Our work titled Going a Step Beyond the Black and White Lists for URL Accesses in the Enterprise by means of Categorical Classifiers, as part of the researh under the MUSES project, has been presented today at the ECTA 2014 conference.
Corporate systems can be secured using an enormous quantity of methods, and the implementation of Black or White lists is among them.
With these lists it is possible to restrict (or to allow) the users the execution of applications or the access to certain URLs, among others. This paper is focused on the latter option. It describes the whole processing of a set of data composed by URL sessions performed by the employees of a company; from the preprocessing stage, including labelling and data balancing processes, to the application of several classification algorithms. The aim is to define a method for automatically make a decision of allowing or denying future URL requests, considering a set of corporate security policies.
Thus, this work goes a step beyond the usual black and white lists, since they can only control those URLs that are specifically included in them, but not by making decisions based in similarity (through classification techniques), or even in other variables of the session, as it is proposed here.
The results show a set of classification methods which get very good classification percentages (95-97%), and which infer some useful rules based in additional features (rather that just the URL string) related to the user’s access. This led us to consider that this kind of tool would be very useful tool for an enterprise.
You can check the presentation at: .
After the bad experience of spending money in clusters and grids and then spending more time doing maintenance, hack-proofing and installing stuff than science, maybe it is the time to rethink how massive distributed evolutionary computation should be done. Nowadays there are lots of free or use-based resources that can be tapped for doing volunteer-based evolutionary algorithms. That is way my last keynote and tutorial have dealt with that: the IDC Keynote, Low or No Cost Evolutionary computation, which you can access here in Heroku, puts the money where its mouth is: talking and doing volunteer-based evolutionary computing at the same time. The PPSN tutorial, Low or no cost distributed evolutionary computation, touched on the same topic, only longer and with more enphasis on tools.
During 1 month, papers accepted at GECCO1’4 will be freely available. Thus, you can get and read our papers:
- “Enforcing corporate security policies via computational intelligence techniques” by Antonio M. Mora, Paloma De las Cuevas, Juan Julián Merelo, Sergio Zamarripa, Anna I. Esparcia-Alcázar (doi: 10.1145/2598394.2605438) at http://goo.gl/33gWES
- “A methodology for designing emergent literary backstories on non-player characters using genetic algorithms”, by Rubén Héctor García-Ortega, Pablo García-Sánchez, Antonio Miguel Mora, Juan Julián Merelo (doi: 10.1145/2598394.2598482) at http://goo.gl/9CEcMc
We cordially invite you to attend the following two-presentations on Spatially Structured Metaheuristics. This mini-workshop will be held at 11.30 a.m. in the CITIC-UGR building (June 26th, 2014).
Spatially Structured Metaheuristics: Principles and Practical Applications
by Juan Luis Jiménez Laredo (University of Luxembourg)
A relevant number of metaheuristics are based on population. Although conventions may establish different names, individuals in evolutionary algorithms, ants in ant colony optimization or particles in particle swarm optimization belong to the same side of a coin: they are all atomic elements of the population (a.k.a. building-blocks). In this context, spatially structured metaheuristics investigate how to improve the performance of metaheuristics by confining these elements in neighborhoods. This talk aims at presenting the working principles of spatially structured metaheuristics and practical applications to enhance diversity, scalability and robustness.
Spatially Structured Metaheuristics: Dynamic and Self-organized Topologies
by Carlos M. Fernandes (University of Lisbon)
Population based metaheuristics are computational search or optimization methods that use a population of possible solutions to a problem. These solutions are able communicate, interact and/or evolve. Two types of strategies for structuring population are possible. In panmictic populations, every individual is allowed to interact with every other individual. In non-panmictic metaheuristics, also called spatially structured population-based metaheuristics, the interaction is restricted to a pre-defined or evolving structure (network). Traditional spatially structured metaheuristics are built on pre-defined static networks of acquaintances over which individuals can interact. However, alternative strategies that overcome some of the difficulties and limitations of static networks (extra design and tuning effort, ad hoc decision policies, rigid connectivity, and lack of feedback from the problem structure and search process) are possible. This talk discusses dynamic topologies for spatially structured metaheuristics and describes a new model for structuring populations into partially connected and self-organized networks. Recent applications of the model on Evolutionary Algorithms and Particle Swarm Optimization are given and discussed.
by Victor Manuel Rivas Santos, Maria Isabel Garcia Arenas, Juan Julian Merelo Guervos, Antonio Mora Garcia and Gustavo Romero Lopez.
In EvoAPPS posters (see the poster at slideshare)
Our paper “The L-Co-R co-evolutionary algorithm: a comparative analysis in medium-term time-series forecasting problems” was accepted for oral presentation in the latest ECTA-IJCCI conference.
This paper presents an experimental study in which the effectiveness of the L-Co-R method is tested. L-Co-R is a co-evolutionary algorithm to time series forecasting that evolves, on one hand, RBFNs building an appropriate architecture of net, and on the other hand, sets of time lags that represents the time series in order to perform the forecasting using, at the same time, its own forecasted values. This coevolutive approach makes possible to divide the main problem into two subproblems where every individual of one population cooperates with the individuals of the other. The goal of this work is to analyze the results obtained by L-Co-R comparing with other methods from the time series forecasting field. For that, 20 time series and 5 different methods found in the literature have been selected, and 3 distinct quality measures have been used to show the results. Finally, a statistical study confirms the good results of L-Co-R in most cases.