most citedHandling Adversarial Concept Drift in Streaming Data

75 citations · 138 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2018★ 6 cited

Security Theater: On the Vulnerability of Classifiers to Exploratory Attacks

Tegjyot Singh Sethi, Mehmed Kantardzic, Joung Woo Ryu

The increasing scale and sophistication of cyberattacks has led to the adoption of machine learning based classification techniques, at the core of cybersecurity systems. These tec…

cs.LG2018★ 10 cited

A Dynamic-Adversarial Mining Approach to the Security of Machine Learning

Tegjyot Singh Sethi, Mehmed Kantardzic, Lingyu Lyua +1

Operating in a dynamic real world environment requires a forward thinking and adversarial aware design for classifiers, beyond fitting the model to the training data. In such scena…

cs.LG2018★ 75 cited

Handling Adversarial Concept Drift in Streaming Data

Tegjyot Singh Sethi, Mehmed Kantardzic

Classifiers operating in a dynamic, real world environment, are vulnerable to adversarial activity, which causes the data distribution to change over time. These changes are tradit…

stat.ML2017

On the Reliable Detection of Concept Drift from Streaming Unlabeled Data

Tegjyot Singh Sethi, Mehmed Kantardzic

Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause th…

stat.ML2017★ 47 cited

Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains

Tegjyot Singh Sethi, Mehmed Kantardzic

While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shap…