Abstract is missing.
- Off the beaten path: machine learning for offensive securityKonrad Rieck. 1-2 [doi]
- Using naive bayes to detect spammy names in social networksDavid Mandell Freeman. 3-12 [doi]
- What you want is not what you get: predicting sharing policies for text-based content on facebookArunesh Sinha, Yan Li, Lujo Bauer. 13-24 [doi]
- GOTCHA password hackers!Jeremiah Blocki, Manuel Blum, Anupam Datta. 25-34 [doi]
- Early security classification of skype users via machine learningAnna Leontjeva, Moisés Goldszmidt, Yinglian Xie, Fang Yu, Martín Abadi. 35-44 [doi]
- Structural detection of android malware using embedded call graphsHugo Gascon, Fabian Yamaguchi, Daniel Arp, Konrad Rieck. 45-54 [doi]
- ACTIDS: an active strategy for detecting and localizing network attacksEitan Menahem, Yuval Elovici, Nir Amar, Gabi Nakibly. 55-66 [doi]
- n-grams in intrusion detection: anomaly detection vs. classificationChristian Wressnegger, Guido Schwenk, Daniel Arp, Konrad Rieck. 67-76 [doi]
- On the hardness of evading combinations of linear classifiersDavid Stevens, Daniel Lowd. 77-86 [doi]
- Is data clustering in adversarial settings secure?Battista Biggio, Ignazio Pillai, Samuel Rota Bulò, Davide Ariu, Marcello Pelillo, Fabio Roli. 87-98 [doi]
- Approaches to adversarial driftAlex Kantchelian, Sadia Afroz, Ling Huang, Aylin Caliskan Islam, Brad Miller, Michael Carl Tschantz, Rachel Greenstadt, Anthony D. Joseph, J. D. Tygar. 99-110 [doi]