CEAI: CCM based Email Authorship Identification Model

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2]

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

Authorship Authentication for Twitter Messages Using Support Vector Machine

With the rapid growth of internet usage, authorship authentication of online messages became challenging research topic in the last decades. In this paper, we used a team of support vector machines to authenticate 5 Twitter authors’ messages. SVM is one of the commonly used and strong classification algorithms in authorship attribution problems. SVM maps the linearly non separable input data to a higher dimensional space by a hyperplane via radial base functions. Firstly using the training data, 10 hyperplanes that separate pair wise five authors training data are built. Then the expertise of these SVMs combined to classify the testing data into five classes. 20 tweets with 16 features from each author were used for evaluation. In spite of the randomly choice of the features, one of the author accuracy around 75% is achieved

A robust authorship attribution on big period

Authorship attribution is a task to identify the writer of unknown text and categorize it to known writer. Writing style of each author is distinct and can be used for the discrimination. There are different parameters responsible for rectifying such changes. When the writing samples collected for an author when it belongs to small period, it can participate efficiently for identification of unknown sample. In this paper author identification problem considered where writing sample is not available on the same time period. Such evidences collected over long period of time. And character n-gram, word n-gram and pos n-gram features used to build the model. As they are contributing towards style of writer in terms of content as well as statistic characteristic of writing style. We applied support vector machine algorithm for classification. Effective results and outcome came out from the experiments. While discriminating among multiple authors, corpus selection and construction were the most tedious task which was implemented effectively. It is observed that accuracy varied on feature type. Word and character n-gram have shown good accuracy than PoS n-gram

Nondescript: A Web Tool to Aid Subversion of Authorship Attribution

A person’s writing style is uniquely quantifiable and can serve reliably as a biometric. A writer who wishes to remain anonymous can use a number of privacy technologies but can still be identified simply by the words they choose to use — how frequently they use common words like “of,” for instance. Nondescript is a web tool designed first to identify the user’s writing style in terms of word frequency from a given writing sample and document, then to suggest how the author can change their document to lessen its probability of being attributed to them. While Nondescript does not guarantee anonymity, the web tool provides a user with an iterative interface to revise their writing and see results of a simulated authorship attribution scenario. Nondescript also provides a synonym-replacement feature, which significantly lowers the probability that a document will be attributed to the original author. (Code repository: https://github.com/robincamille/nondescript

Ransomware Detection and Classification Strategies

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these cyberthreats multiply, researchers have proposed a range of ransomware detection and classification schemes. Most of these methods use advanced machine learning techniques to process and analyze real-world ransomware binaries and action sequences. Hence this paper presents a survey of this critical space and classifies existing solutions into several categories, i.e., including network-based, host-based, forensic characterization, and authorship attribution. Key facilities and tools for ransomware analysis are also presented along with open challenges.Comment: 9 pages, 2 figure

Overview of the PAN/CLEF 2015 Evaluation Lab

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24027-5_49This paper presents an overview of the PAN/CLEF evaluation lab. During the last decade, PAN has been established as the main forum of text mining research focusing on the identification of personal traits of authors left behind in texts unintentionally. PAN 2015 comprises three tasks: plagiarism detection, author identification and author profiling studying important variations of these problems. In plagiarism detection, community-driven corpus construction is introduced as a new way of developing evaluation resources with diversity. In author identification, cross-topic and cross-genre author verification (where the texts of known and unknown authorship do not match in topic and/or genre) is introduced. A new corpus was built for this challenging, yet realistic, task covering four languages. In author profiling, in addition to usual author demographics, such as gender and age, five personality traits are introduced (openness, conscientiousness, extraversion, agreeableness, and neuroticism) and a new corpus of Twitter messages covering four languages was developed. In total, 53 teams participated in all three tasks of PAN 2015 and, following the practice of previous editions, software submissions were required and evaluated within the TIRA experimentation framework.Stamatatos, E.; Potthast, M.; Rangel, F.; Rosso, P.; Stein, B. (2015). Overview of the PAN/CLEF 2015 Evaluation Lab. En Experimental IR Meets Multilinguality, Multimodality, and Interaction: 6th International Conference of the CLEF Association, CLEF'15, Toulouse, France, September 8-11, 2015, Proceedings. 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E-mail forensic authorship attribution

E-mails have become the standard for business as well as personal communication. The inherent security risks within e-mail communication present the problem of anonymity. If an author of an e-mail is not known, the digital forensic investigator needs to determine the authorship of the e-mail using a process that has not been standardised in the e-mail forensic field. This research project examines many problems associated with e-mail communication and the digital forensic domain; more specifically e-mail forensic investigations, and the recovery of legally admissible evidence to be presented in a court of law. The Research Methodology utilised a comprehensive literature review in combination with Design Science which results in the development of an artifact through intensive research. The Proposed E-Mail Forensic Methodology is based on the most current digital forensic investigation process and further validation of the process was established via expert reviews. The opinions of the digital forensic experts were an integral portion of the validation process which adds to the credibility of the study. This was performed through the aid of the Delphi technique. This Proposed E-Mail Forensic Methodology adopts a standardised investigation process applied to an e-mail investigation and takes into account the South African perspective by incorporating various checks with the laws and legislation. By following the Proposed E-mail Forensic Methodology, e-mail forensic investigators can produce evidence that is legally admissible in a court of law

AUTHOR VERIFICATION OF ELECTRONIC MESSAGING SYSTEMS

Messaging systems have become a hugely popular new paradigm for sending and delivering text messages; however, online messaging platforms have also become an ideal place for criminals due to their anonymity, ease of use and low cost. Therefore, the ability to verify the identity of individuals involved in criminal activity is becoming increasingly important. The majority of research in this area has focused on traditional authorship problems that deal with single-domain datasets and large bodies of text. Few research studies have sought to explore multi-platform author verification as a possible solution to problems around forensics and security. Therefore, this research has investigated the ability to identify individuals on messaging systems, and has applied this to the modern messaging platforms of Email, Twitter, Facebook and Text messages, using different single-domain datasets for population-based and user-based verification approaches. Through a novel technique of cross-domain research using real scenarios, the domain incompatibilities of profiles from different distributions has been assessed, based on real-life corpora using data from 50 authors who use each of the aforementioned domains. The results show that the use of linguistics is likely be similar between platforms, on average, for a population-based approach. The best corpus experimental result achieved a low EER of 7.97% for Text messages, showing the usefulness of single-domain platforms where the use of linguistics is likely be similar, such as Text messages and Emails. For the user-based approach, there is very little evidence of a strong correlation of stylometry between platforms. It has been shown that linguistic features on some individual platforms have features in common with other platforms, and lexical features play a crucial role in the similarities between users’ modern platforms. Therefore, this research shows that the ability to identify individuals on messaging platforms may provide a viable solution to problems around forensics and security, and help against a range of criminal activities, such as sending spam texts, grooming children, and encouraging violence and terrorism.Royal Embassy of Saudi Arabia, Londo