KEYWORDS: Received signal strength, Computer intrusion detection, Environmental sensing, Digital filtering, Statistical analysis, Transmitters, Receivers, Diagnostics, Reliability, Signal detection
The large-scale wireless sensing data collected from wireless networks can be used for detecting intruders
(e.g., enemies in tactical fields), and further facilitating real-time situation awareness in Army's networkcentric
warfare applications such as intrusion detection, battlefield protection and emergency evacuation.
In this work, we focus on exploiting Received Signal Strength (RSS) obtained from the existing wireless
infrastructures for performing intrusion detection when the intruders or objects do not carry any radio
devices. This is also known as passive intrusion detection. Passive intrusion detection based on the RSS
data is an attractive approach as it reuses the existing wireless environmental data without requiring a
specialized infrastructure. We propose a clustering-based learning mechanism for passive intrusion detection
in wireless networks. Specifically, our detection scheme utilizes the clustering method to analyze the changes
of RSS, caused by intrusions, at multiple devices to diagnose the presence of intrusions collaboratively. Our
experimental results using an IEEE 802.15.4 (Zigbee) network in a real office environment show that our clustering-based learning can effectively detect the presence of intrusions.
This paper presents a high efficiency algorithm, Multiple Analytical Distribution Filter (MADF), to estimate
location for underwater navigation. Using small grid sampling around candidate areas of high probability,
MADF computes probabilities directly from the known analytical distributions of each beacon. The algorithm is
deterministic and achieves similar results to particle filters, but at a lower computational cost in our tests. MADF
and particle filters represent improvements over Kalman Filters for environments characterized by non-Gaussian
noise distribution.
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