Network intrusions leverage vulnerable hosts as stepping stones to penetrate deeper into a network and mask malicious actions from detection. Identifying stepping stones presents a significant challenge because network sessions appear as legitimate traffic. This research focuses on a novel active watermark technique using discrete wavelet transformations to mark and detect interactive network sessions. This technique is scalable, resilient to network noise, and difficult for attackers to discern that it is in use. Previously captured timestamps from the CAIDA 2009 dataset are sent using live stepping stones in the Amazon Elastic Compute Cloud service. The client system sends watermarked and unmarked packets from California to Virginia using stepping stones in Tokyo, Ireland and Oregon. Five trials are conducted in which the system sends simultaneous watermarked samples and unmarked samples to each target. The live experiment results demonstrate approximately 5% False Positive and 5% False Negative detection rates. Additionally, watermark extraction rates of approximately 92% are identified for a single stepping stone. The live experiment results demonstrate the effectiveness of discerning watermark traffic as applied to identifying stepping stones.
E.C Adam defined Situational Awareness (SA) as "the mental representation and understanding of objects, events,
people, system states, interactions, environmental conditions, and other situation-specific factors affecting human
performance in complex and dynamic tasks. Stated in lay terms, SA is simply knowing what is going on so you can
figure out what to do." We propose a novel idea to assist the human in gaining SA. Our hypothesis is that nature uses
qualia as a compression scheme to represent the many concepts encountered in everyday life. Qualia enable humans to
quickly come up with SA based on many complex measurements from their sensors, (eyes, ears, taste, touch, memory,
etc.), expectations, and experiences. Our ultimate objective is to develop a computer that uses qualia concepts to
transform sensor data to assist the human in gaining and maintaining improved SA. However, before any computer can
use qualia, we must first define a representation for qualia that can be implemented computationally. This paper will
present our representation for qualia. The representation is not simply a hierarchical aggregation of input data. Instead, it
is a prediction of what will happen next, derived from computations resulting from sensory inputs and the computational
engine of a qualia generator and qualia processor.