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In law enforcement (LE), interoperability, i.e., the ability to exchange information between databases and systems, enhances the ability of agencies to detect and investigate crime. A fundamental way of improving interoperability is data integration, but integrating LE databases is often difficult due to heterogeneity of database types and the semantics of the data. In this study, an ontology-based and Linked Data approach for integrating heterogeneous LE databases is proposed.

The approach is evaluated for use in an operational setting by LE data domain experts. The evaluation feedback indicates that the approach has the potential to address some of the common challenges faced when integrating heterogeneous LE databases, and could provide benefit if used in an LE agency’s operational systems.

In this paper, I propose and explore a method for image location classification. Most existing works concentrate on outdoor scenes as scenery or an iconic landmark make it easier to point out the location. Few researchers have addressed the issue of indoor scenes. Although indoor images increase the difficulty of tracking geolocation, it is necessary to respond to this shortcoming as many crimes happen indoors.

To address this problem, I propose a method for indoor image location classification by segmenting patterns of extracted objects from images. Specifically, I extract objects from images. Then, based on the accuracy levels of the bounding boxes of specific kinds of objects in the image, I only crop that kind of objects from original images. Moreover, I segment patterns from the extracted objects and crop those patterns by thresholding techniques. To classify images by these segmented patterns, I employ convolutional neural networks. Experimental results in the dataset of hotel rooms across the globe show promising accuracies, which witnesses that my method contributes to ultimately identifying the hotel chain which the image belongs to from the hotel dataset.

Cyber Threat Intelligence (CTI) sharing is a way security professionals and threat analysts can freely access and share information to tackle emerging cyber threats. CTI information can be found in various textual sources such as threat reports, blog posts and online forums, however, there is an increasing centre of attention towards automatic extraction and information retrieval of CTI knowledge. In this study, we evaluate existing ontologies that have worked towards automatic CTI extraction, then we investigate the mechanisms used to extract CTI information automatically. Our contribution is in constructing a pipeline used to develop a training dataset from disparate data sources that can predict tactics and techniques based from the MITRE ATT&CK framework.