Muni Sravanth Sankavaram

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David Sidoti

United States Naval Research Laboratory

Diego Fernando Martínez Ayala

University of Connecticut

Krishna R. Pattipati

University of Connecticut

James A. Hansen

University of Illinois Urbana-Champaign

Balakumar Balasingam

University of Windsor

Manisha Mishra

V.F. Corporation (United States)

Christopher Lintz

Comcast (United States)

Peter Willett

University of Connecticut

Jim Fahrny

Comcast (United States)

G. Commeau

Comcast (United States)

Cooperative Institutions

University of Connecticut

Comcast (United States)

United States Naval Research Laboratory

Naval Research Laboratory Marine Meteorology Division

University of Windsor

McMaster University

Institute of Electrical and Electronics Engineers

University of Illinois Urbana-Champaign

Vibrant Data (United States)

Argonne National Laboratory

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Online anomaly detection in big data

International Conference on Information Fusion (2014)

Balakumar Balasingam Muni Sravanth Sankavaram K. Choi Diego Fernando Martínez Ayala David Sidoti

In this paper, the problem of online anomaly detection in multi-attributed, asynchronous data from a large number of individual devices is considered. It has become increasingly common for many services, such as video-on-demand (VOD), to have connected customers where hundreds of millions of subscribers access a cluster of content servers for online services. It is important to monitor these transactions online, in order to ensure acceptable quality of experience to the customers as well as for detecting any abnormal or undesirable activities. Our proposed anomaly detection strategy works in two phases: First we perform intermittent anomaly detection in space, using data from the entire set of devices for a short duration in time. This phase employs principal component analysis (PCA) for data reduction and captures models of normal and abnormal features. Then, these identified models are used to monitor each subscriber's devices online in order to quickly detect any abnormalities. The proposed approach is demonstrated on Comcast's Xfinity video streaming data.

Anomaly (physics)

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Context-Aware Dynamic Asset Allocation for Maritime Interdiction Operations

IEEE Transactions on Systems Man and Cybernetics Systems (2017)

David Sidoti Krishna R. Pattipati Han Xu Lingyi Zhang Gopi Vinod Avvari

This paper validates two approximate dynamic programming approaches on a maritime interdiction problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to interdict multiple drug smugglers using heterogeneous types of transportation on the sea with varying contraband weights. The asset allocation is based on a probability of activity surface, which represents spatio-temporal target activity obtained by integrating intelligence data on drug smuggler whereabouts/waypoints for contraband transportation, behavior models, and meteorological and oceanographic information. We validate the proposed architectural and algorithmic concepts via several realistic mission scenarios. We conduct sensitivity analyses to quantify the robustness and proactivity of our approach, as well as to measure the value of information used in the allocation process. The contributions of this paper have been transitioned to and are currently being tested by Joint Interagency Task Force-South, an organization tasked with providing the initial line of defense against drug trafficking in the East Pacific and Caribbean Oceans.

Interdiction

Robustness

10.1109/tsmc.2017.2767568

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Citations (7)