There is an increasing need for both governments and businesses to discover latent anomalous activities in unstructured publicly-available data, produced by professional agencies and the general public. Over the past two decades, consumers have begun to use smart devices to both take in and generate a large volume of open-source text-based data, providing the opportunity for latent anomaly analysis. However, real-time data acquisition, and the processing and interpretation of various types of unstructured data, remains a great challenge. Recent efforts have focused on artificial intelligence / machine learning (AI/ML) solutions to accelerate the labor-intensive linear collection, exploitation, and dissemination analysis cycle and enhance it with a data-driven rapid integration and correlation process of open-source data. This paper describes an Activity Based Intelligence framework for anomaly detection of open-source big data using AI/ML to perform semantic analysis. The proposed Anomaly Detection using Semantic Analysis Knowledge (ADUSAK) framework includes four layers: input layer, knowledge layer, reasoning layer, and graphical user interface (GUI)/output layer. The corresponding main technologies include: Information Extraction, Knowledge Graph (KG) construction, Semantic Reasoning, and Pattern Discovery. Finally, ADUSAK was verified by performing Emerging Events Detection, Fake News Detection, and Suspicious Network Analysis. The generalized ADUSAK framework can be easily extended to a wide range of applications by adjusting the data collection, modeling construction, and event alerting.
Although
more
information
than
ever
before
is
available
to
support
the
knowledge
discovery
and
decision
making
processes,
the
vast
proliferation
of
types
of
data,
devices,
and
protocols
makes
it
increasingly
difficult
to
ensure
that
the
right
information
is
received
by
the
right
people
at
the
right
time.
It
becomes
even
more
challenging
when
the
information
has
security
classifications
that
need
to
be
processed
as
well.
This
paper
investigates
methods
and
procedures
for
handling
and
disseminating
information
to
users
and
groups
of
users
that
possess
varying
constraints,
including
security
classifications.
The
cross-domain
implications
are
critical
in
that
certain
users
must
only
be
allowed
access
to
information
that
meets
their
clearance
level
and
need-to-know.
The
ability
to
securely
manage
and
deliver
critical
knowledge
and
actionable
intelligence
to
the
decision
maker
regardless
of
device
configuration
(bandwidth,
processing
speed,
etc.),
classification
level
or
location
in
a
reliable
manner,
would
provide
anytime
access
to
useable
information.
There
are
several
important
components
to
an
intuitive
system
that
can
provide
timely
information
in
a
receiver-preferred
manner.
Besides
the
ability
to
format
information
to
accommodate
the
user's
device
and
profiles,
it's
very
important
to
address
multi-level
security,
which
could
provide
ability
to
properly
send
classified
information
across
different
domains,
thus enabling
faster
dissemination
of
time
critical
information.
One
factor
that
may
simplify
this
process
is
the
information
provider's
disregard
for
the
recipient's
device
limitations.
The
system
that
provides
or
"proxies"
the
transfer
of
information
should
handle
the
presentation
to
the
receiver.
These
topics
will
be
the
main
theme
of
this
paper.
Although more information than ever before is available to support the intelligence
analyst, the vast proliferation of types of data, devices, and protocols makes it increasingly
difficult to ensure that the right information is received by the right people at the right time.
Analysts struggle to balance information overload and an information vacuum depending on
their location and available equipment. The ability to securely manage and deliver critical
knowledge and actionable intelligence to the analyst regardless of device configuration,
classification level or location in a reliable manner, would provide the analyst 24/7 access to
useable information. There are several important components to an intuitive system that can
provide timely information in a user-preferred manner. Two of these components: information
presentation based on the user's preference and requirements and the identification of solutions
to the problem of secure information delivery across multiple security levels, will be discussed in
this paper.
Although more information than ever before is available to support the intelligence analyst, the vast proliferation of types of data, devices, and protocols makes it increasingly
difficult to ensure that the right information is received by the right people at the right time. Analysts can rapidly shift between information overload and an information vacuum depending on their location and available equipment. The ability to securely manage and deliver critical knowledge and actionable intelligence to the analyst regardless of device configuration (bandwidth, processing speed, etc.), classification level or location in a reliable manner, would provide the analyst 24/7 access to useable information. There are several important components to an intuitive system that can provide timely information in a user-preferred manner. Two of these components: formatting information to accommodate the user's profiles and the identification of solutions to the problem of secure information delivery across multiple security
levels, will be discussed in this paper.
Conference Committee Involvement (4)
Machine Intelligence and Bio-inspired Computation: Theory and Applications VII
2 May 2013 | Baltimore, Maryland, United States
Evolutionary and Bio-inspired Computation: Theory and Applications VI
25 April 2012 | Baltimore, Maryland, United States
Evolutionary and Bio-Inspired Computation: Theory and Applications V
27 April 2011 | Orlando, Florida, United States
Evolutionary and Bio-Inspired Computation: Theory and Applications IV
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