Bot Detection in Social Networks Based on Multilayered Deep Learning Approach

With the swift rise of social networking sites, they have now come to hold tremendous influence in the daily lives of millions around the globe. The value of one’s social media profile and its reach has soared highly. This has invited the use of fake accounts, spammers and bots to spread content favourable to those who control them. Thus, in this project we propose using a machine learning approach to identify bots and distinguish them from genuine users. This is achieved by compiling activity and profile information of users on Twitter and subsequently using natural language processing and supervised machine learning to achieve the objective classification. Finally, we compare and analyse the efficiency and accuracy of different learning models in order to ascertain the best performing bot detection system

Cyberbullying: Its Social and Psychological Harms Among Schoolers

Criminal justice around the world has prioritized the prevention and protection of bullying and its victims due to the rapid increases in peer violence. Nevertheless, relatively few studies have examined what treatments or assistance are effective for peer victims to reduce and recover from their social and psychological suffering, especially in cyberbullying cases. Using data derived from the National Crime Victimization Survey-School Crime Supplement data in 2011 and 2013 (N=823), the current study examined the impact of two emotional support groups (i.e., adult and peer groups) on cyberbullying victims\u27 social and psychological harm. The findings indicated that both adult and peer support reduced social and psychological harm inflicted by cyberbullying victimization. Based on these findings, the study recommends developing or modifying existing adult and peer support groups to minimize victims\u27 social and psychological distress

Detecting Malicious Usage of Online Social Network Application Programming Interfaces from Network Flows

While online social networks (OSNs) provide application programming interfaces (APIs) to enable the development of OSN applications, some of these applications, unfortunately, can be malicious. They can be running on the devices for OSN users throughout the Internet, causing security, privacy, and liability concerns to the network service providers of these OSN users. In this thesis, we study how a network service provider may inspect its network traffic to detect network flows from malicious API-based OSN applications. In particular, we devise a deep learning based methodology to detect network flows generated by malicious API-based OSN applications. We implement this methodology on a testbed, and show that our solution is effective and can accurately label 97.6% network flows from the malicious OSN applications, with only 1.6% false positives

Enhancing data privacy and security related process through machine learning

In this thesis, we exploit the advantages of Machine learning (ML) in the domains of data security and data privacy. ML is one of the most exciting technologies being developed in the world today. The major advantages of ML technology are its prediction capability and its ability to reduce the need for human activities to perform tasks. These benefits motivated us to exploit ML to improve users' data privacy and security. Firstly, we use ML technology to try to predict the best privacy settings for users, since ML has a strong prediction ability and the average user might find it difficult to properly set up privacy settings due to a lack of knowledge and subsequent lack of decision-making abilities regarding the privacy of their data. Besides, since the ML approach has the potential to considerably cut down on manual efforts by humans, our second task in this thesis is to exploit ML technology to redesign security mechanisms of social media environments that rely on human participation for providing such services. In particular, we use ML to train spam filters for identifying and removing violent, insulting, aggressive, and harassing content creators (a.k.a. spammers) from a social media platform. It helps to solve violent and aggressive issues that have been growing on social media environments. The experimental results show that our proposals are efficient and effective

Effective Features and Machine Learning Methods for Document Classification

Document classification has been involved in a variety of applications, such as phishing and fraud detection, news categorisation, and information retrieval. This thesis aims to provide novel solutions to several important problems presented by document classification. First, an improved Principal Components Analysis (PCA), based on similarity and correlation criteria instead of covariance, is proposed, which aims to capture low-dimensional feature subset that facilitates improved performance in text classification. The experimental results have demonstrated the advantages and usefulness of the proposed method for text classification in high-dimensional feature space in terms of the number of features required to achieve the best classification accuracy. Second, two hybrid feature-subset selection methods are proposed based on the combination (via either union or intersection) of the results of both supervised (in one method) and unsupervised (in the other method) filter approaches prior to the use of a wrapper, leading to low-dimensional feature subset that can achieve both high classification accuracy and good interpretability, and spend less processing time than most current methods. The experimental results have demonstrated the effectiveness of the proposed methods for feature subset selection in high-dimensional feature space in terms of the number of selected features and the processing time spent to achieve the best classification accuracy. Third, a class-specific (supervised) pre-trained approach based on a sparse autoencoder is proposed for acquiring low-dimensional interesting structure of relevant features, which can be used for high-performance document classification. The experimental results have demonstrated the merit of this proposed method for document classification in high-dimensional feature space, in terms of the limited number of features required to achieve good classification accuracy. Finally, deep classifier structures associated with a stacked autoencoder (SAE) for higher-level feature extraction are investigated, aiming to overcome the difficulties experienced in training deep neural networks with limited training data in high-dimensional feature space, such as overfitting and vanishing/exploding gradients. This investigation has resulted in a three-stage learning algorithm for training deep neural networks. In comparison with support vector machines (SVMs) combined with SAE and Deep Multilayer Perceptron (DMLP) with random weight initialisation, the experimental results have shown the advantages and effectiveness of the proposed three-stage learning algorithm