Machine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. Recent research focuses on the functionality metrics of ML techniques, depicting their prediction effectiveness, but overlooked their operational requirements. ML techniques are resource-demanding that require careful adaptation to fit the limited computing resources of a large sector of their operational platform, namely, embedded systems. In this paper, we propose cloud-based service architecture for managing ML models that best fit different IoT device operational configurations for security. An IoT device may benefit from such a service by offloading to the cloud heavy-weight activities such as feature selection, model building, training, and validation, thus reducing its IDS maintenance workload at the IoT device and get the security model back from the cloud as a service.

No abstract

No abstract

This study has carried out a systematic literature review to examine the metrics that have been applied in the prevailing literature to operationalise or quantify the effectiveness of internal auditing, as well as to determine the factors that are thought to have impact on the influence of the internal auditing. With predefined exclusion and inclusion criteria, this research has finally selected a total of 33 primary studies that were published between 2000 and 2019. This study has identified a total of eleven indicators will used to measure the effectiveness of auditing. These indicators have further grouped into two categories: objectively assessed effectiveness; and perceived effectiveness. The indicators in the perceived group have dominance in the prevailing literature, and therefore this study argues that the indicators used for measuring the effectiveness in objective way generally demonstrates the strides assumed by the internal auditors. Additionally, this study has identified a total of twenty factors that have been considered as the influencing factors in terms of the influence of internal audit. These twenty factors have been further grouped into two categories: factors on supply side and factors on demand side.