The IEEE Special Issue on Data Mining for Cybersecurity (IEEE Intelligent Systems, Vol. 33, Issue No. 02, March/April 2018) co-edited by Internet Governance Lab Faculty Fellow Dr. Nathalie Japkowicz has now been published. Dr. Japkowicz, Professor in the Department of Computer Science at American University, co-edited the volume with Dr. Yuval Elovici, Professor in Software and Information Systems Engineering and Director of the Deutsche Telekom Laboratories at Ben-Gurion University in Israel.
According to the IEEE website, "The articles presented in this special issue are quite representative of the field of data mining applied to cybersecurity—both in terms of the tasks and domains that they consider and in terms of the solutions that they propose...The articles published in this volume provide both a comprehensive introduction to the types of issues encountered in the field and present sophisticated solution to tackle them."
The Special Issue includes the following articles:
“Adaptive Biometric Systems using Ensembles” by Pisani, Lorena, and de Carvalho.
“Identifying SCADA Systems and Their Vulnerabilities on the Internet of Things: A Text-Mining Approach” by Samtani, Yu, Zhu, Patton, Matherly, and Chen.
“Predicting Adversarial Cyber Intrusion Stages Using Autoregressive Neural Networks” by Rege, Obradovic, Asadi, Parker, Pandit, Masceri, and Singer.
“Real-time Dynamic Network Anomaly Detection” by Noble and Adams.
“Transfer Learning for User Action Identification in Mobile Apps via Encrypted Traffic Analysis” by Grolman, Finkelstein, Puzis, Shabtai, Celniker, Katzir, and Rosenfeld.
- “Probing the Limits of Anomaly Detectors for Automobiles with a Cyberattack Framework” by Taylor, Leblanc, and Japkowicz.