Bucur D.,University of Twente |
Expert Systems with Applications | Year: 2017
Increasingly more digital communication is routed among wireless, mobile computers over ad-hoc, unsecured communication channels. In this paper, we design two stochastic search algorithms (a greedy heuristic, and an evolutionary algorithm) which automatically search for strong insider attack methods against a given ad-hoc, delay-tolerant communication protocol, and thus expose its weaknesses. To assess their performance, we apply the two algorithms to two simulated, large-scale mobile scenarios (of different route morphology) with 200 nodes having free range of movement. We investigate a choice of two standard attack strategies (dropping messages and flooding the network), and four delay-tolerant routing protocols: First Contact, Epidemic, Spray and Wait, and MaxProp. We find dramatic drops in performance: replicative protocols (Epidemic, Spray and Wait, MaxProp), formerly deemed resilient, are compromised to different degrees (delivery rates between 24% and 87%), while a forwarding protocol (First Contact) is shown to drop delivery rates to under 5% — in all cases by well-crafted attack strategies and with an attacker group of size less than 10% the total network size. Overall, we show that the two proposed methods combined constitute an effective means to discover (at design-time) and raise awareness about the weaknesses and strengths of existing ad-hoc, delay-tolerant communication protocols against potential malicious cyber-attacks. © 2017 Elsevier Ltd
Bucur D.,University of Groningen |
Iacca G.,INCAS3 |
Squillero G.,Polytechnic University of Turin |
Tonda A.,French National Institute for Agricultural Research
Applied Soft Computing Journal | Year: 2014
The analysis of worst-case behavior in wireless sensor networks is an extremely difficult task, due to the complex interactions that characterize the dynamics of these systems. In this paper, we present a new methodology for analyzing the performance of routing protocols used in such networks. The approach exploits a stochastic optimization technique, specifically an evolutionary algorithm, to generate a large, yet tractable, set of critical network topologies; such topologies are then used to infer general considerations on the behaviors under analysis. As a case study, we focused on the energy consumption of two well-known ad hoc routing protocols for sensor networks: the multi-hop link quality indicator and the collection tree protocol. The evolutionary algorithm started from a set of randomly generated topologies and iteratively enhanced them, maximizing a measure of "how interesting" such topologies are with respect to the analysis. In the second step, starting from the gathered evidence, we were able to define concrete, protocol-independent topological metrics which correlate well with protocols' poor performances. Finally, we discovered a causal relation between the presence of cycles in a disconnected network, and abnormal network traffic. Such creative processes were made possible by the availability of a set of meaningful topology examples. Both the proposed methodology and the specific results presented here - that is, the new topological metrics and the causal explanation - can be fruitfully reused in different contexts, even beyond wireless sensor networks. © 2013 Elsevier B.V.
Van Diest M.,INCAS3 |
Van Diest M.,University of Groningen |
Lamoth C.J.,University of Groningen |
Stegenga J.,INCAS3 |
And 3 more authors.
Journal of NeuroEngineering and Rehabilitation | Year: 2013
Fall injuries are responsible for physical dysfunction, significant disability, and loss of independence among elderly. Poor postural control is one of the major risk factors for falling but can be trained in fall prevention programs. These however suffer from low therapy adherence, particularly if prevention is the goal. To provide a fun and motivating training environment for elderly, exercise games, or exergames, have been studied as balance training tools in the past years. The present paper reviews the effects of exergame training programs on postural control of elderly reported so far. Additionally we aim to provide an in-depth discussion of technologies and outcome measures utilized in exergame studies. Thirteen papers were included in the analysis. Most of the reviewed studies reported positive results with respect to improvements in balance ability after a training period, yet few reached significant levels. Outcome measures for quantification of postural control are under continuous dispute and no gold standard is present. Clinical measures used in the studies reviewed are well validated yet only give a global indication of balance ability. Instrumented measures were unable to detect small changes in balance ability as they are mainly based on calculating summary statistics, thereby ignoring the time-varying structure of the signals. Both methods only allow for measuring balance after the exergame intervention program. Current developments in sensor technology allow for accurate registration of movements and rapid analysis of signals. We propose to quantify the time-varying structure of postural control during gameplay using low-cost sensor systems. Continuous monitoring of balance ability leaves the user unaware of the measurements and allows for generating user-specific exergame training programs and feedback, both during one game and in timeframes of weeks or months. This approach is unique and unlocks the as of yet untapped potential of exergames as balance training tools for community dwelling elderly. © 2013 van Diest et al.; licensee BioMed Central Ltd.
Bucur D.,University of Groningen |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016
We live in a world of social networks. Our everyday choices are often influenced by social interactions. Word of mouth, meme diffusion on the Internet, and viral marketing are all examples of how social networks can affect our behaviour. In many practical applications, it is of great interest to determine which nodes have the highest influence over the network, i.e., which set of nodes will, indirectly, reach the largest audience when propagating information. These nodes might be, for instance, the target for early adopters of a product, the most influential endorsers in political elections, or the most important investors in financial operations, just to name a few examples. Here, we tackle the NP-hard problem of influence maximization on social networks by means of a Genetic Algorithm. We show that, by using simple genetic operators, it is possible to find in feasible runtime solutions of high-influence that are comparable, and occasionally better, than the solutions found by a number of known heuristics (one of which was previously proven to have the best possible approximation guarantee, in polynomial time, of the optimal solution). The advantages of Genetic Algorithms show, however, in them not requiring any assumptions about the graph underlying the network, and in them obtaining more diverse sets of feasible solutions than current heuristics. © Springer International Publishing Switzerland 2016.
