No abstract

The proliferation of Unmanned Aerial Vehicles (UAVs) raises a host of new security concerns. Our research resulted in a prototype UAV monitoring system, which captures flight data and performs real-time estimation/tracking of airframe and controller parameters utilizing the Recursive Least Squares Method. Subjected to statistical validation and trend analysis, parameter estimates are instrumental for the detection of some classes of cyber attacks and incipient hardware failures that can invariably jeopardize mission success. Our results demonstrate that achieving efficient anomaly detection during flight is possible through the intelligent application of statistical methods to system behavioral profiling.

No abstract

While network worms carry various payloads and may utilize any available exploits, they all have one common component -the propagation engine. Moreover, it is important to note that the number of conceptually distinct propagation engines employed by existing network worms is quite limited.This paper presents a novel signature-based approach for detecting attacks perpetrated by network worms as a manifestation of a semantic functionality performed by one of the few known propagation engines. We propose a novel methodology to recognize any semantic functionality in the system call domain through utilizing Colored Petri Nets. In this application, Petri Nets embody behavior-based signatures of the propagation engine functionalities. These signatures are indicative of the shell code activity in the first stage of the worm proliferation.We developed, tested and evaluated a Propagation Engine Detector (PED) system that detects activity of the worm shell code executed by a process during an attack. Moreover, PED is able to recognize the type of propagation engine employed by the attacking worm.

No abstract