Digital steganography is gaining wide acceptance in the world of electronic copyright stamping. Digital media that are
easy to steal, such as graphics, photos and audio files, are being tagged with both visible and invisible copyright stamps
(known as digital watermarking). However, these same techniques can also be used to hide communications between
actors in criminal or covert activities. An inherent difficulty in detecting steganography is overcoming the variety of
methods for hiding a message and the multitude of choices of available media. Another problem in steganography
defense is the issue of detection speed since the encoded data is frequently time-sensitive. When a message is visually
transmitted in a non-textual format (i.e., in an image) it is referred to as a semagram. Semagrams are relatively easy to
create, but very difficult to detect. While steganography can often be identified by detecting digital modifications to an
image's structure, an image-based semagram is more difficult because the message is the image itself. The work
presented describes the creation of a novel, computer-based application, which uses hybrid hierarchical neural network
architecture to detect the likely presence of a semagram message in an image. The prototype system was used to detect
semagrams containing Morse Code messages. Based on the results of these experiments our approach provides a
significant advance in the detection of complex semagram patterns. Specific results of the experiments and the potential
practical applications of the neural network-based technology are discussed. This presentation provides the final results
of our research experiments.
Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical
applications due to the flexibility and extensibility of the technology. While these networks possess numerous
advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same
types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth
available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the
technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an
approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the
network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the
autonomic processes of biological systems. Each component of the network recognizes activity in its local environment
and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm
intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network.
The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to
identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established
interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted
collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network
attacks.
Digital steganography has been used extensively for electronic copyright stamping, but also for criminal or covert
activities. While a variety of techniques exist for detecting steganography the identification of semagrams, messages
transmitted visually in a non-textual format remain elusive. The work that will be presented describes the creation of a
novel application which uses hierarchical neural network architectures to detect the likely presence of a semagram
message in an image. The application was used to detect semagrams containing Morse Code messages with over 80%
accuracy. These preliminary results indicate a significant advance in the detection of complex semagram patterns.
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