Zamani M.,University of California at Los Angeles |
Pola G.,University of L'Aquila |
Mazo Jr. M.,INCAS3 |
Mazo Jr. M.,University of Groningen |
Tabuada P.,University of California at Los Angeles
IEEE Transactions on Automatic Control | Year: 2012
Finite-state models of control systems were proposed by several researchers as a convenient mechanism to synthesize controllers enforcing complex specifications. Most techniques for the construction of such symbolic models have two main drawbacks: either they can only be applied to restrictive classes of systems, or they require the exact computation of reachable sets. In this paper, we propose a new abstraction technique that is applicable to any nonlinear sampled-data control system as long as we are only interested in its behavior in a compact set. Moreover, the exact computation of reachable sets is not required. The effectiveness of the proposed results is illustrated by synthesizing a controller to steer a vehicle. © 2011 IEEE.
De Melo V.V.,University of Sao Paulo |
Expert Systems with Applications | Year: 2014
In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. The presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the use of specific techniques to handle fitness landscapes which generally show complex properties. In this paper, we introduce a modified Covariance Matrix Adaptation Evolution Strategy (CMA-ES) specifically designed for solving constrained optimization problems. The proposed method makes use of the restart mechanism typical of most modern variants of CMA-ES, and handles constraints by means of an adaptive penalty function. This novel CMA-ES scheme presents competitive results on a broad set of benchmark functions and engineering problems, outperforming most state-of-the-art algorithms as for both efficiency and constraint handling. © 2014 Elsevier Ltd. All rights reserved.
Bosman H.H.W.J.,TU Eindhoven |
Liotta A.,TU Eindhoven |
Iacca G.,INCAS3 |
Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013 | Year: 2013
Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feed forward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS). The proposed implementation is tested extensively with generated and real-world datasets, and compared with RLS, Linear Least Squares Estimation, and a rule-based method as benchmarks. The methods are evaluated on the prediction accuracy and on the detection of anomalies. The experimental results demonstrate that fixed-point OS-ELM can be successfully implemented on resource-limited embedded systems, with guarantees of numerical stability. Furthermore, the detection accuracy of fixed-point OS-ELM shows better generalization properties in comparison with, for instance, fixed-point RLS. © 2013 IEEE.
Bosman H.H.W.J.,TU Eindhoven |
Liotta A.,TU Eindhoven |
Iacca G.,INCAS3 |
Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 | Year: 2013
The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. Lightweight online on-mote processing may improve the usage of their limited resources, such as energy, by transmitting only unexpected sensor data (anomalies). We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly. We evaluate the use of fixed-point RLS with adaptive thresholding, and its application to anomaly detection in embedded systems. We present an extensive experimental campaign on generated and real-world datasets, with floating-point RLS, LLSE, and a rule-based method as benchmarks. The methods are evaluated on prediction accuracy of the models, and on detection of anomalies, which are injected in the generated dataset. The experimental results show that the proposed algorithm is comparable, in terms of prediction accuracy and detection performance, to the other LS methods. However, fixed-point RLS is efficiently implementable in embedded devices. The presented method enables online on-mote anomaly detection with results comparable to offline LS methods. © 2013 IEEE.
Veloso De Melo V.,Federal University of São Paulo |
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - FOCI 2014: 2014 IEEE Symposium on Foundations of Computational Intelligence, Proceedings | Year: 2015
Constraint optimization problems play a crucial role in many application domains, ranging from engineering design to finance and logistics. Specific techniques are therefore needed to handle complex fitness landscapes characterized by multiple constraints. In the last decades, a number of novel meta-heuristics have been applied to constraint optimization. Among these, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has been attracting lately the most attention of researchers. Recent variants of CMA-ES showed promising results on several benchmarks and practical problems. In this paper, we attempt to improve the performance of an adaptive penalty CMA-ES recently proposed in the literature. We build upon it a 2-stage memetic framework, coupling the CMA-ES scheme with a local optimizer, so that the best solution found by CMA-ES is used as starting point for the local search. We test, separately, the use of three classic local search algorithms (Simplex, BOBYQA, and L-BFGS-B), and we compare the baseline scheme (without local search) and its three memetic variants with some of the state-of-the-art methods for constrained optimization. © 2014 IEEE.
De Roo F.,INCAS3 |
Mazo Jr. M.,Technical University of Delft
Proceedings of the IEEE Conference on Decision and Control | Year: 2013
In the present paper we provide a methodology to approximately solve complex optimal control problems by using symbolic models. It has been shown in recent years that symbolic models (also called discrete abstractions) offer a convenient approach to deal with the analysis and control of complex qualitative control problems. We show how the notion of approximate simulation enables also the transfer of quantitative information between a given control system and its symbolic model. In particular, we show that quantities computed on the symbolic model provide lower and upper bounds for the optimal achievable cost of the control system. Finally, we indicate how the theoretical results may be applied to automatically synthesize controllers for qualitative specifications, given in a fragment of Linear Temporal Logics, and simultaneously guaranteeing some lower and upper bounds for the attainable cost. ©2013 IEEE.