(Georgia Institute of Technology, 2012-07-10)
Lawrence, Nigel Rhea
Network monitoring is a necessity for both reducing downtime and ensuring
rapid response in the case of software or hardware failure. Unfortunately, one of the
most widely used protocols for monitoring networks, the Simple Network Management
Protocol (SNMPv3), does not offer an acceptable level of confidentiality or integrity
for these services. In this paper, we demonstrate two attacks against the most current
and secure version of the protocol with authentication and encryption enabled. In
particular, we demonstrate that under reasonable conditions, we can read encrypted
requests and forge messages between the network monitor and the hosts it observes.
Such attacks are made possible by an insecure discovery mechanism, which allows
an adversary capable of compromising a single network host to set the keys used by
the security functions. Our attacks show that SNMPv3 places too much trust on the
underlying network, and that this misplaced trust introduces vulnerabilities that can
be exploited.
(Georgia Institute of Technology, 2010-11-18)
Bormann, Richard Klaus Eduard
In this thesis we investigate visual place categorization by combining successful global image descriptors with a method of visual attention in order to automatically detect meaningful objects for places. The idea behind this is to incorporate information about typical objects for place categorization without the need for tedious labelling of important objects. Instead, the applied attention mechanism is intended to find the objects a human observer would focus first, so that the algorithm can use their discriminative power to conclude the place category. Besides this object-based place categorization approach we employ the Gist and the Centrist descriptor as holistic image descriptors.
To access the power of all these descriptors we employ SVM-DAS (discriminative accumulation scheme) for cue integration and furthermore smooth the output trajectory with a delayed Hidden Markov Model. For the classification of the variety of descriptors we present and evaluate several classification methods. Among them is a joint probability modelling approach with two approximations as well as a modified KNN classifier, AdaBoost and SVM. The latter two classifiers are enhanced for multi-class use with a probabilistic computation scheme which treats the individual classifiers as peers and not as a hierarchical sequence.
We check and tweak the different descriptors and classifiers in extensive tests mainly with a dataset of six homes. After these experiments we extend the basic algorithm with further filtering and tracking methods and evaluate their influence on the performance. Finally, we also test our algorithm within a university environment and on a real robot within a home environment.