Feature-based validation reasoning for intent-driven engineering design

Feature based modelling represents the future of CAD systems. However, operations such as modelling and editing can corrupt the validity of a feature-based model representation. Feature interactions are a consequence of feature operations and the existence of a number of features in the same model. Feature interaction affects not only the solid representation of the part, but also the functional intentions embedded within features. A technique is thus required to assess the integrity of a feature-based model from various perspectives, including the functional intentional one, and this technique must take into account the problems brought about by feature interactions and operations. The understanding, reasoning and resolution of invalid feature-based models requires an understanding of the feature interaction phenomena, as well as the characterisation of these functional intentions. A system capable of such assessment is called a feature-based representation validation system. This research studies feature interaction phenomena and feature-based designer's intents as a medium to achieve a feature-based representation validation system. [Continues.

Development of self-adaptive back propagation and derivative free training algorithms in artificial neural networks

Three new iterative, dynamically self-adaptive, derivative-free and training parameter free artificial neural network (ANN) training algorithms are developed. They are defined as self-adaptive back propagation, multi-directional and restart ANN training algorithms. The descent direction in self-adaptive back propagation training is determined implicitly by a central difference approximation scheme, which chooses its step size according to the convergence behavior of the error function. This approach trains an ANN when the gradient information of the corresponding error function is not readily available. The self- adaptive variable learning rates per epoch are determined dynamically using a constrained interpolation search. As a result, appropriate descent to the error function is achieved. The multi-directional training algorithm is self-adaptive and derivative free. It orients an initial search vector in a descent location at the early stage of training. Individual learning rates and momentum term for all the ANN weights are determined optimally. The search directions are derived from rectilinear and Euclidean paths, which explore stiff ridges and valleys of the error surface to improve training. The restart training algorithm is derivative free. It redefines a de-generated simplex at a re-scale phase. This multi-parameter training algorithm updates ANN weights simultaneously instead of individually. The descent directions are derived from the centroid of a simplex along a reflection point opposite to the worst vertex. The algorithm is robust and has the ability to improve local search. These ANN training methods are appropriate when there is discontinuity in corresponding ANN error function or the Hessian matrix is ill conditioned or singular. The convergence properties of the algorithms are proved where possible. All the training algorithms successfully train exclusive OR (XOR), parity, character-recognition and forecasting problems. The simulation results with XOR, parity and character recognition problems suggest that all the training algorithms improve significantly over the standard back propagation algorithm in average number of epoch, function evaluations and terminal function values. The multivariate ANN calibration problem as a regression model with small data set is relatively difficult to train. In forecasting problems, an ANN is trained to extrapolate the data in validation period. The extrapolation results are compared with the actual data. The trained ANN performs better than the statistical regression method in mean absolute deviations; mean squared errors and relative percentage error. The restart training algorithm succeeds in training a problem, where other training algorithms face difficulty. It is shown that a seasonal time series problem possesses a Hessian matrix that has a high condition number. Convergence difficulties as well as slow training are therefore not atypical. The research exploits the geometry of the error surface to identify self-adaptive optimized learning rates and momentum terms. Consequently, the algorithms converge with high success rate. These attributes brand the training algorithms as self-adaptive, automatic, parameter free, efficient and easy to use

