${session.getAttribute("locale")} 5 Unsupervised authorship analysis of phishing webpages Wed 24 Jul 2019 15:14:21 AEST ]]> A multi-tier ensemble construction of classifiers for phishing email detection and filtering Wed 24 Jul 2019 15:08:30 AEST ]]> Profiling phishing activity based on hyperlinks extracted from phishing emails Wed 10 Jul 2019 12:42:49 AEST ]]> Consensus clustering and supervised classification for profiling phishing emails in internet commerce security Wed 08 Nov 2017 07:16:55 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 ]]> Automatically generating classifier for phishing email prediction Wed 01 May 2019 16:55:03 AEST ]]> A survey on latest botnet attack and defense Mon 25 Mar 2019 10:19:24 AEDT ]]> Cybercrime attribution : An Eastern European case study Mon 16 Jan 2017 15:34:53 AEDT ]]> ICANN or ICANT: Is WHOIS an Enabler of Cybercrime? Mon 13 Jan 2020 10:54:18 AEDT ]]> 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 ]]> Why do users trust the wrong messages? A behavioural model of phishing Fri 26 Apr 2019 12:28:40 AEST ]]>