This paper reviews the use of outlier detection approaches in urban traffic analysis. We
divide existing solutions into two main categories: flow outlier detection and trajectory outlier …

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

This paper looks into the problem of detecting network anomalies by analyzing NetFlow
records. While many previous works have used statistical models and machine learning …

The detection of outliers in spatio-temporal traffic data is an important research problem in
the data mining and knowledge discovery community. However to the best of our …

Statistical machine learning techniques have recently garnered increased popularity as a
means to improve network design and security. For intrusion detection, such methods build …

Intrusion detection is the identification of malicious activities in a given network by analyzing
its traffic. Data mining techniques used for this analysis study the traffic traces and identify …

Traditional Network Intrusion Detection Systems (NIDSs) rely on either specialized
signatures of previously seen attacks, or on expensive and difficult to produce labeled traffic …

Secure multiparty computation (MPC) allows joint privacy-preserving computations on data
of multiple parties. Although MPC has been studied substantially, building solutions that are …

Despite advances in measurement technology, it is still challenging to reliably compile large-
scale network datasets. For example, because of flaws in the measurement systems or …

We propose a novel two-step mining and optimization framework for inferring the root cause
of anomalies that appear in road traffic data. We model road traffic as a time-dependent flow …