STIXnet: entity and relation extraction from unstructured CTI reports

The increased frequency of cyber attacks against organizations and their potentially devastating effects has raised awareness on the severity of these threats. In order to proactively harden their defences, organizations have started to invest in Cyber Threat Intelligence (CTI), the field of Cybersecurity that deals with the collection, analysis and organization of intelligence on the attackers and their techniques. By being able to profile the activity of a particular threat actor, thus knowing the types of organizations that it targets and the kind of vulnerabilities that it exploits, it is possible not only to mitigate their attacks, but also to prevent them. Although the sharing of this type of intelligence is facilitated by several standards such as STIX (Structured Threat Information eXpression), most of the data still consists of reports written in natural language. This particular format can be highly time-consuming for Cyber Threat Intelligence analysts, which may need to read the entire report and label entities and relations in order to generate an interconnected graph from which the intel can be extracted. In this thesis, done in collaboration with Leonardo S.p.A., we provide a modular and extensible system called STIXnet for the extraction of entities and relations from natural language CTI reports. The tool is embedded in a larger platform, developed by Leonardo, called Cyber Threat Intelligence System (CTIS) and therefore inherits some of its features, such as an extensible knowledge base which also acts as a database for the entities to extract. STIXnet uses techniques from Natural Language Processing (NLP), the branch of computer science that studies the ability of a computer program to process and analyze natural language data. This field of study has been recently revolutionized by the increasing popularity of Machine Learning, which allows for more efficient algorithms and better results. After looking for known entities retrieved from the knowledge base, STIXnet analyzes the semantic structure of the sentences in order to extract new possible entities and predicts Tactics, Techniques, and Procedures (TTPs) used by the attacker. Finally, an NLP model extracts relations between these entities and converts them to be compliant with the STIX 2.1 standard, thus generating an interconnected graph which can be exported and shared. STIXnet is also able to be constantly and automatically improved with some feedback from a human analyzer, which by highlighting false positives and false negatives in the processing of the report, can trigger a fine-tuning process that will increase the tool's overall accuracy and precision. This framework can help defenders to immediately know at a glace all the gathered intelligence on a particular threat actor and thus deploy effective threat detection, perform attack simulations and strengthen their defenses, and together with the Cyber Threat Intelligence System platform organizations can be always one step ahead of the attacker and be secure against Advanced Persistent Threats (APTs).The increased frequency of cyber attacks against organizations and their potentially devastating effects has raised awareness on the severity of these threats. In order to proactively harden their defences, organizations have started to invest in Cyber Threat Intelligence (CTI), the field of Cybersecurity that deals with the collection, analysis and organization of intelligence on the attackers and their techniques. By being able to profile the activity of a particular threat actor, thus knowing the types of organizations that it targets and the kind of vulnerabilities that it exploits, it is possible not only to mitigate their attacks, but also to prevent them. Although the sharing of this type of intelligence is facilitated by several standards such as STIX (Structured Threat Information eXpression), most of the data still consists of reports written in natural language. This particular format can be highly time-consuming for Cyber Threat Intelligence analysts, which may need to read the entire report and label entities and relations in order to generate an interconnected graph from which the intel can be extracted. In this thesis, done in collaboration with Leonardo S.p.A., we provide a modular and extensible system called STIXnet for the extraction of entities and relations from natural language CTI reports. The tool is embedded in a larger platform, developed by Leonardo, called Cyber Threat Intelligence System (CTIS) and therefore inherits some of its features, such as an extensible knowledge base which also acts as a database for the entities to extract. STIXnet uses techniques from Natural Language Processing (NLP), the branch of computer science that studies the ability of a computer program to process and analyze natural language data. This field of study has been recently revolutionized by the increasing popularity of Machine Learning, which allows for more efficient algorithms and better results. After looking for known entities retrieved from the knowledge base, STIXnet analyzes the semantic structure of the sentences in order to extract new possible entities and predicts Tactics, Techniques, and Procedures (TTPs) used by the attacker. Finally, an NLP model extracts relations between these entities and converts them to be compliant with the STIX 2.1 standard, thus generating an interconnected graph which can be exported and shared. STIXnet is also able to be constantly and automatically improved with some feedback from a human analyzer, which by highlighting false positives and false negatives in the processing of the report, can trigger a fine-tuning process that will increase the tool's overall accuracy and precision. This framework can help defenders to immediately know at a glace all the gathered intelligence on a particular threat actor and thus deploy effective threat detection, perform attack simulations and strengthen their defenses, and together with the Cyber Threat Intelligence System platform organizations can be always one step ahead of the attacker and be secure against Advanced Persistent Threats (APTs)

Trust-based mechanisms for secure communication in cognitive radio networks

Cognitive radio (CR) technology was introduced to solve the problem of spectrum scarcity to support the growth of wireless communication. However, the inherent properties of CR technology make such networks more vulnerable to attacks. This thesis is an effort to develop a trust-based framework to ensure secure communication in CRN by authenticating trustworthy nodes to share spectrum securely and increasing system's availability and reliability by selecting the trustworthy key nodes in CRNs

Aeronautical Engineering: A continuing bibliography with indexes (supplement 177)

This bibliography lists 469 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1984

Aeronautical Engineering: A continuing bibliography with indexes (supplement 206)

This bibliography lists 422 reports, articles and other documents introduced into the NASA scientific and technical information system in October 1986

Tools for Composite Indicators Building

Our society is changing so fast we need to know as soon as possible when things go wrong (Euroabstracts, 2003). This is where composite indicators enter into the discussion. A composite indicator is an aggregated index comprising individual indicators and weights that commonly represent the relative importance of each indicator. However, the construction of a composite indicator is not straightforward and the methodological challenges raise a series of technical issues that, if not addressed adequately, can lead to composite indicators being misinterpreted or manipulated. Therefore, careful attention needs to be given to their construction and subsequent use. This document reviews the steps involved in a composite indicator’s construction process and discusses the common pitfalls to be avoided. We stress the need for multivariate analysis prior to the aggregation of the individual indicators. We deal with the problem of missing data and with the techniques used to bring into a common unit the indicators that are of very different nature. We explore different methodologies for weighting and aggregating indicators into a composite and test the robustness of the composite using uncertainty and sensitivity analysis. Finally we show how the same information that is communicated by the composite indicator can be presented in very different ways and how this can influence the policy message.JRC.G.9-Econometrics and statistical support to antifrau