Better Features Sets for Authorship Attribution of Short Messages

Authorship authentication analysis can help to display information about the writers of messages by analyzing the writing styles. Previous researches in the authorship authentication were showed that generally people have their unique stylistic discriminators and characteristics, just like their fingerprints or signature. In this concept, researchers are developing different analysis features and techniques and have gained remarkable results in the authorship identification research field. Authorship authentication of online messages became an outstanding research topic in the last decades because of internet usage growth. One of the problems of authorship authentication analysis regarding online sources is short messages usage. Author identification techniques are started to be applied to short and informal texts in last decade and get very significant results. Authorship authentication is one of the security concerns in social network and in this research we will study how to authenticate a user by the writing style in a short text posted on Twitter. The effects of different feature sets and sample sizes are evaluated in the research

Authorship Authentication of Short Messages from Social Networks Machines

Dataset consists of 17000 tweets collected from Twitter, as 500 tweets for each of 34 authors that meet certain criteria. Raw data is collected by using the software Nvivo. The collected raw data is preprocessed to extract frequencies of 200 features. In the data analysis 128 of features are eliminated since they are rare in tweets. As a progressive presentation, five – fifteen – twenty – twenty five – thirty and thirty four of these authors are selected each time. Since recurrent artificial neural networks are more stable and in general ANNs are more successful distinguishing two classes, for N authors, N×N neural networks are trained for pair wise classification. These experts then organized in N competing teams (CANNT) to aggregate decisions of these NXN experts. Then this procedure is repeated seven times and committees with seven members voted for final decision. By a commonest type voting, the accuracy is boosted around ten percent. Number of authors is seen not so effective on the accuracy of the authentication, and around 80% accuracy is achieved for any number of authors

A framework for securing email entrances and mitigating phishing impersonation attacks

Emails are used every day for communication, and many countries and organisations mostly use email for official communications. It is highly valued and recognised for confidential conversations and transactions in day-to-day business. The Often use of this channel and the quality of information it carries attracted cyber attackers to it. There are many existing techniques to mitigate attacks on email, however, the systems are more focused on email content and behaviour and not securing entrances to email boxes, composition, and settings. This work intends to protect users' email composition and settings to prevent attackers from using an account when it gets hacked or hijacked and stop them from setting forwarding on the victim's email account to a different account which automatically stops the user from receiving emails. A secure code is applied to the composition send button to curtail insider impersonation attack. Also, to secure open applications on public and private devices