Tech note
TN22/04: Cyber Threat Intelligence (Student thesis paper)

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.

The EXPLAIN Project

Although there has been a great deal of work in the use of AI technologies across the law enforcement sector, […]

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Image Localisation by Content

One of the challenges in image processing in a law enforcement context is that of understanding spatio-temporal context. This problem […]

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Law Enforcement Data Interoperability

Systemic interoperability within and between law enforcement agencies is vital to address the large scale technical challenges inherent in combatting […]

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Project Metior Telum

Project Metior Telum (“measure the weapon“) is a large scale AiLECS initiative to build a highly accurate automatic firearm detection system. A […]

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The VALID Project

The VALID (Veracity, Agency, Longevity and Integrity in Datasets) project is actively working on frameworks to improve data quality, ethical accountability, and public […]

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Funding boost for the AiLECS Lab

The Minister for Home Affairs Karen Andrews announced on March 22, 2022 that $4.4 million from the Proceeds of Crime […]

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Welcome Dr Rubina Sarki

Dr Rubina Sarki has joined the AiLECS lab to work on detection and prevention of deepfakes, as part of an […]

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New Technical Report

A new technical report, The Data Airlock is available in the resource centre.

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Transfer Learning for CSAM Classification

A common approach to automatically identifying child sexual abuse material (CSAM), is that of dividing the whole task into several […]

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Perceptual Hashing

Effective automatic detection of Child Sexual Abuse Material (CSAM) is a continuing challenge, including the rapid detection of previously seen […]

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Countering online child exploitation

Scope The Australian Centre to Counter Child Exploitation received in 2020 alone, over 21,000 reports of the online child sexual […]

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