S2 Grupo (Spain)

LiDAR technology is reshaping cave surveying by providing detailed 3D models that enhance the accuracy of morphological and rock art digitisation and reduce subjective interpretation. This technology, in its varied forms and solely or combined with other remote sensing techniques such as photogrammetry, enriches the documentation and supports multidisciplinary research by enabling spatial analyses and virtual exploration, thus opening new possibilities in various fields such as archaeology, geology, speleology, tourism or education. In this sense, this research aimed to democratise the use of low-cost mobile LiDAR (Light Detection and Ranging) 3D scanning, subjected to fewer accessibility limitations than tripod-mounted Terrestrial Laser Scanners (TLS), in cave archaeology and speleology. For this purpose, La Pileta Cave in Málaga (Spain) was chosen as a case study. Declared a Spanish National Monument in 1924, the cave boasts one of the greatest collections of prehistoric art in Europe and, therefore, a reference in South Iberia, and outstands out for its varied karstic morphologies. The research methodology involved a systematic process to ensure clarity and accuracy. First, the main itinerary in La Pileta was scanned using the smartphone LiDAR technique. This was followed by a Terrestrial Laser Scanning (TLS) survey of a specific sector within the same itinerary, with numerous morphological details of its karstic environment and important Palaeolithic rock art samples. Both the smartphone LiDAR and TLS spatial data were then validated against a Ground Control Points (GCPs) network previously established using a total station. Given the higher accuracy of TLS for graphical documentation, it was further employed as a benchmark to validate the accuracy of smartphone LiDAR. Despite its limitations, this research revealed smartphone LiDAR as a suitable technique for geometric data recording in cave archaeology and speleology. Solely or combined with TLS, mobile LiDAR can be used to document rock art panels in karstic environments, surpassing the latter technique in terms of texture quality. In addition to the accurate graphic documentation carried out in the cave sector, this research broke down the advantages and disadvantages of the smartphone LiDAR technique and provided a series of recommendations for its use in this context. • Suitability evaluation of built-in smartphone LiDAR for cave digitisation. • The accuracy of three different mobile LiDAR applications was analysed. • Accurate graphic documentation of a sector of great relevance in La Pileta Cave. • A series of recommendations for using mobile LiDAR in cave archaeology are offered.

Destructive and control operations are today a major threat for cyber physical systems. These operations, known as Computer Network Attack (CNA), and usually linked to state-sponsored actors, are much less analyzed than Computer Network Exploitation activities (CNE), those related to intelligence gathering. While in CNE operations the main tactics and techniques are defined and well structured, in CNA there is a lack of such consensuated approaches. This situation hinders the modeling of threat actors, which prevents an accurate definition of control to identify and to neutralize malicious activities. In this paper, we propose the first global approach for CNA operations that can be used to map real-world activities. The proposal significantly reduces the amount of effort need to identify, analyze, and neutralize advanced threat actors targeting cyber physical systems. It follows a logical structure that can be easy to expand and adapt.

Industrial control systems and critical infrastructures have become an important target for cyber-crime, cyber-terrorism and industrial espionage. In order to protect these systems from cyber-attacks it is important to obtain accurate and up-to-date intelligence on cyber security threats. Honeypots are simulated systems, deliberately exposed on the Internet to attract the attention of cyber criminals, in order to observe attacks and gain intelligence. Whilst honeypots can be a very effective way of gathering intelligence, it is not trivial to simulate a realistic industrial control system, without raising the attacker’s suspicion. In this experience report, we describe the development of a honeypot, representing a water treatment plant, from the point of view of a cyber security service provider charged with the protection of critical infrastructure. The system has been continuously exposed and has provided intelligence for more than two years, feeding intelligence used in our monitoring toolchain and managed security services. (This work was partially supported by the Spanish Ministry for Industry, Energy and Tourism under grant number TSI-100200-2014-19 and the European Horizon 2020 Programme under grant agreement number 740477).

