KEYWORDS: Analytics, Reconnaissance, Sensors, Surveillance, Defense and security, Control systems, Machine learning, Data processing, Information science
Ongoing research with The International Technology Alliance in Distributed Analytics and Information Sciences (DAIS-ITA) aims to enable secure, dynamic, semantically-aware, distributed analytics for deriving situational understanding in future coalitions. This paper sets out an example military scenario and operations in a future time frame, to capture the expected battlespace context and key future challenges. Key considerations involve complex multi-actor situations, high complexity information, high tempo processing, all within human-machine hybrid-teams. The coalition composition of these teams is critical and all resources will be constrained. A phased operation is proposed across rural and urban operation involving a range of ISR sensors and autonomous devices. All these are subject to enemy action and perturbation and must be used across a highly contested and congested electromagnetic spectrum. Agile command and control is required across the coalition with information arriving from multiple sources and partners that may also be utilised for learning.
Behavioral Analytics (BA) relies on digital breadcrumbs to build user profiles and create clusters of entities that exhibit a large degree of similarity. The prevailing assumption is that an entity will assimilate the group behavior of the cluster it belongs to. Our understanding of BA and its application in different domains continues to evolve and is a direct result of the growing interest in Machine Learning research. When trying to detect security threats, we use BA techniques to identify anomalies, defined in this paper as deviation from the group behavior. Early research papers in this field reveal a high number of false positives where a security alert is triggered based on deviation from the cluster learned behavior but still within the norm of what the system defines as an acceptable behavior. Further, domain specific security policies tend to be narrow and inadequately represent what an entity can do. Hence, they: a) limit the amount of useful data during the learning phase; and, b) lead to violation of policy during the execution phase. In this paper, we propose a framework for future research on the role of policies and behavior security in a coalition setting with emphasis on anomaly detection and individual's deviation from group activities.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.