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Fake Account Detection in Social Networks with Machine Learning: Difference between revisions
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Fake Account Detection in Social Networks with Machine Learning (view source)
Revision as of 11:22, 4 May 2017
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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). | 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 | References: | ||
* [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 | * [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 | |||
[3] Q. Cao et al: 'Combating Friend Spam Using Social Rejections', in Proceedings of IEEE ICDCS 2015 | |||