63 citations · 135 across the 11 of their papers we have counts for
14 papers
BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems
Andy K. Zhang, Joey Ji, Celeste Menders +31
AI agents have the potential to significantly alter the cybersecurity landscape. Here, we introduce the first framework to capture offensive and defensive cyber-capabilities in evo…
Streaming Anomaly Detection
Siddharth Bhatia
Anomaly detection is critical for finding suspicious behavior in innumerable systems. We need to detect anomalies in real-time, i.e. determine if an incoming entity is anomalous or…
MonLAD: Money Laundering Agents Detection in Transaction Streams
Xiaobing Sun, Wenjie Feng, Shenghua Liu +5
Given a stream of money transactions between accounts in a bank, how can we accurately detect money laundering agent accounts and suspected behaviors in real-time? Money laundering…
SSMF: Shifting Seasonal Matrix Factorization
Koki Kawabata, Siddharth Bhatia, Rui Liu +2
Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands? In general, given a data stream of events with seasonal pa…
GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs
Siddharth Bhatia, Yiwei Wang, Bryan Hooi +1
Finding anomalous snapshots from a graph has garnered huge attention recently. Existing studies address the problem using shallow learning mechanisms such as subspace selection, eg…
Sketch-Based Anomaly Detection in Streaming Graphs
Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi +3
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, usin…
MemStream: Memory-Based Streaming Anomaly Detection
Siddharth Bhatia, Arjit Jain, Shivin Srivastava +2
Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities? Most of the existing unsupervised an…
Isconna: Streaming Anomaly Detection with Frequency and Patterns
Rui Liu, Siddharth Bhatia, Bryan Hooi
An edge stream is a common form of presentation of dynamic networks. It can evolve with time, with new types of nodes or edges being continuously added. Existing methods for anomal…
Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data
Shivin Srivastava, Siddharth Bhatia, Lingxiao Huang +3
In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier. Previous methods for s…
AugSplicing: Synchronized Behavior Detection in Streaming Tensors
Jiabao Zhang, Shenghua Liu, Wenting Hou +4
How can we track synchronized behavior in a stream of time-stamped tuples, such as mobile devices installing and uninstalling applications in the lockstep, to boost their ranks in…
ExGAN: Adversarial Generation of Extreme Samples
Siddharth Bhatia, Arjit Jain, Bryan Hooi
Mitigating the risk arising from extreme events is a fundamental goal with many applications, such as the modelling of natural disasters, financial crashes, epidemics, and many oth…
Real-Time Anomaly Detection in Edge Streams
Siddharth Bhatia, Rui Liu, Bryan Hooi +3
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant tim…
MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams
Siddharth Bhatia, Arjit Jain, Pan Li +2
Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner? For example, in…
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
Siddharth Bhatia, Bryan Hooi, Minji Yoon +2
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant tim…