Abstract is missing.
- Machine learning meets social networking security: detecting and analyzing malicious social networks for fun and profitGuofei Gu. 1-2 [doi]
- Improving malware classification: bridging the static/dynamic gapBlake Anderson, Curtis Storlie, Terran Lane. 3-14 [doi]
- Early detection of malicious behavior in JavaScript codeKristof Schütt, Marius Kloft, Alexander Bikadorov, Konrad Rieck. 15-24 [doi]
- An information theoretic framework for web inference detectionHoi Le Thi, Reihaneh Safavi-Naini. 25-36 [doi]
- Learning stateful models for network honeypotsTammo Krueger, Hugo Gascon, Nicole Krämer, Konrad Rieck. 37-48 [doi]
- Nonparametric semi-supervised learning for network intrusion detection: combining performance improvements with realistic in-situ trainingChristopher T. Symons, Justin M. Beaver. 49-58 [doi]
- Robust detection of comment spam using entropy rateAlex Kantchelian, Justin Ma, Ling Huang, Sadia Afroz, Anthony D. Joseph, J. D. Tygar. 59-70 [doi]
- Understanding the time-series behavioral characteristics of evolutionally advanced email spammersYukiko Sawaya, Ayumu Kubota, Akira Yamada. 71-80 [doi]
- Tracking concept drift in malware familiesAnshuman Singh, Andrew Walenstein, Arun Lakhotia. 81-92 [doi]
- Autonomous learning for detection of JavaScript attacks: vision or reality?Guido Schwenk, Alexander Bikadorov, Tammo Krueger, Konrad Rieck. 93-104 [doi]
- On the effectiveness of using state-of-the-art machine learning techniques to launch cryptographic distinguishing attacksJung-Wei Chou, Shou-de Lin, Chen-Mou Cheng. 105-110 [doi]