Biblio
Large scale sensor networks are ubiquitous nowadays. An important objective of deploying sensors is to detect anomalies in the monitored system or infrastructure, which allows remedial measures to be taken to prevent failures, inefficiencies, and security breaches. Most existing sensor anomaly detection methods are local, i.e., they do not capture the global dependency structure of the sensors, nor do they perform well in the presence of missing or erroneous data. In this paper, we propose an anomaly detection technique for large scale sensor data that leverages relationships between sensors to improve robustness even when data is missing or erroneous. We develop a probabilistic graphical model-based global outlier detection technique that represents a sensor network as a pairwise Markov Random Field and uses graphical model inference to detect anomalies. We show our model is more robust than local models, and detects anomalies with 90% accuracy even when 50% of sensors are erroneous. We also build a synthetic graphical model generator that preserves statistical properties of a real data set to test our outlier detection technique at scale.
Autonomous active exploration requires search algorithms that can effectively balance the need for workspace coverage with energetic costs. We present a strategy for planning optimal search trajectories with respect to the distribution of expected information over a workspace. We formulate an iterative optimal control algorithm for general nonlinear dynamics, where the metric for information gain is the difference between the spatial distribution and the statistical representation of the time-averaged trajectory, i.e. ergodicity. Previous work has designed a continuous-time trajectory optimization algorithm. In this paper, we derive two discrete-time iterative trajectory optimization approaches, one based on standard first-order discretization and the other using symplectic integration. The discrete-time methods based on first-order discretization techniques are both faster than the continuous-time method in the studied examples. Moreover, we show that even for a simple system, the choice of discretization has a dramatic impact on the resulting control and state trajectories. While the standard discretization method turns unstable, the symplectic method, which is structure-preserving, achieves lower values for the objective.
Dynamic firewalls with stateful inspection have added a lot of security features over the stateless traditional static filters. Dynamic firewalls need to be adaptive. In this paper, we have designed a framework for dynamic firewalls based on probabilistic ontology using Multi Entity Bayesian Networks (MEBN) logic. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated substructures and can express a probability distribution over models of any consistent first order theory. The motivation of our proposed work is about preventing novel attacks (i.e. those attacks for which no signatures have been generated yet). The proposed framework is in two important parts: first part is the data flow architecture which extracts important connection based features with the prime goal of an explicit rule inclusion into the rule base of the firewall; second part is the knowledge flow architecture which uses semantic threat graph as well as reasoning under uncertainty to fulfill the required objective of providing futuristic threat prevention technique in dynamic firewalls.