On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

A survey towards an integration of big data analytics to big insights for value-creation

Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier

An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture

Text mining has become one of the trendy fields that has been incorporated in several research fields such as computational linguistics, Information Retrieval (IR) and data mining Natural Language Processing (NLP) techniques were used to extract knowledge from the textual text that is written by human beings Text mining reads an unstructured form of data to provide meaningful information patterns in a shortest time period Social networking sites are a great source of communication as most of the people in today’s world use these sites in their daily lives to keep connected to each other It becomes a common practice to not write a sentence with correct grammar and spelling This practice may lead to different kinds of ambiguities like lexical, syntactic, and semantic and due to this type of unclear data, it is hard to find out the actual data order Accordingly, we are conducting an investigation with the aim of looking for different text mining methods to get various textual orders on social media websites This survey aims to describe how studies in social media have used text analytics and text mining techniques for the purpose of identifying the key themes in the data This survey focused on analyzing the text mining studies related to Facebook and Twitter; the two dominant social media in the world Results of this survey can serve as the baselines for future text mining research

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A survey of text mining in social media: Facebook and Twitter perspectives

Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection

Data mining techniques in social media

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.

Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection

The continued ability to detect malicious network intrusions has become an exercise in scalability, in which data mining techniques are playing an increasingly important role. We survey and categorize the fields of data mining and intrusion detection systems, providing a systematic treatment of methodologies and techniques. We apply a criterion-based approach to select 95 relevant articles from 2007 to 2017. We identified 19 separate data mining techniques used for intrusion detection, and our analysis encompasses rich information for future research based on the strengths and weaknesses of these techniques. Furthermore, we observed a research gap in establishing the effectiveness of classifiers to identify intrusions in modern network traffic when trained with aging data sets. Our review points to the need for more empirical experiments addressing real-time solutions for big data against contemporary attacks.

Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review

Machine learning has been leveraged to increase the effectiveness of intrusion detection systems (IDSs). The focus of this approach, however, has largely be on detecting known attack patterns based on outdated datasets. In this paper, we propose an ensemble feature selection method along with an anomaly detection method that combines unsupervised and supervised machine learning techniques to classify network traffic to identify previously unseen attack patterns. To that end, three different feature selection techniques are used as part of an ensemble model that selects 8 common features. Moreover, k-Means clustering is used to first partition the training instances into k clusters using the Manhattan distance. A classification model is then built based on the resulting clusters, which represent a density region of normal or anomaly instances. This in turn helps determine the effectiveness of the clustering in detecting unknown attack patterns within the data. The performance of our classifier is evaluated using the Kyoto dataset, which was collected between 2006 and 2015. To our knowledge, no previous work proposed such a framework that combines unsupervised and supervised machine learning approaches using this dataset. Experimental results show the effectiveness of the proposed framework in detecting previously unseen attack patterns compared to the traditional classification approach.

Fadi Salo

Papers

Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection

Data mining techniques in social media

Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection

Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review

Clustering Enabled Classification using Ensemble Feature Selection for Intrusion Detection