With the rapid development of online social networks (OSN), maintaining the security of social media ecosystems becomes dramatically important for public. Among all the security threats in OSN, malicious social bot is the most common risk factor. This paper puts forward a detection method called BotFlowMon that only utilize NetFlow data to identify OSN bot traffic. The detection procedure takes the raw NetFlow data as input and use DBSCAN algorithm to aggregate related flows into transaction level data. Then a special data fusion technique along with a visualization method are proposed to extract features, normalize values and help analyzing flows. A new clustering algorithm called Clustering Based on Density Sort and Valley Point Competition is also designed to subdivide transactions into basic operations. After the above preprocessing steps, some classic machine learning algorithms are applied to construct the classification model. / 2020-09-06

Online social networks (OSNs) have seen a remarkable rise in the presence of automated social agents, or social bots. Social bots are the new computing viral, that are surreptitious and clever. What facilitates the creation of social agents is the massive human user-base and business-supportive operating model of social networks. These automated agents are injected by agencies, brands, individuals, and corporations to serve their work and purpose; utilising them for news and emergency communication, marketing, social activism, political campaigning, and even spam and spreading malicious content. Their influence was recently substantiated by coordinated social hacking and computational political propaganda. The thesis of my dissertation argues that automated agents exercise a profound impact on OSNs that transforms into an array of influence on our society and systems. However, latent or veiled, these agents can be successfully detected through measurement, feature extraction and finely tuned supervised learning models. The various types of automated agents can be further unravelled through unsupervised machine learning and natural language processing, to formally inform the populace of their existence and impact.

With social media rising in popularity over the recent years, new so called social bots are infiltrating by spamming and manipulating people all over the world. Many different methods have been presented to solve this problem with varying success. This study aims to compare some of these methods, on a dataset of Twitter account metadata, to provide helpful information to companies when deciding how to solve this problem. Two machine learning algorithms and a human survey will be compared on the ability to classify accounts. The algorithms used are the supervised algorithm random forest and the unsupervised algorithm k-means. There will also be an evaluation of two ways to run these algorithms, using the machine learning as a service BigML and the python library Scikit-learn. Additionally, what metadata features are most valuable in the supervised and human survey will be compared. Results show that supervised machine learning is the superior technique for social bot identification with an accuracy of almost 99%. To conclude, it depends on the expertise of the company and if a relevant training dataset is available but in most cases supervised machine learning is recommended.