Static Analysis of Aspect Interaction and Composition in Component Models

International audienceComponent based software engineering and aspect orientation are claimed to be two complementary approaches. While the former ensures the modularity and the reusability of software entities, the latter enables the modularity of crosscutting concerns that cannot be modularized by regular components. Nowadays, several approaches and frameworks are dedicated to integrate aspects into component models. However, when several aspects are woven, interferences may appear which results on undesirable behaviors. The contribution of this paper is twofold. First, we show how aspectualized component models can be formally modeled in Uppaal model checker in order to detect potential interferences among aspects. Second, we provide an extendible catalog of composition operators used for aspect composition. We illustrate our general approach with an airport Internet service example

Web page phishing detection

The provided dataset includes 11430 URLs with 87 extracted features. The dataset are designed to be used as a a benchmark for machine learning based phishing detection systems. Features are from three different classes: 56 extracted from the structure and syntax of URLs, 24 extracted from the content of their correspondent pages and 7 are extracetd by querying external services. The datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs. Associated to the dataset, we provide Python scripts used for the extraction of the features for potential replication or extension. dataset_A: contains a list a URLs together with their DOM tree objects that can be used for replication and experimenting new URL and content-based features overtaking short-time living of phishing web pages. dataset_B: containes the extracted feature values that can be used directly as inupt to classifiers for examination. Note that the data in this dataset are indexed with URLs so that one need to remove the index before experimentation. Datasets are constructed on May 2020. Due to huge size of dataset A, only a sample of the dataset is provided, I will try to divide into sample files and upload them one by one, for full copy, please contact directly the author at any time at: [email protected]

Web page phishing detection

The provided dataset includes 11430 URLs with 87 extracted features. The dataset are designed to be used as a a benchmark for machine learning based phishing detection systems. Features are from three different classes: 56 extracted from the structure and syntax of URLs, 24 extracted from the content of their correspondent pages and 7 are extracetd by querying external services. The datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs. Associated to the dataset, we provide Python scripts used for the extraction of the features for potential replication or extension. Datasets are constructed on May 2020.dataset_A: contains a list a URLs together with their DOM tree objects that can be used for replication and experimenting new URL and content-based features overtaking short-time living of phishing web pages.dataset_B: containes the extracted feature values that can be used directly as inupt to classifiers for examination. Note that the data in this dataset are indexed with URLs so that one need to remove the index before experimentation.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Web page phishing detection

The provided dataset includes 11430 URLs with 87 extracted features. The dataset are designed to be used as a a benchmark for machine learning based phishing detection systems. Features are from three different classes: 56 extracted from the structure and syntax of URLs, 24 extracted from the content of their correspondent pages and 7 are extracetd by querying external services. The datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs. Associated to the dataset, we provide Python scripts used for the extraction of the features for potential replication or extension. dataset_A: contains a list a URLs together with their DOM tree objects that can be used for replication and experimenting new URL and content-based features overtaking short-time living of phishing web pages. dataset_B: containes the extracted feature values that can be used directly as inupt to classifiers for examination. Note that the data in this dataset are indexed with URLs so that one need to remove the index before experimentation. Datasets are constructed on May 2020. Due to huge size of dataset A, only a sample of the dataset is provided, I will try to divide into sample files and upload them one by one, for full copy, please contact directly the author at any time at: [email protected]

Multi-language Webshell dataset

This dataset contains a collection of benign and malicious webshells. The source of these webshells are mainly collected from Github and open source projects. It is made available for replication of our ongoing work on the automatic detection of webshells. The list of webshells is filtered using MD5 for all languages and VLD for PHP language

Semantic Scholar Evaluation

The dataset is constructed for a project that investigates the coverage and the role of Semantic Scholar (S2) search engine in condunting secondary studies in software engineering.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Semantic Scholar Evaluation

The dataset is constructed for a project that investigates the coverage and the role of Semantic Scholar (S2) search engine in condunting secondary studies in software engineering.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV