Fake Account Detection in Social Networks with Machine Learning: Difference between revisions
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(Created page with "== Details == {{Project Description |supervisor=[http://user.informatik.uni-goettingen.de/~dkoll] |duration=3-6 months |type=Master's Thesis (plus Student Project if required) |s...") |
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== Details == | == Details == | ||
{{Project Description | {{Project Description | ||
|supervisor=[http://user.informatik.uni-goettingen.de/~dkoll] | |supervisor=[David Koll http://user.informatik.uni-goettingen.de/~dkoll] | ||
|duration=3-6 months | |duration=3-6 months | ||
|type=Master's Thesis (plus Student Project if required) | |type=Master's Thesis (plus Student Project if required) | ||
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The scope of this thesis is two-fold: In a first step, we aim at breaking state-of-the-art solutions in Sybil defenses, and in a second step will target an improvement of their detection capability based on the lessons learned in the first step. | The scope of this thesis is two-fold: In a first step, we aim at breaking state-of-the-art solutions in Sybil defenses, and in a second step will target an improvement of their detection capability based on the lessons learned in the first step. | ||
A student taking up this topic should have good knowledge in JAVA or Python (on a level that is sufficient to efficiently implement research like the approach proposed in [3]), basic understanding of classification techniques in machine learning (e.g., Logistic Regression, RandomForest, etc.), evaluation metrics in machine learning (e.g., AUC, False Positives, False Negatives, Precision/Recall) and, most importantly, the ability to critical thinking when faced with research papers (to identify potential attack vectors and weaknesses of the state-of-the-art). The thesis work is likely to result in a peer-reviewed publication. | A student taking up this topic should have good knowledge in JAVA or Python (on a level that is sufficient to efficiently implement research like the approach proposed in [3]), basic understanding of classification techniques in machine learning (e.g., Logistic Regression, RandomForest, etc.), evaluation metrics in machine learning (e.g., AUC, False Positives, False Negatives, Precision/Recall) and, most importantly, the ability to critical thinking when faced with research papers (to identify potential attack vectors and weaknesses of the state-of-the-art). The thesis work is likely to result in a peer-reviewed publication, and will be carried out in collaboration with the University of Oregon (USA). | ||
[1] David Koll, Martin Schwarzmaier, Jun Li, XiangYang Li and Xiaoming Fu: 'Thank You For Being A Friend: An Attacker View on Online-Social-Network-based Sybil Defenses', in Proceedings of IEEE ICDCS 2017 (Workshops): The 9th International Workshop on Hot Topics in Planet-Scale Mobile Computing and Online Social Networking (HotPOST'17), Atlanta, USA, June 2017 | |||
[2] David Koll, Joshua Stein, Jun Li, and Xiaoming Fu: 'On the State of OSN-based Sybil Defenses', in Proceedings of IEEE/IFIP Networking 2014, Trondheim, Norway, June 2014 | |||
[3] Q. Cao et al: 'Combating Friend Spam Using Social Rejections', in Proceedings of IEEE ICDCS 2015 | |||