56 research outputs found
A proposed data fusion architecture for micro-zone analysis and data mining
Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presents an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture
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Wireless Indoor Location Estimation Based on Neural Network RSS Signature Recognition (LENSR)
Location Based Services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological Counter Propagation Network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results
Building Energy Load Forecasting using Deep Neural Networks
Ensuring sustainability demands more efficient energy management with
minimized energy wastage. Therefore, the power grid of the future should
provide an unprecedented level of flexibility in energy management. To that
end, intelligent decision making requires accurate predictions of future energy
demand/load, both at aggregate and individual site level. Thus, energy load
forecasting have received increased attention in the recent past, however has
proven to be a difficult problem. This paper presents a novel energy load
forecasting methodology based on Deep Neural Networks, specifically Long Short
Term Memory (LSTM) algorithms. The presented work investigates two variants of
the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S)
architecture. Both methods were implemented on a benchmark data set of
electricity consumption data from one residential customer. Both architectures
where trained and tested on one hour and one-minute time-step resolution
datasets. Experimental results showed that the standard LSTM failed at
one-minute resolution data while performing well in one-hour resolution data.
It was shown that S2S architecture performed well on both datasets. Further, it
was shown that the presented methods produced comparable results with the other
deep learning methods for energy forecasting in literature
An Adversarial Approach for Explainable AI in Intrusion Detection Systems
Despite the growing popularity of modern machine learning techniques (e.g.
Deep Neural Networks) in cyber-security applications, most of these models are
perceived as a black-box for the user. Adversarial machine learning offers an
approach to increase our understanding of these models. In this paper we
present an approach to generate explanations for incorrect classifications made
by data-driven Intrusion Detection Systems (IDSs). An adversarial approach is
used to find the minimum modifications (of the input features) required to
correctly classify a given set of misclassified samples. The magnitude of such
modifications is used to visualize the most relevant features that explain the
reason for the misclassification. The presented methodology generated
satisfactory explanations that describe the reasoning behind the
mis-classifications, with descriptions that match expert knowledge. The
advantages of the presented methodology are: 1) applicable to any classifier
with defined gradients. 2) does not require any modification of the classifier
model. 3) can be extended to perform further diagnosis (e.g. vulnerability
assessment) and gain further understanding of the system. Experimental
evaluation was conducted on the NSL-KDD99 benchmark dataset using Linear and
Multilayer perceptron classifiers. The results are shown using intuitive
visualizations in order to improve the interpretability of the results
Intelligent Control in Automation Based on Wireless Traffic Analysis
Wireless technology is a central component of many factory automation infrastructures in both the commercial and government sectors, providing connectivity among various components in industrial realms (distributed sensors, machines, mobile process controllers). However wireless technologies provide more threats to computer security than wired environments. The advantageous features of Bluetooth technology resulted in Bluetooth units shipments climbing to five million per week at the end of 2005 [1, 2]. This is why the real-time interpretation and understanding of Bluetooth traffic behavior is critical in both maintaining the integrity of computer systems and increasing the efficient use of this technology in control type applications. Although neuro-fuzzy approaches have been applied to wireless 802.11 behavior analysis in the past, a significantly different Bluetooth protocol framework has not been extensively explored using this technology. This paper presents a new neurofuzzy traffic analysis algorithm of this still new territory of Bluetooth traffic. Further enhancements of this algorithm are presented along with the comparison against the traditional, numerical approach. Through test examples, interesting Bluetooth traffic behavior characteristics were captured, and the comparative elegance of this computationally inexpensive approach was demonstrated. This analysis can be used to provide directions for future development and use of this prevailing technology in various control type applications, as well as making the use of it more secure
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DSTiPE Algorithm for Fuzzy Spatio-Temporal Risk Calculation in Wireless Environments
Time and location data play a very significant role in a variety of factory automation scenarios, such as automated vehicles and robots, their navigation, tracking, and monitoring, to services of optimization and security. In addition, pervasive wireless capabilities combined with time and location information are enabling new applications in areas such as transportation systems, health care, elder care, military, emergency response, critical infrastructure, and law enforcement. A person/object in proximity to certain areas for specific durations of time may pose a risk hazard either to themselves, others, or the environment. This paper presents a novel fuzzy based spatio-temporal risk calculation DSTiPE method that an object with wireless communications presents to the environment. The presented Matlab based application for fuzzy spatio-temporal risk cluster extraction is verified on a diagonal vehicle movement example
Time Synchronization in Hierarchical TESLA Wireless Sensor Networks
Time synchronization and event time correlation are important in wireless sensor networks. In particular, time is used to create a sequence events or time line to answer questions of cause and effect. Time is also used as a basis for determining the freshness of received packets and the validity of cryptographic certificates. This paper presents secure method of time synchronization and event time correlation for TESLA-based hierarchical wireless sensor networks. The method demonstrates that events in a TESLA network can be accurately timestamped by adding only a few pieces of data to the existing protocol
Improving cyber-security of smart grid systems via anomaly detection and linguistic domain knowledge
The planned large scale deployment of smart grid network devices will generate a large amount of information exchanged over various types of communication networks. The implementation of these critical systems will require appropriate cyber-security measures. A network anomaly detection solution is considered in this work. In common network architectures multiple communications streams are simultaneously present, making it difficult to build an anomaly detection solution for the entire system. In addition, common anomaly detection algorithms require specification of a sensitivity threshold, which inevitably leads to a tradeoff between false positives and false negatives rates. In order to alleviate these issues, this paper proposes a novel anomaly detection architecture. The designed system applies the previously developed network security cyber-sensor method to individual selected communication streams allowing for learning accurate normal network behavior models. Furthermore, the developed system dynamically adjusts the sensitivity threshold of each anomaly detection algorithm based on domain knowledge about the specific network system. It is proposed to model this domain knowledge using Interval Type-2 Fuzzy Logic rules, which linguistically describe the relationship between various features of the network communication and the possibility of a cyber attack. The proposed method was tested on experimental smart grid system demonstrating enhanced cyber-security
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