Current cyber-related security and safety risks are unprecedented, due in no small part to information overload
and skilled cyber-analyst shortages. Advances in decision support and Situation Awareness (SA) tools are
required to support analysts in risk mitigation. Inspired by human intelligence, research in Artificial Intelligence
(AI) and Computational Intelligence (CI) have provided successful engineering solutions in complex domains
including cyber. Current AI approaches aggregate large volumes of data to infer the general from the particular,
i.e. inductive reasoning (pattern-matching) and generally cannot infer answers not previously programmed.
Whereas humans, rarely able to reason over large volumes of data, have successfully reached the top of the
food chain by inferring situations from partial or even partially incorrect information, i.e. abductive reasoning
(pattern-completion); generating a hypothetical explanation of observations. In order to achieve an engineering
advantage in computational decision support and SA we leverage recent research in human consciousness, the role
consciousness plays in decision making, modeling the units of subjective experience which generate consciousness,
qualia. This paper introduces a novel computational implementation of a Cognitive Modeling Architecture (CMA)
which incorporates concepts of consciousness. We apply our model to the malware type-classification task. The
underlying methodology and theories are generalizable to many domains.
In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being
implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile
devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to
properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational
complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile
environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today
are capable of processing a majority of the available classification algorithms without concern of processing while the
same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system
targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance
shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The
methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.
There are over 250 image steganography methods available on the Internet. In digital image steganalysis an analyst has
three goals, first determine if an embedded message exists, next determine the embedding method used to create the
stego image and finally extract the hidden message. The objective of this paper lies on the second goal, that is, to
identify the embedding technique used to create the steganography image. Several detection systems currently exist, so
the identification problem becomes one of determining which detection system has correctly identified the embedding
method. In this work, the individual detection systems are fused using boosting. Boosting is a powerful technique for
combining an ensemble of base classifiers to produce a form of committee with improved performance over any of the
single classifiers in the ensemble. The results in this paper show that boosting takes advantage of the individual strengths
from each detection systems and classification performance is increased by 10%.
There are several security issues tied to multimedia when implementing the various applications in the cellular phone
and wireless industry. One primary concern is the potential ease of implementing a steganography system. Traditionally,
the only mechanism to embed information into a media file has been with a desktop computer. However, as the cellular
phone and wireless industry matures, it becomes much simpler for the same techniques to be performed using a cell
phone. In this paper, two methods are compared that classify cell phone images as either an anomaly or clean, where a
clean image is one in which no alterations have been made and an anomalous image is one in which information has
been hidden within the image. An image in which information has been hidden is known as a stego image. The main
concern in detecting steganographic content with machine learning using cell phone images is in training specific
embedding procedures to determine if the method has been used to generate a stego image. This leads to a possible flaw
in the system when the learned model of stego is faced with a new stego method which doesn't match the existing
model. The proposed solution to this problem is to develop systems that detect steganography as anomalies, making the
embedding method irrelevant in detection. Two applicable classification methods for solving the anomaly detection of
steganographic content problem are single class support vector machines (SVM) and Parzen-window. Empirical
comparison of the two approaches shows that Parzen-window outperforms the single class SVM most likely due to the fact that Parzen-window generalizes less.
Images and data files provide an excellent opportunity for concealing illegal or clandestine material. Currently, there are
over 250 different tools which embed data into an image without causing noticeable changes to the image. From a
forensics perspective, when a system is confiscated or an image of a system is generated the investigator needs a tool
that can scan and accurately identify files suspected of containing malicious information. The identification process is
termed the steganalysis problem which focuses on both blind identification, in which only normal images are available
for training, and multi-class identification, in which both the clean and stego images at several embedding rates are
available for training. In this paper an investigation of a clustering and classification technique (Expectation
Maximization with mixture models) is used to determine if a digital image contains hidden information. The steganalysis
problem is for both anomaly detection and multi-class detection. The various clusters represent clean images and stego
images with between 1% and 10% embedding percentage. Based on the results it is concluded that the EM classification
technique is highly suitable for both blind detection and the multi-class problem.
Steganalysis has many challenges; which include the accurate and efficient detection of hidden content within digital images. This paper focuses on the development of a new multi pixel comparison method used for the detection of steganographic content within digital images transmitted over mobile channels. The sensitivity of detecting hidden information within a digital image can be increased or decreased to determine if slight changes have been made to the digital image for the target of blind steganalysis. The key thought of the presented method is to increase the sensitivity of features when alterations are made within the bit planes of a digital image. The differences between the new method and existing pixel comparison methods are; multiple masks of different sizes are used to increase the sensitivity and weighted features are used to improve the classification of the feature sets. Weights are also used with the various pixel comparisons to ensure proper sensitivity when detecting small changes. The article also investigates the reliability of detection and estimation length of hidden data within wireless digital images with potential for military applications emphasizing on defense and security.
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