Identifying Online Sexual Predators Using Support Vector Machine

A two-stage classification model is built in the research for online sexual predator identification. The first stage identifies the suspicious conversations that have predator participants. The second stage identifies the predators in suspicious conversations. Support vector machines are used with word and character n-grams, combined with behavioural features of the authors to train the final classifier. The unbalanced dataset is downsampled to test the performance of re-balancing an unbalanced dataset. An age group classification model is also constructed to test the feasibility of extracting the age profile of the authors, which can be used as features for classifier training. The e↵ect of re-balancing the unbalanced dataset resulted in a better performance of the classifier. Testing the two-stage classification model on the unseen test set, 171 out of 254 predators are successfully identified giving a precision of 0.85, recall of 0.67 and f-score of 0.807. Comparing the classification performance with and without the behavioural feature, it can be seen the n-gram contributed the most to the performance of the classifier, while the behavioural features do not contribute significantly to the performance

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

Automatic Identification of Online Predators in Chat Logs by Anomaly Detection and Deep Learning

Providing a safe environment for juveniles and children in online social networks is considered as a major factor in improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of juvenile abuse in cyberspace has become inevitable. Using automatic ways to address this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and binary classification in machine learning. This thesis proposes two machine learning approaches to deal with the following two issues in the domain of online predator identification: 1) The first problem is gathering a comprehensive set of negative training samples which is unrealistic due to the nature of the problem. This problem is addressed by applying an existing method for semi-supervised anomaly detection that allows the training process based on only one class label. The method was tested on two datasets; 2) The second issue is improving the performance of current binary classification methods in terms of classification accuracy and F1-score. In this regard, we have customized a deep learning approach called Convolutional Neural Network to be used in this domain. Using this approach, we show that the classification performance (F1-score) is improved by almost 1.7% compared to the classification method (Support Vector Machine). Two different datasets were used in the empirical experiments: PAN-2012 and SQ (Sûreté du Québec). The former is a large public dataset that has been used extensively in the literature and the latter is a small dataset collected from the Sûreté du Québec

Online Sexual Predator Detection

Online sexual abuse is a concerning yet severely overlooked vice of modern society. With more children being on the Internet and with the ever-increasing advent of web-applications such as online chatrooms and multiplayer games, preying on vulnerable users has become more accessible for predators. In recent years, there has been work on detecting online sexual predators using Machine Learning and deep learning techniques. Such work has trained on severely imbalanced datasets, and imbalance is handled via manual trimming of over-represented labels. In this work, we propose an approach that first tackles the problem of imbalance and then improves the effectiveness of the underlying classifiers. Our evaluation of the proposed sampling approach on PAN benchmark dataset shows performance improvements on several classification metrics, compared to prior methods that otherwise require hands-crafted sampling of the data

Methodologies for the Management, Normalization and Identification of Sexual Predation of Minors in Cyber Chat Logs

Neural networks based on the Transformer architecture have shown great results in tasks such as machine translation and text generation. Our contribution provides a methodology for an AI agent capable of Sexual Predator Identification (SPI) based on the classification capabilities of models built on the Transformer architecture. Results are comparable to existing state-of-the-art methods, with a F0.5 score of 92.5% for predator identification on the PAN2012 test dataset consisting of 2,004,235 lines of text. Practical considerations require an AI agent that can evaluate large numbers of chats quickly. In that regard the Transformer based AI agent is able to evaluate over 2 million lines of text in under 6 minutes on a modestly configured workstation. An AI agent by itself does not provide a complete solution to sexual predator identification. In an effort to give practical value to an AI agent, we address the vitally important but often overlooked issues of chat management and normalization. Our contribution provides a methodology for efficiently transforming raw chats from a native format into a consistent 'normalized' format suitable for analysis. We define a methodology to the problem of managing large numbers of chats, converting/normalizing 10,000 documents in a dataset in under 3 minutes on a modestly configured workstation. We present a software-based solution that among other things brings together chat management, normalization, and AI based analysis into a cohesive, productive environment that law enforcement can use to identify and build a case against suspected predators

Visualizing Instant Messaging Author Writeprints for Forensic Analysis

As cybercrime continues to increase, new cyber forensics techniques are needed to combat the constant challenge of Internet anonymity. In instant messaging (IM) communications, criminals use virtual identities to hide their true identity, which hinders social accountability and facilitates cybercrime. Current instant messaging products are not addressing the anonymity and ease of impersonation over instant messaging. It is necessary to have IM cyber forensics techniques to assist in identifying cyber criminals as part of the criminal investigation. Instant messaging behavioral biometrics include online writing habits, which may be used to create an author writeprint to assist in identifying an author of a set of instant messages. The writeprint is a digital fingerprint that represents an author’s distinguishing stylometric features that occur in his/her computer-mediated communications. Writeprints can provide cybercrime investigators a unique tool for analyzing IMassisted cybercrimes. The analysis of IM author writeprints in this paper provides a foundation for using behavioral biometrics as a cyber forensics element of criminal investigations. This paper demonstrates a method to create and analyze behavioral biometrics-based instant messaging writeprints as cyber forensics input for cybercrime investigations. The research uses the Principal Component Analysis (PCA) statistical method to analyze IM conversation logs from two distinct data sets to visualize authorship identification. Keywords: writeprints, authorship attribution, authorship identification, principal component analysi

Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using increasingly sophisticated techniques to breach security systems and steal sensitive data. In recent years, machine learning, deep learning, and transfer learning techniques have emerged as promising tools for predicting cybercrime and preventing it before it occurs. This paper aims to provide a comprehensive survey of the latest advancements in cybercrime prediction using above mentioned techniques, highlighting the latest research related to each approach. For this purpose, we reviewed more than 150 research articles and discussed around 50 most recent and relevant research articles. We start the review by discussing some common methods used by cyber criminals and then focus on the latest machine learning techniques and deep learning techniques, such as recurrent and convolutional neural networks, which were effective in detecting anomalous behavior and identifying potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset, and then focus on active and reinforcement Learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. Overall, this paper presents a holistic view of cutting-edge developments in cybercrime prediction, shedding light on the strengths and limitations of each method and equipping researchers and practitioners with essential insights, publicly available datasets, and resources necessary to develop efficient cybercrime prediction systems.Comment: 27 Pages, 6 Figures, 4 Table

A human-centered systematic literature review of the computational approaches for online sexual risk detection

In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.Accepted manuscrip