Data integrity becomes paramount as the number of Internet of Things (ioT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanisms that detect drifts and irregularities can prevent disruptions and data bias in the state of an IoT application. This paper presents LE3D, an ensemble framework of data drift estimators capable of detecting abnormal sensor behaviours. Working collaboratively with surrounding ioT devices, the type of drift (natural/abnormal) can also be identified and reported to the end-user. The proposed framework is a lightweight and unsupervised implementation able to run on resource-constrained IoT devices. Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration. We compare our method against state-of-the-art ensemble data drift detection frameworks, evaluating both the real-world detection accuracy as well as the resource utilisation of the implementation. Experimenting with real-world data and emulated drifts, we show the effectiveness of our method, which achieves up to 97% of detection accuracy while requiring minimal resources to run.

Edge computing is rapidly changing the IoT-Cloud landscape. Various testbeds are now able to run multiple Docker-like containers developed and deployed by end-users on edge devices. However, this capability may allow an attacker to deploy a malicious container on the host and compromise it. This paper presents a dataset based on the Linux Auditing System, which contains malicious and benign container activity. We developed two malicious scenarios, a denial of service and a privilege escalation attack, where an adversary uses a container to compromise the edge device. Furthermore, we deployed benign user containers to run in parallel with the malicious containers. Container activity can be captured through the host system via system calls. Our time series auditd dataset contains partial labels for the benign and malicious related system calls. Generating the dataset is largely automated using a provided AutoCES framework. We also present a semi-supervised machine learning use case with the collected data to demonstrate its utility. The dataset and framework code are open-source and publicly available.

The evaluation of fluorescent in situ hybridization (FISH) images is one of the most widely used methods to determine Her-2/neu status of breast samples, a valuable prognostic indicator. Conventional evaluation is a difficult task since it involves manual counting of dots in multiple images. In this paper, we present a multistage algorithm for the automated classification of FISH images from breast carcinomas. The algorithm focuses not only on the detection of FISH dots per image, but also on combining results from multiple images taken from a slice for overall case classification. The algorithm includes mainly two stages for nuclei and dot detection respectively. The dot segmentation consists of a top-hat filtering stage followed by template matching to separate real signals from noise. Nuclei segmentation includes a nonlinearity correction step, global thresholding to identify candidate regions, and a geometric rule to distinguish between holes within a nucleus and holes between nuclei. Finally, the marked watershed transform is used to segment cell nuclei with markers detected as regional maxima of the distance transform. Combining the two stages allows the measurement of FISH signals ratio per cell nucleus and the collective classification of cases as positive or negative. The system was evaluated with receiver operating characteristic analysis and the results were encouraging for the further development of this method.