Design of a Novel Information System for Semi-automated Management of Cybersecurity in Industrial Control Systems

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ameri, Kimia; Hempel, Michael; Sharif, Hamid; Lopez, Juan; Perumalla, Kalyan

Abstract: There is an urgent need in many critical infrastructure sectors, including the e ... Expand

Abstract: There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this article presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. We developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports. Collapse

Topics: IT security database system website Python electronic authentication

Methods: computational algorithm sentiment analysis natural language processing BERT language model

Hey Article, What Are You About? Question Answering for Information Systems Articles through Transformer Models for Long Sequences

2023 | HICSS | Citations: 0

Authors: Ebert, L.; Huettemann, S.; Mueller, R.

Abstract: Question Answering (QA) systems can significantly reduce manual effort of searc ... Expand

Abstract: Question Answering (QA) systems can significantly reduce manual effort of searching for relevant information. However, challenges arise from a lack of domain-specificity and the fact that QA systems usually retrieve answers from short text passages instead of long scientific articles. We aim to address these challenges by (1) exploring the use of transformer models for long sequence processing, (2) performing domain adaptation for the Information Systems (IS) discipline and (3) developing novel techniques by performing domain adaptation in multiple training phases. Our models were pre-trained on a corpus of 2 million sentences retrieved from 3,463 articles from the Senior Scholars' Basket and fine-tuned on SQuAD and a manually created set of 500 QA pairs from the IS field.In six experiments, we tested two transfer learning techniques for fine-tuning (TANDA and FANDO). The results show that fine-tuning with task-specific domain knowledge considerably increases the models' Collapse

Topics: Wikipedia online learning system technology adoption IS discipline crowdsourcing

Methods: experiment machine learning deep learning computational algorithm transformer model

Theories: adaptive structuration theory structuration theory

Extraction of Forward-looking Financial Information for Stock Price Prediction from Annual Reports Using NLP Techniques

2023 | HICSS | Citations: 0

Authors: Glodd, Alexander; Hristova, Diana

Abstract: Annual reports are one of the most important sources of information for financia ... Expand

Abstract: Annual reports are one of the most important sources of information for financial decisions. They contain forward-looking statements (FLS), which describe future trends and expectations. Thus, several studies deal with the automated identification of FLS, where the latest ones involve a combination of a rule-based approach and machine learning classification. In this paper, we extend this research with state-of-the-art NLP methods. We use DistilBERT for FLS identification and determine their sentiment with FinBERT. The result is processed by a Random Forest model for stock price growth prediction of different periods. Our evaluation shows that DestilBERT achieves higher accuracies on FLS identification than existing methods. For short-term stock price rate prediction, the extracted FLS information together with historical stock data outperforms the sole use of historical stock data. For mid-term prediction, using FLS alone with DestilBERT shows the best result. Finally, in the long-term, FLS provide no benefit. Collapse

Topics: Python decision making

Methods: BERT support vector machine natural language processing DistilBERT literature study

Generative AI in Idea Development: The Role of Numeric and Visual Feedback

2023 | International Conference on Information Systems | Citations: 0

Authors: Gordetzki, Philipp; Blohm, Ivo; Hofstetter, Reto

Abstract: ... Expand

Abstract: Collapse

Topics: artificial intelligence innovation management generative AI human computer interaction human AI collaboration

Methods: experiment statistical hypothesis test experimental group survey experimental design

Theories: media synchronicity theory dual-coding theory

Detection of Stock Manipulation Influencer Content using Supervised Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Haase, Frederic; Rath, Oliver; Lauten, Julia; Schoder, Detlef

Abstract: In recent years, social media influencers have emerged as key players in stock ... Expand

Abstract: In recent years, social media influencers have emerged as key players in stock manipulation schemes. Despite their growing impact, methods to detect such activities remain scarcely explored. In this study, we examine the social media content of stock manipulation influencers (SMIs) implicated in a $100 million fraud case by the U.S. Securities and Exchange Commission (SEC) in 2022. Leveraging natural language processing (NLP) techniques, we first investigate the linguistic characteristics present in the social media content published by SMIs. Next, we develop and evaluate supervised learning models to detect manipulative content. Our results have significant implications for investors, regulators, and the broader financial community. They reveal the unique linguistic characteristics of SMI content and demonstrate the potential of machinelearning and deep-learning-based techniques in advancing fraud detection systems. Collapse

Topics: Twitter social media fraud detection database system social media analytics

Methods: machine learning RoBERTa DistilBERT computational algorithm natural language processing

A deep multi-embedding model for mobile application recommendation

2023 | Decision Support Systems | Citations: 0

Authors: Liu, Yi-Hung; Chen, Yen-Liang; Chang, Po-Ya

Abstract: With the explosion in the number of mobile applications, it becomes difficult an ... Expand

Abstract: With the explosion in the number of mobile applications, it becomes difficult and time-consuming for users to find the most suitable and interesting application from the millions of applications that exist today. Therefore, mobile app recommendation becomes a top priority to help people solve this problem. In previous studies, there are two common methods to solve this problem, one is to apply matrix factorization to the rating data to predict the target user's ratings for other applications, and the other is to predict ratings based on the user's review text. In addition to combining the above two traditional methods, this study also combines deep learning methods to generate better models. For rating data, we use a deep matrix factorization method to obtain latent factor vectors of users and items from the rating matrix to improve the embedding of ratings. For review text, we improve the text representation by using a multi-embedding approach. Most importantly, our model also incorporates deep neural networks with nonlinear transformation capabilities to extract more representative abstract features. We conduct experiments on datasets collected from the real world. Extensive experimental results show that our proposed method has better performance than other existing methods. Collapse

Topics: mobile application mobile application market recommender system digital distribution mobile system

Methods: BERT deep learning experiment sensitivity analysis artificial neural network

Is a Pretrained Model the Answer to Situational Awareness Detection on Social Media?

