Use Of Evolutionary Computation Techniques Research Articles

Coevolution of Mobile Malware and Anti-Malware

Mobile malware is one of today's greatest threats in computer security. Furthermore, new mobile malware is emerging daily that introduces new security risks. However, while existing security solutions generally protect mobile devices against known risks, they are vulnerable to as yet unknown risks. How anti-malware software reacts to new, unknown malicious software is generally difficult to predict. Therefore, anti-malware software is in continuous development in order to be able to detect new malware or new variants of existing malware. Similarly, as long as anti-malware software develops, malware writers also develop their malicious code by using various evasion strategies, such as obfuscation and encryption. This is the lifecycle of malicious and anti-malware software. In this paper, the use of evolutionary computation techniques are investigated, both for developing new variants of mobile malware which successfully evades anti-malware systems based on static analysis and for developing better security solutions against them automatically. A coevolutionary arms race mechanism has always been considered a potential candidate for developing a more robust system against new attacks and for system testing. To the best of the authors' knowledge, this paper is the first application of coevolutionary computation to address this problem.

Application of evolutionary computation techniques for the identification of innovators in open innovation communities

Open innovation represents an emergent paradigm by which organizations make use of internal and external resources to drive their innovation processes. The growth of information and communication technologies has facilitated a direct contact with customers and users, which can be organized as open innovation communities through Internet. The main drawback of this scheme is the huge amount of information generated by users, which can negatively affect the correct identification of potentially applicable ideas. This paper proposes the use of evolutionary computation techniques for the identification of innovators, that is, those users with the ability of generating attractive and applicable ideas for the organization. For this purpose, several characteristics related to the participation activity of users though open innovation communities have been collected and combined in the form of discriminant functions to maximize their correct classification. The right classification of innovators can be used to improve the ideas evaluation process carried out by the organization innovation team. Besides, obtained results can also be used to test lead user theory and to measure to what extent lead users are aligned with the organization strategic innovation policies.

Evolutionary computation techniques for intrusion detection in mobile ad hoc networks

Intrusion detection on mobile ad hoc networks (MANETs) is difficult. This is because of their dynamic nature, the lack of central points, and their highly resource-constrained nodes. In this paper we explore the use of evolutionary computation techniques, particularly genetic programming and grammatical evolution, to evolve intrusion detection programs for such challenging environments. Cognizant of the particular importance of power efficiency we analyse the power consumption of evolved programs and employ a multi-objective evolutionary algorithm to discover optimal trade-offs between intrusion detection ability and power consumption.

Introduction. Editorial on ‘Signal processing in vital rhythms and signs’

Signal processing has become a core technology in medical devices today, as it is crucial in a wide range of applications. It is used to automate the measurement of various signal characteristics, which previously was done manually and, as a result, subjectivity is reduced and reliability is increased. Another purpose is to filter out undesired signal components of technical or physiological origin so that the signalto-noise ratio is improved and subsequent analysis facilitated. Signal processing is also of key importance when uncovering components with low amplitude and/or subtle variations in frequency, which are very difficult, if not impossible, to observe by the naked eye. For example, signal processing plays a crucial role for the detection of the alternans phenomenon occurring during cardiac repolarization. Information about alternans has turned out to be the most useful for risk stratification of certain cardiac patients. The analysis of heart rate variability represents another application where signal processing is essential for the characterization of tiny oscillations in rhythm; the resulting measurements convey unique information about the autonomic nervous system. Considering that today’s recording equipment and computers are quite powerful and available at affordable prices, limitations related to implementation are rather procedural than computational. The role of signal processing can therefore be expected to further increase in the future as novel techniques are developed and used to uncover and characterize hidden activity which may be present in a signal. Traditionally, biomedical signal processing has been largely synonymous to ‘unimodal’ analysis in which only one type of signal has been subjected to analysis at a time. Countless approaches have been developed to unimodal analysis of the most common bioelectrical signals, i.e. the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG). However, multimodal signal modelling and processing are currently receiving considerable attention as it is highly desirable to account for the interdependence that exists between different

An inductive approach to ecological time series modelling by evolutionary computation

Building time series models for ecological systems that can be physically interpreted is important both for understanding the dynamics of these natural systems and the development of decision support systems. This work describes the application of an evolutionary computation framework for the discovery of predictive equations and rules for phytoplankton abundance in freshwater lakes from time series data. The suggested framework evolves several different equations and rules, based on limnological and climate variables. The results demonstrate that non-linear processes in natural systems may be successfully modelled through the use of evolutionary computation techniques. Further, it shows that a grammar based genetic programming system may be used as a tool for exploring the driving processes underlying freshwater system dynamics.