Dorylus: An Ant Colony Based Tool for Automated Test Case Generation

Automated test generation to cover all branches within a program is a hard task. We present Dorylus, a test suite generation tool that uses ant colony optimisation, guided by coverage. Dorylus constructs a continuous domain over which it conducts independent, multiple objective search that employs a lightweight, dynamic, path-based input dependency analysis. We compare Dorylus with EvoSuite with respect to both coverage and speed using two corpora. The first benchmark contains string based programs, where our results demonstrate that Dorylus improves over EvoSuite on branch coverage and is 50% faster on average. The second benchmark consists of 936 Java programs from SF110 and suggests Dorylus generalises well as it achieves 79% coverage on average whereas the best performing of three EvoSuite algorithms reaches 89%

Analysing temporal performance profiles of UAV operators using time series clustering

The continuing growth in the use of Unmanned Aerial Vehicles (UAVs) is causing an important social step forward in the performance of many sensitive tasks, reducing both human and economical risks. The work of UAV operators is a key aspect to guarantee the success of this kind of tasks, and thus UAV operations are studied in many research fields, ranging from human factors to data analysis and machine learning. The present work aims to describe the behaviour of operators over time using a profile-based model where the evolution of the operator performance during a mission is the main unit of measure. In order to compare how different operators act throughout a mission, we describe a methodology based of multivariate-time series clustering to define and analyse a set of representative temporal performance profiles. The proposed methodology is applied in a multi-UAV simulation environment with inexperienced operators, obtaining a fair description of the temporal behavioural patterns followed during the course of the simulation

A study on performance metrics and clustering methods for analyzing behavior in UAV operations

Unmanned Aerial Vehicles (UAVs) are starting to provide new possibilities to human societies and their demand is growing according to the new industrial application fields for these revolutionary tools. The current systems are still evolving, specially from an Artificial Intelligence perspective, which is increasing the different tasks that UAVs can perform. However, the current state still requires a strong human supervision. As a consequence, a good preparation for UAV operators is mandatory due to some of their applications might affect human safety. During the training process, it is important to measure the performance of these operators according to different factors that can help to decide what operators are more suitable for different kinds of missions creating operator profiles. Having this goal in mind, this work aims to present an extensive and robust methodology to automatically extract different performance profiles from the training process of operators in an UAV simulation environment. Our method combines the definition of a set of performance metrics with clustering techniques to define operators profiles, ensuring that the behavior discrimination is suitable and consistent

Exploring digital corporate social responsibility communications on Twitter

Many brands utilize social media to communicate with consumers, but are they taking advantage of these media's potential for co-creation? We explore this in the corporate social responsibility (CSR) context where online CSR dialogs form as brands interact with consumers using social media. Study 1 examines eight brands' digital CSR communications on Twitter and suggests these dialogs are present but are rarely part of the process with most interactions between their consumers. Study 2 assesses the brands' CSR relevant tweets' content and finds that most are not relevant to CSR and, moreover, are predominantly one-way. Therefore, both studies reveal that brands are not tapping into the potential for co-creation that is inherent in social media. Thus, we recommend that social media communications should include (a) mentions of individual consumers, (b) audience specific and relevant message content, and (c) opportunities for consumers to co-create value with the relevant brands

Genetic boosting classification for malware detection

In the last few years virus writers have made use of new obfuscation techniques with the aim of hindering malware in order to difficult their detection by Anti-Virus engines. Strategies to reverse this trend involve executing potentially malicious programs and monitor the actions they perform in runtime, what is known as dynamic analysis. In this paper we present a method able to reach a high accuracy rate without using this kind of analysis. Instead we use a static analysis approach, which discards those samples that cannot be classified with enough certainty and need, certainly, a dynamic analysis. The K-means clustering algorithm has been used to group samples into regions according to their features. Then a boosting process, guided by a genetic algorithm, is executed in each region that are evaluated using a test dataset discarding those regions which do not reach a minimum accuracy threshold

ADROIT: Android malware detection using meta-information

Android malware detection represents a current and complex problem, where black hats use different methods to infect users' devices. One of these methods consists in directly upload malicious applications to app stores, whose filters are not always successful at detecting malware, entrusting the final user the decision of whether installing or not an application. Although there exist different solutions for analysing and detecting Android malware, these systems are far from being sufficiently precise, requiring the use of third-party antivirus software which is not always simple to use and practical. In this paper, we propose a novel method called ADROIT for analysing and detecting malicious Android applications by employing meta-information available on the app store website and also in the Android Manifest. Its main objective is to provide a fast but also accurate tool able to assist users to avoid their devices to become infected without even requiring to install the application to perform the analysis. The method is mainly based on a text mining process that is used to extract significant information from meta-data, that later is used to build efficient and highly accurate classifiers. The results delivered by the experiments performed prove the reliability of ADROIT, showing that it is capable of classifying malicious applications with 93.67% accuracy

Medoid-based clustering using ant colony optimization

The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets

MOCDroid: multi-objective evolutionary classifier for Android malware detection

Malware threats are growing, while at the same time, concealment strategies are being used to make them undetectable for current commercial Anti-Virus. Android is one of the target architectures where these problems are specially alarming, due to the wide extension of the platform in different everyday devices.The detection is specially relevant for Android markets in order to ensure that all the software they offer is clean, however, obfuscation has proven to be effective at evading the detection process. In this paper we leverage third-party calls to bypass the effects of these concealment strategies, since they cannot be obfuscated. We combine clustering and multi-objective optimisation to generate a classifier based on specific behaviours defined by 3rd party calls groups. The optimiser ensures that these groups are related to malicious or benign behaviours cleaning any non-discriminative pattern. This tool, named MOCDroid, achieves an ac-curacy of 94.6% in test with 2.12% of false positives with real apps extracted from the wild, overcoming all commercial Anti-Virus engines from VirusTotal

Mechanisms and mechanics of cell competition in epithelia

When fast-growing cells are confronted with slow-growing cells in a mosaic tissue, the slow-growing cells are often progressively eliminated by apoptosis through a process known as cell competition. The underlying signalling pathways remain unknown, but recent findings have shown that cell crowding within an epithelium leads to the eviction of cells from the epithelial sheet. This suggests that mechanical forces could contribute to cell elimination during cell competition