ensemble-classifier
در نشریات گروه فناوری اطلاعات-
Journal of Artificial Intelligence, Applications, and Innovations, Volume:1 Issue: 3, Summer 2024, PP 31 -37
Today, smartphones are prevalent for personal and corporate use and have become the new personal computer due to their portability, ease of use, and functionality (such as video conferencing, Internet browsing, e-mail, continuous wireless and data connectivity, worldwide map location services, and countless mobile applications such as banking applications). On the other hand, we store many sensitive and private information daily on smart devices. This information is of interest to malicious writers who are developing malware to steal information from mobile devices. Unfortunately, the open source and widespread adoption of the Android operating system has made it the most targeted of the four popular mobile platforms by malware writers. Many researchers have tried to identify malware using program signatures, which have been successful to some extent. However, the signature cannot effectively identify new and unknown malware. For this reason, in this article, we propose a method that designs a machine-learning model for Android malware detection based on the properties of Permissions, Intents APKs. In this study, we evaluated more than 25,000 Android samples belonging to malware and trusted samples. Experimental results show the effectiveness of the proposed method by obtaining 96.27% accuracy.
Keywords: Malware Detection, Artificial Intelligence, Machine Learning, Anti-Malware, Android, Xgboost, Ensemble Classifier -
Recently by developing the technology, the number of network-based servicesis increasing, and sensitive information of users is shared through the Internet.Accordingly, large-scale malicious attacks on computer networks could causesevere disruption to network services so cybersecurity turns to a major concern fornetworks. An intrusion detection system (IDS) could be considered as anappropriate solution to address the cybersecurity. Despite the applying differentmachine learning methods by researchers, low accuracy and high False AlarmRate are still critical issues for IDS. In this paper, we propose a new approach forimproving the accuracy and performance of intrusion detection. The proposedapproach utilizes a clustering-based method for sampling the records, as well asan ensembling strategy for final decision on the class of each sample. For reducingthe process time, K-means clustering is done on the samples and a fraction of eachcluster is chosen. On the other hand, incorporating three classifiers includingDecision Tree (DT), K-Nearest-Neighbor (KNN) and Deep Learning in theensembling process results to an improved level of precision and confidence. Themodel is tested by different kinds of feature selection methods. The introducedframework was evaluated on NSL-KDD dataset. The experimental results yieldedan improvement in accuracy in comparison with other modelsKeywords: Intrusion Detection System, Ensemble Classifier, Clustering, Decision tree, KNearest-Neighbor, deep learning
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In recent years, data received from social media has increased exponentially. They have become valuable sources of information for many analysts and businesses to expand their business. Automatic document classification is an essential step in extracting knowledge from these sources of information. In automatic text classification, words are assessed as a set of features. Selecting useful features from each text reduces the size of the feature vector and improves classification performance. Many algorithms have been applied for the automatic classification of text. Although all the methods proposed for other languages are applicable and comparable, studies on classification and feature selection in the Persian text have not been sufficiently carried out. The present research is conducted in Persian, and the introduction of a Persian dataset is a part of its innovation. In the present article, an innovative approach is presented to improve the performance of Persian text classification. The authors extracted 85,000 Persian messages from the Idekav-system, which is a Telegram search engine. The new idea presented in this paper to process and classify this textual data is on the basis of the feature vector expansion by adding some selective features using the most extensively used feature selection methods based on Local and Global filters. The new feature vector is then filtered by applying the secondary feature selection. The secondary feature selection phase selects more appropriate features among those added from the first step to enhance the effect of applying wrapper methods on classification performance. In the third step, the combined filter-based methods and the combination of the results of different learning algorithms have been used to achieve higher accuracy. At the end of the three selection stages, a method was proposed that increased accuracy up to 0.945 and reduced training time and calculations in the Persian dataset.
Keywords: Feature Selection, Text Mining, Classification Accuracy, Machine Learning, Ensemble Classifier -
Web Services provides a solution to web application integration. Due to the significance of trust in choosing the proper web service, a novel optimal configuration of neural networks by multi-objective genetic algorithm and ensemble-classifier approach is used to evaluate the trust of single web services. For evaluating trust in the single web services, first, a set of neural networks were trained by the settings their parameters through the multi-objective genetic algorithm. Next, the best combination of neural networks was selected to make an ensemble classifier. This method was evaluated with single WS dataset considered eight criteria. Three measurements, accuracy, time and ROC curve were considered to assess the efficiency. Ultimately, the results show that the proposed approach can achieve a trade-off between time and accuracy by the multi-objective genetic algorithm. Also using ensemble-classifiers approach increases the reliability of the model. Consequently, the proposed method promote the detection accuracy.
Keywords: web service, Trust, Artificial Neural network, Multi-Objective Genetic Algorithm, Ensemble-Classifier
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