JavaScript (JS) is nowadays the only language that can be used to develop web-based client-server applications in both tiers, client and server. This makes it an interesting choice for developing distributed evolutionary computation experiments, but the best way from algorithmic and practical point of views is not clear, so we will compare different distributed EC architectures in JavaScript using NodEO, an open source JS framework released by us.

After more than fifteen years, JavaScript has finally risen as a popular language for implementing all kind of applications, from server-based to rich internet applications. The fact that it is implemented in the browser and in server-side tools makes it interesting for designing evolutionary algorithm frameworks that encompass both tiers, but besides, they allow a change in paradigm that goes beyond the canonical evolutionary algorithm. In this paper we will experiment with different architectures, client-server and peer to peer to assess which ones offer most advantages in terms of performance, scalability and ease of use for the computer scientist. All implementations have been released as open source, and besides showing that the concept of working with evolutionary algorithms in JavaScript can be done efficiently, we prove that a master-slave parallel architecture offers the best combination of time and algorithmic improvements in a parallel evolutionary algorithm that leverages JavaScript implementation features.

Solving the MasterMind puzzle, that is, finding out a hidden combination by using hints that tell you how close some strings are to that one is a combinatorial optimization problem that becomes increasingly difficult with string size and the number of symbols used in it. Since it does not have an exact solution, heuristic methods have been traditionally used to solve it; these methods scored each combination using a heuristic function that depends on comparing all possible solutions with each other. In this paper we first optimize the implementation of previous evolutionary methods used for the game of mastermind, obtaining up to a 40% speed improvement over them. Then we study the behavior of an entropy-based score, which has previously been used but not checked exhaustively and compared with previous solutions. The combination of these two strategies obtain solutions to the game of Mastermind that are competitive, and in some cases beat, the best solutions obtained so far. All data and programs have also been published under an open source license.

In this work we address the problem of inventory and routing management in a retail chain. This involves the minimisation of two contradicting objectives, inventory holding costs and transportation costs, but which can be compounded in to a single one, the global costs. In previous work we addressed this using a single objective evolutionary algorithm but the duality inherent in the problem prompts us to consider a multiobjective approach; the aim is to determine what advantages each can bring. A number of experiments are carried out on several simulated and one real retail chain.

This paper focuses on the iterative design of SofEA , an architecture for distributing evolutionary algorithms (EAs) across computer networks in an asynchronous and decentralized way. SofEA is based on a pool architecture implemented on an object store, allowing the asynchronous interaction with which several clients. The fact that each client is autonomous leads to complex behavior, which will be examined in the work, so that the design can be validated, rules of thumb can be extracted, and the limits of scalability can be found. In this paper we advance the design of an asynchronous, fault-tolerant, and hopefully scalable distributed EA based on the object store CouchDB. We do so by iteratively analyzing running time and average evaluations to solutions on increasingly better versions of the algorithm, looking for the best results, at least from the point of view of running time. By doing so, we increase speed almost fourfold, and also decrement the average number of evaluations to solution in some cases. Experiments have shown also which critical parameters have the bigger influence on the performance in this kind of systems: live population size and number of conflicts, with both being influenced by the number of clients and the size of the population block each client handles at a time. These experiments also show that there is a balance between scalability and fault tolerance, with scalability dropping when a certain number of clients is reached; further clients only increase fault tolerance, at least in the configurations we are using in this paper. The paper also shows that experimentation and measurement conform a good methodology for the design of this kind of asynchronous, heterogeneous and distributed systems, where analytic performance prediction is almost impossible.

This paper studies SofEA, an architecture for distributing evolutionary algorithms (EAs) across computer networks in an asynchronous and decentralized way. SofEA is based on a pool architecture which is implemented using an object store interacting asynchronously with several clients. The fact that each client is autonomous leads to a complex behavior that will be examined in this paper, so that the design can be validated, rules of thumb can be extracted and the limits of scalability found. We will show how, beyond the usual measures employed in EA, specific measures such as the number of conflicts across clients can give us hints on the algorithm behavior, and how implementation details can change not only the running time, but also the behavior of the evolutionary algorithm itself. By using these measures we try to find ideal values for parameters such as the simultaneous number of individuals evaluated by a client or the way these are chosen from the pool.