Antonio Martínez-Álvarez

Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

A Novel Co-Design Approach for Soft Errors Mitigation in Embedded Systems

There is an increasing concern about the mitigation of radiation effects in embedded systems. This fact is demanding new flexible design methodologies and tools that allow dealing with design constraints and dependability requirements at the same time. This paper presents a novel proposal to design radiation-tolerant embedded systems combining hardware and software mitigation techniques. A hardening infrastructure, which facilitates the design space exploration and the trade-offs analyses, has been developed to support this fault tolerance co-design approach. The advantages of our proposal are illustrated by means of a case study.

Compiler-Directed Soft Error Mitigation for Embedded Systems

The protection of processor-based systems to mitigate the harmful effect of transient faults (soft errors) is gaining importance as technology shrinks. At the same time, for large segments of embedded markets, parameters like cost and performance continue to be as important as reliability. This paper presents a compiler-based methodology for facilitating the design of fault-tolerant embedded systems. The methodology is supported by an infrastructure that permits to easily combine hardware/software soft errors mitigation techniques in order to best satisfy both usual design constraints and dependability requirements. It is based on a generic microprocessor architecture that facilitates the implementation of software-based techniques, providing a uniform isolated-from-target hardening core that allows the automatic generation of protected source code (hardened code). Two case studies are presented. In the first one, several software-based mitigation techniques are implemented and evaluated showing the flexibility of the infrastructure. In the second one, a customized fault tolerant embedded system is designed by combining selective protection on both hardware and software. Several trade-offs among performance, code size, reliability, and hardware costs have been explored. Results show the applicability of the approach. Among the developed software-based mitigation techniques, a novel selective version of the well known SWIFT-R is presented.

Multi-objective adaptive evolutionary strategy for tuning compilations

Tuning compilations is the process of adjusting the values of a compiler options to improve some features of the final application. In this paper, a strategy based on the use of a genetic algorithm and a multi-objective scheme is proposed to deal with this task. Unlike previous works, we try to take advantage of the knowledge of this domain to provide a problem-specific genetic operation that improves both the speed of convergence and the quality of the results. The evaluation of the strategy is carried out by means of a case of study aimed to improve the performance of the well-known web server Apache. Experimental results show that a 7.5% of overall improvement can be achieved. Furthermore, the adaptive approach has shown an ability to markedly speed-up the convergence of the original strategy.

Fault tolerant embedded systems design by multi-objective optimization

The design of fault tolerant systems is gaining importance in large domains of embedded applications where design constrains are as important as reliability. New software techniques, based on selective application of redundancy, have shown remarkable fault coverage with reduced costs and overheads. However, the large number of different solutions provided by these techniques, and the costly process to assess their reliability, make the design space exploration a very difficult and time-consuming task. This paper proposes the integration of a multi-objective optimization tool with a software hardening environment to perform an automatic design space exploration in the search for the best trade-offs between reliability, cost, and performance. The first tool is commanded by a genetic algorithm which can simultaneously fulfill many design goals thanks to the use of the NSGA-II multi-objective algorithm. The second is a compiler-based infrastructure that automatically produces selective protected (hardened) versions of the software and generates accurate overhead reports and fault coverage estimations. The advantages of our proposal are illustrated by means of a complex and detailed case study involving a typical embedded application, the AES (Advanced Encryption Standard).

Efficient Mitigation of Data and Control Flow Errors in Microprocessors

The use of microprocessor-based systems is gaining importance in application domains where safety is a must. For this reason, there is a growing concern about the mitigation of SEU and SET effects. This paper presents a new hybrid technique aimed to protect both the data and the control-flow of embedded applications running on microprocessors. On one hand, the approach is based on software redundancy techniques for correcting errors produced in the data. On the other hand, control-flow errors can be detected by reusing the on-chip debug interface, existing in most modern microprocessors. Experimental results show an important increase in the system reliability even superior to two orders of magnitude, in terms of mitigation of both SEUs and SETs. Furthermore, the overheads incurred by our technique can be perfectly assumable in low-cost systems.

RetinaStudio: A bioinspired framework to encode visual information

Abstract The retina is a very complex neural structure, which performs spatial, temporal, and chromatic processing on visual information and converts it into a compact ‘digital’ format composed of neural impulses. This paper presents a new compiler-based framework able to describe, simulate and validate custom retina models. The framework is compatible with the most usual neural recording and analysis tools, taking advantage of the interoperability with these kinds of applications. Furthermore it is possible to compile the code to generate accelerated versions of the visual processing models compatible with COTS microprocessors, FPGAs or GPUs. The whole system represents an ongoing work to design and develop a functional visual neuroprosthesis. Several case studies are described to assess the effectiveness and usefulness of the framework.

Selective SWIFT-R

Commercial off-the-shelf microprocessors are the core of low-cost embedded systems due to their programmability and cost-effectiveness. Recent advances in electronic technologies have allowed remarkable improvements in their performance. However, they have also made microprocessors more susceptible to transient faults induced by radiation. These non-destructive events (soft errors), may cause a microprocessor to produce a wrong computation result or lose control of a system with catastrophic consequences. Therefore, soft error mitigation has become a compulsory requirement for an increasing number of applications, which operate from the space to the ground level. In this context, this paper uses the concept of selective hardening, which is aimed to design reduced-overhead and flexible mitigation techniques. Following this concept, a novel flexible version of the software-based fault recovery technique known as SWIFT-R is proposed. Our approach makes possible to select different registers subsets from the microprocessor register file to be protected on software. Thus, design space is enriched with a wide spectrum of new partially protected versions, which offer more flexibility to designers. This permits to find the best trade-offs between performance, code size, and fault coverage. Three case studies have been developed to show the applicability and flexibility of the proposal.

Automatic Tuning of a Retina Model for a Cortical Visual Neuroprosthesis Using a Multi-Objective Optimization Genetic Algorithm

The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. The encoding of visual information is one of the basic questions in visual and computational neuroscience and is also of seminal importance in the field of visual prostheses. In this framework, it is essential to have artificial retina systems to be able to function in a way as similar as possible to the biological retinas. This paper proposes an automatic evolutionary multi-objective strategy based on the NSGA-II algorithm for tuning retina models. Four metrics were adopted for guiding the algorithm in the search of those parameters that best approximate a synthetic retinal model output with real electrophysiological recordings. Results show that this procedure exhibits a high flexibility when different trade-offs has to be considered during the design of customized neuro prostheses.

Soft core based embedded systems in critical aerospace applications

There is an increasing interest in the aerospace industry to reduce the cost of the systems by means of using Commercial Off The Shelf (COTS) devices. The engineering of novel microsatellites and nanosatellites are clear examples of this new trend. However, the use of sub-micron technologies has led to greater sensitivity of these devices to radiation-induced transient faults, limiting the exploitation of this approach in critical systems. This paper presents an innovative application of soft-core microprocessor based embedded systems, to design dependable and reduced-cost critical systems with COTS reconfigurable devices (flash based FPGAs). To make this possible, it is necessary to fine-tune the protection strategy by combining selectively fault mitigation techniques based on hardware or software. In this way, the resultant system not only fulfills both the design constraints and the dependability requirements, but also avoids the cost provoked by excessive use of protection mechanisms. A case study is presented in which the design space exploration between hardware and software protection techniques permits to find the best trade-offs among performance, reliability, memory size and hardware cost in a dependable subsystem.