Applying artificial intelligence and machine learning to analyzing network traffic has the potential to be transformative in protecting organizations from cyber threats. Intrusion detection systems (IDS) are historically rule-based; however, they could be improved. Applying machine learning in the form of Anomaly Detection could be the next step in preventing cyber threats from causing malicious activity on the network. Two algorithms that are implemented in anomaly detection through the use of Amazon SageMaker are Random Cut Forest (RCF) and XGBoost. The data for this project are the training and testing data set provided by the UNSW-15 data set. The models are created using the Jupiter Notebook on the Amazon SageMaker Studio Lab platform. The models were tested using the metrics of accuracy, precision, recall, and F1 score. The best-performing model was the XGBoost model, with an accuracy of 61.83%. The recall for this model was 96.49%, and the f1 score was 73.24%.

Containerization is becoming an increasingly common aspect of DevOps. Adding a container layer increases the complexity and could impact system performance. This study explores the performance differences of the Apache and Nginx web servers on Virtual Machines (VMs) and Docker Containers with official web server images from Docker Hub. A sandbox environment was created with both containerized and non-containerized versions of the web servers, and their performance was analyzed using line graphs. The results showed differences in performance between VMs and Docker Containers, with some variation from previous research due to the virtualization being done locally rather than on the cloud. This study would be advantageous for organizations with on-premises infrastructure due to security or governing regulations.

Machine learning classifiers are known algorithms used to classify network intrusion detection due to the drastic growth of data, new tools are being required to handle such a large amount of data within a short time frame. In this Paper, we present a Model using the Apache Mahout Framework to train machine learning classifiers Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB) on CSE-CIC-IDS2018 dataset using Chi-Square and ANOVA f-test filter-based feature selection technique on an Apache Hadoop Framework. The performance of classifiers is measured in terms of Accuracy, Kappa, Precision, Recall, and Fl-Score for a comparative analysis of the various machine learning classifiers.

As many artificial neural libraries are developing the deep learning algorithm and implementing it became accessible to anyone. This study points out the disparity of performance in deep learning models such as convolutional neural networks (CNN) when implemented with different artificial neural libraries. Libraries such as Keras, Pytorch, and MXnet was utilized for each three CNN model then binary image classification was done based on the Dogs vs. Cats dataset from Kaggle. With using 75% of the dataset as the training set and the rest of 25% as a testing set, and as a result, each CNN model gave a different F1 score value and accuracy.

Knowledge portals are a method to provide integrated access to users of multiple systems through a single-entry point. A large body of literature exists on knowledge portals; however, the only published literature reviews are outdated, as they only cover material prior to the 21st century. The purpose of this article is to present review on some major papers about knowledge portals that were published from 2000-2017. The review takes a holistic perspective based on systems development life cycle to critique the literature and identifies key challenges that enlighten future directions. Trends in the first decade of the 21st century include the desire to formalize and standardize a model of knowledge portals, while major challenges for the future include the need to maintain cybersecurity across users and platforms.

Data science is a relatively new and emerging field with strong job growth projections. In this work, we develop a new theoretical model based on the theory of planned behavior and the IS Success Model in order to understand public perceptions about data science. Specifically, we aim to determine the potential impact and if the public views data science as beneficial to organizations and society and whether this in turn leads to an intent to use data science. In order to answer the aforementioned questions, we develop a definition of data science derived from current, state-of-the-art literature. Next, we test our theoretical model via a survey instrument that adapts relevant constructs from academic literature. Results indicate support for our model and subsequent hypotheses which show that information quality and system quality impact social norms and behavioral control which in turn influences perceived benefits of data science which influences the intent to use data science. Our model can be employed to advance the adoption of data science as a tool for business and data driven decision-making as well as position academia to train future generations of data scientists.

Distribution of deceptive counterfeit goods via online marketplaces such as Amazon and eBay has introduced a particularly burdensome decision making process for the consumers. The consumers need to spend additional time in the information search step, reading product and seller reviews to assist with counterfeit detection. Automated counterfeit detection could assist with this process. This paper presents the conceptual framework that employs artificial intelligence techniques, such as natural language processing and topic analysis, in order to automatically detecting counterfeit goods. Specifically, online reviews of products and sellers can be downloaded and parsed using natural language processing. Topic analysis methods can be performed against the resulting text corpus to detect the most frequent terms in the reviews and to examine the reviews for a collection of keywords related to fraudulent products. The implications of this research are to alert consumers to potentially counterfeit products thereby increasing trust and efficiency in the online marketplace.

There has been a plethora of research in the area of knowledge portals, knowledge warehouses, ontologies, ontology creation and mapping, as well as the automatic creation and mapping of ontologies. While research exists in each respective area there is a lack of conceptual models that will integrate ontologies as a tool for disparate data source integration into knowledge portals or enterprise knowledge warehouses. The purpose of this work is to discuss different tools that have been developed in academic research and provide a conceptual model of how to implement these tools in relation to knowledge portals and warehouses.