18,797 research outputs found
A Machine-learning Based Ensemble Method For Anti-patterns Detection
Anti-patterns are poor solutions to recurring design problems. Several
empirical studies have highlighted their negative impact on program
comprehension, maintainability, as well as fault-proneness. A variety of
detection approaches have been proposed to identify their occurrences in source
code. However, these approaches can identify only a subset of the occurrences
and report large numbers of false positives and misses. Furthermore, a low
agreement is generally observed among different approaches. Recent studies have
shown the potential of machine-learning models to improve this situation.
However, such algorithms require large sets of manually-produced training-data,
which often limits their application in practice. In this paper, we present
SMAD (SMart Aggregation of Anti-patterns Detectors), a machine-learning based
ensemble method to aggregate various anti-patterns detection approaches on the
basis of their internal detection rules. Thus, our method uses several
detection tools to produce an improved prediction from a reasonable number of
training examples. We implemented SMAD for the detection of two well known
anti-patterns: God Class and Feature Envy. With the results of our experiments
conducted on eight java projects, we show that: (1) our method clearly improves
the so aggregated tools; (2) SMAD significantly outperforms other ensemble
methods.Comment: Preprint Submitted to Journal of Systems and Software, Elsevie
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System
Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier
BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology
This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software
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