Sarah M. Erfani

Learn More
Keywords: Anomaly detection Outlier detection High-dimensional data Deep belief net Deep learning One-class SVM Feature extraction a b s t r a c t High-dimensional problem domains pose significant challenges for anomaly detection. The presence of irrelevant features can conceal the presence of anomalies. This problem, known as the 'curse of(More)
The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning(More)
In collaborative anomaly detection, multiple data sources submit their data to an on-line service, in order to detect anomalies with respect to the wider population. A major challenge is how to achieve reasonable detection accuracy without disclosing the actual values of the participants' data. We propose a lightweight and scalable privacy-preserving(More)
Participatory sensing using mobile devices is emerging as a promising method for large-scale data sampling. A critical challenge for participatory sensing is how to preserve the privacy of individual contributors' data. In addition, the integrity of the data aggregation is vital to ensure the acceptance of the participating sensing model by the(More)
An important challenge in network management and intrusion detection is the problem of data stream classification to identify new and abnormal traffic flows. An open research issue in this context is concept-evolution, which involves the emergence of a new class in the data stream. Most traditional data classification techniques are based on the assumption(More)
The ubiquity of mobile sensing devices in the Internet of Things (IoT) enables an emerging data crowdsourcing paradigm called participatory sensing, where multiple individuals collect data and use a cloud service to analyse the union of the collected data. An example of such collaborative analysis is collaborative anomaly detection. Given the possibility(More)