${session.getAttribute("locale")} 5 Automatically determining phishing campaigns using the USCAP methodology Wed 31 Jul 2019 16:55:32 AEST ]]> Ethical considerations when using online datasets for research purposes Wed 27 Mar 2019 11:09:50 AEDT ]]> Automated unsupervised authorship analysis using evidence accumulation clustering Wed 24 Jul 2019 16:17:51 AEST ]]> Unsupervised authorship analysis of phishing webpages Wed 24 Jul 2019 15:14:21 AEST ]]> Recentred local profiles for authorship attribution Wed 24 Jul 2019 15:11:45 AEST ]]> Local n-grams for author identification: Notebook for PAN at CLEF 2013 C3 - CEUR Workshop Proceedings Wed 24 Jan 2018 10:35:52 AEDT ]]> Improving authorship attribution in twitter through topic-based sampling Wed 20 Sep 2017 15:12:13 AEST ]]> Automating Open Source Intelligence: Algorithms for OSINT Wed 17 Jan 2018 12:55:57 AEDT ]]> Be careful who you trust: Issues with the public key infrastructure Wed 10 Jul 2019 14:22:18 AEST ]]> Evaluating authorship distance methods using the positive Silhouette coefficient Wed 08 Nov 2017 07:19:35 AEDT ]]> Authorship attribution for Twitter in 140 characters or less th century considered it difficult to determine the authorship of a document of fewer than 1000 words. By the 1990s this values had decreased to less than 500 words and in the early 21 st century it was considered possible to determine the authorship of a document in 250 words. The need for this ever decreasing limit is exemplified by the trend towards many shorter communications rather than fewer longer communications, such as the move from traditional multi-page handwritten letters to shorter, more focused emails. This trend has also been shown in online crime, where many attacks such as phishing or bullying are performed using very concise language. Cybercrime messages have long been hosted on Internet Relay Chats (IRCs) which have allowed members to hide behind screen names and connect anonymously. More recently, Twitter and other short message based web services have been used as a hosting ground for online crimes. This paper presents some evaluations of current techniques and identifies some new preprocessing methods that can be used to enable authorship to be determined at rates significantly better than chance for documents of 140 characters or less, a format popularised by the micro-blogging website Twitter1. We show that the SCAP methodology performs extremely well on twitter messages and even with restrictions on the types of information allowed, such as the recipient of directed messages, still perform significantly higher than chance. Further to this, we show that 120 tweets per user is an important threshold, at which point adding more tweets per user gives a small but non-significant increase in accuracy. © 2010 IEEE.]]> Wed 08 Nov 2017 07:13:28 AEDT ]]> REPLOT : REtrieving Profile Links on Twitter for malicious campaign discovery Wed 08 Nov 2017 06:55:43 AEDT ]]> The seven scam types: Mapping the terrain of cybercrime Wed 08 Nov 2017 06:42:11 AEDT ]]> Authorship analysis of the zeus botnet source code Tue 16 Jan 2018 14:47:23 AEDT ]]> How much material on BitTorrent is infringing content? A case study Thu 28 Mar 2019 13:44:07 AEDT ]]> Authorship attribution of IRC messages using inverse author frequency Thu 25 Jul 2019 10:25:35 AEST ]]> Towards an implementation of information flow security using semantic web technologies Thu 25 Jul 2019 10:23:48 AEST ]]> Identifying cyber predators through forensic authorship analysis of chat logs Thu 25 Jul 2019 10:05:14 AEST ]]> Characterising network traffic for Skype forensics Thu 25 Jul 2019 09:31:15 AEST ]]> Identifying Faked Hotel Reviews Using Authorship Analysis Thu 21 Mar 2019 14:08:00 AEDT ]]> Malicious Spam Emails Developments and Authorship Attribution Thu 21 Mar 2019 14:06:06 AEDT ]]> Optimization based clustering algorithms for authorship analysis of phishing emails Thu 18 Oct 2018 11:03:24 AEDT ]]> The role of love stories in Romance Scams : A qualitative analysis of fraudulent profiles Mon 29 Jan 2018 14:15:59 AEDT ]]> Fake file detection in P2P networks by consensus and reputation Mon 25 Mar 2019 11:19:14 AEDT ]]> Application of SVM in citation information extraction Mon 25 Mar 2019 11:17:27 AEDT ]]> REPLOT: REtrieving profile links on Twitter for suspicious networks detection Mon 16 Jan 2017 21:16:35 AEDT ]]> Mining malware to detect variants Mon 16 Jan 2017 20:41:40 AEDT ]]> API design for machine learning software: experiences from the scikit-learn project Mon 16 Jan 2017 19:58:49 AEDT ]]> Crime toolkits: The productisation of cybercrime Mon 16 Jan 2017 17:45:48 AEDT ]]> Indirect information linkage for OSINT through authorship analysis of aliases Mon 16 Jan 2017 15:35:23 AEDT ]]> Malware detection based on structural and behavioural features of API calls Mon 16 Jan 2017 13:54:51 AEDT ]]> ICANN or ICANT: Is WHOIS an Enabler of Cybercrime? Mon 16 Jan 2017 13:41:35 AEDT ]]> Skype Traffic Classification Using Cost Sensitive Algorithms Mon 16 Jan 2017 11:44:18 AEDT ]]> Relative cyberattack Attribution Mon 12 Feb 2018 11:07:54 AEDT ]]> Using stereotypes to improve early-match poker play Fri 26 Oct 2018 15:27:04 AEDT ]]> Characterising and predicting cyber attacks using the Cyber Attacker Model Profile (CAMP) Fri 26 Jul 2019 13:49:07 AEST ]]> Using differencing to increase distinctiveness for phishing website clustering Fri 26 Apr 2019 15:08:40 AEST ]]> Determining provenance in phishing websites using automated conceptual analysis Fri 26 Apr 2019 12:30:24 AEST ]]> Authorship analysis of aliases: Does topic influence accuracy? Fri 15 Mar 2019 15:49:15 AEDT ]]>