2023 | HICSS | Citations: 0

Authors: Lo, S.; Lee, K.; Zhang, Y.

Abstract: Social media can be valuable for extracting information about an event or incid ... Expand

Abstract: Social media can be valuable for extracting information about an event or incident on the ground. However, the vast amount of content shared, and the linguistic variants of languages used on social media make it challenging to identify important situational awareness content to aid in decision-making for first responders. In this study, we assess whether pretrained models can be used to address the aforementioned challenges on social media. Various pretrained models, including static word embedding (such as Word2Vec and GloVe) and contextualized word embedding (such as DistilBERT) are studied in detail. According to our findings, a vanilla DistilBERT pretrained language model is insufficient to identify situation awareness information. Fine-tuning by using datasets of various event types and vocabulary extension is essential to adapt a DistilBERT model for real-world situational awareness detection. Collapse

Topics: Twitter situational awareness social media Wikipedia wiki

Methods: BERT DistilBERT support vector machine machine learning word embedding

Fake Review Detection - The Value of Domain-Specificity

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Theuerkauf, René; Peters, Ralf

Abstract: The issue of fake reviews, a perennial challenge faced by e-commerce companies, ... Expand

Abstract: The issue of fake reviews, a perennial challenge faced by e-commerce companies, has been worsened in recent years. In this paper, we investigate the trade-off between the application of general-purpose and domain-specific model by selecting training data using transformer-based models to identify fake reviews. Therefore, we compare two scenarios using different set ups of data selection. First, a general-purpose model was identified and applied on specific domains. Afterwards, domain-specific models were trained and tested. Then, the results from these scenarios were compared, yielding the conclusion that models trained on data from a similar product category outperform general-purpose models in classification performance up to 21% (on average by 4%). Our findings send an important message to e-commerce companies to rethink their strategy on training general-purpose models to identify fake reviews to create several, more domainspecific models on that task according to present data domains. Collapse

Topics: fake review online review Facebook electronic commerce recommender system

Methods: RoBERTa DeBERTa BERT transformer model Cross-Industry Standard Process for Data Mining

sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics

2023 | Information Systems Research | Citations: 0

Authors: Yang, Yi; Zhang, Kunpeng; Fan, Yangyang

Abstract: Topic modeling methods such as latent Dirichlet allocation (LDA) are powerful to ... Expand

Abstract: Topic modeling methods such as latent Dirichlet allocation (LDA) are powerful tools for analyzing massive amounts of textual data. They have been used extensively in information systems (IS) and business discipline research to identify latent topics for data exploration and as a feature engineering mechanism to derive new variables for analyses. However, existing topic modeling approaches are mostly unsupervised and only leverage textual data, while ignoring additional useful metadata often associated with text, such as star ratings in customer reviews or categories of posts in online forums. As a result, the identified topics and variables derived based on the learned topic model may not be accurate, which could lead to incorrect estimations that affect subsequent empirical analysis and to inferior performance on predictive tasks. In this study, we propose a novel supervised deep topic modeling approach called sDTM, which combines a neural variational autoencoder model and a recurrent neural network. sDTM leverages the auxiliary data associated with text to enhance the topic modeling capability. We conduct empirical case studies and predictive analytics on an online consumer review data set and an online knowledge community data set. Experimental results show that in comparison with benchmark methods, sDTM can enhance both the empirical estimation and predictive performance. sDTM makes methodological contributions to the IS literature and has direct relevance for research using text analytics.History: Alok Gupta, Senior Editor; Huimin Zhao, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1124. Collapse

Topics: online review open source user-generated content Wikipedia accounting

Methods: topic model deep learning machine learning BERT data transformation

A deep learning approach for detecting fake reviewers: Exploiting reviewing behavior and textual information

2023 | Decision Support Systems | Citations: 0

Authors: Zhang, Dong; Li, Wenwen; Niu, Baozhuang; Wu, Chong

Abstract: Ensuring the credibility of online consumer reviews (OCRs) is a growing societal ... Expand

Abstract: Ensuring the credibility of online consumer reviews (OCRs) is a growing societal concern. However, the problem of fake reviewers on online platforms significantly influences e-commerce authenticity and consumer trust. Existing studies for fake reviewer detection mainly focus on deriving novel behavioral and linguistic features. These features require extensive human labor and expertise, placing a heavy burden on platforms. Therefore, we propose a novel end-to-end framework to detect fake reviewers based on behavior and textual information. It has two key components: (1) a behavior-sensitive feature extractor that learns the underlying patterns of reviewing behavior; (2) a context-aware attention mechanism that extracts valuable features from online reviews. We rigorously evaluate each proposed module and the entire framework against state-of-the-art benchmarks on two real-world datasets from http://Yelp.com. Experimental results demonstrate that our method achieves state-ofthe-art results on fake reviewer detection. Our method can be considered a tentative step toward lowering human labor costs in realizing automated fake reviewer detection on e-commerce platforms. Collapse

Topics: fake review online review electronic commerce deception detection shapley additive explanation

Methods: machine learning digital trace data experiment parametric test deep learning