS) as one promising data source to tackle this issue. Data provenance represents information flows between system entities as Direct Acyclic Graph (DAG) . Provenance-based Intrusion Detection Systems (PIDS) utilize data provenance to enhance the detection performance of intrusions and reduce false-alarm rates compared to traditional IDS. This survey demonstrates the potential of PIDS by providing a detailed evaluation of recent research in the field, proposing a novel taxonomy for PIDS, discussing current issues, and potential future research directions. This survey aims to help and motivate researchers to get started in the field of PIDS by tackling issues of data collection, graph summarization, intrusion detection, and developing real-world benchmark datasets.

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