How “What you think you know about cybersecurity” can help users make more secure decisions

2023 | Information & Management | Citations: 0

Authors: Fard Bahreini, Amir; Cavusoglu, Hasan; Cenfetelli, Ronald T.

Abstract: The increasing use of information technology artifacts in daily life makes secur ... Expand

Abstract: The increasing use of information technology artifacts in daily life makes security a shared responsibility of both users and companies. In recent years, increasing a user’s objective (i.e., actual) security knowledge and providing applications with more secure default settings appear among the most ubiquitous tools companies use to broaden their efforts to help users make more secure decisions. Examining both solutions matters because they are widely used, cost effective, and understood by many security practitioners. Additionally, default settings and users’ objective knowledge provide anchors for decision-making. However, human errors and insecure default settings are increasing and raising further questions about the efficacy of such efforts. Using the theory of bounded ra­ tionality, we investigated the role of objective, subjective (i.e., self-assessed) security knowledge, and default settings security level on the overall decision security. We found that objective security knowledge can lead to secure decisions when paired with high subjective security knowledge. In the absence of the latter, objective security knowledge is unable to lead to better security decisions. Furthermore, subjective security knowledge reduces the extent to which users fully accept default security settings, thereby mitigating bias toward insecure default settings. Collapse

Topics: mobile application data security decision making self efficacy mobile application market

Methods: survey descriptive statistic structural equation modeling literature study post-hoc analysis

Theories: theory of bounded rationality

A Flow-Oriented Process Model of Social Media Services Use

2023 | European Conference On Information Systems | Citations: 0

Authors: Frenzel-Piasentin, Adeline; Bruckner, Moritz; Wagner, Christina

Abstract: While social media has been a popular subject of prior research, we know little ... Expand

Abstract: While social media has been a popular subject of prior research, we know little about the conditions and the flows of action of social media use, including staying, discontinuance, and switching behavior. This makes it difficult to understand platform choices and usage patterns of social media services. Based on a flow-oriented theoretical approach, we conduct an exploratory qualitative research study with ten focus groups based on five popular social media services. We conceptualize a multidimensional perspective of social media use behavior. We identify four flows of action -"adjusting", "coextending", "fading out", and "intensifying"and develop a flow-oriented process model of social media service use. Our findings help to understand the broad spectrum of social media usage behaviors and motives, and provide new avenues for future research. Furthermore, we provide platform providers with insights to better understand the dynamics of social media services. Collapse

Topics: social media technostress information system use spyware paywall

Methods: focus group theory development grounded theory qualitative coding business process modeling

Theories: flow theory action theory

Deep Learning for Information Systems Research

2023 | Journal of Management Information Systems | Citations: 1

Authors: Samtani, Sagar; Zhu, Hongyi; Padmanabhan, Balaji; Chai, Yidong; Chen, Hsinchun; Nunamaker Jr., Jay F.

Abstract: Modern artificial intelligence (AI) is heavily reliant on deep learning (DL), an ... Expand

Abstract: Modern artificial intelligence (AI) is heavily reliant on deep learning (DL), an emerging class of algorithms that can automatically detect non-trivial patterns from petabytes of rapidly evolving "Big Data." Although the information systems (IS) discipline has embraced DL, questions remain about DL's interface with a domain and theory and DL contribution types. In this paper, we present a DL information systems research (DL-ISR) schematic that reviews DL while considering the role of the application environment and knowledge base, summarizes extant DL research in IS, a knowledge contribution framework (KCF) to position DL contributions, and ten guidelines to help IS scholars design, execute, and present DL for computational, behavioral, or economic IS research. We illustrate a research contribution to DL for cybersecurity. This article's contribution to theory resides in the conceptual DL-ISR schematic and KCF, while its contributions to practice are based on its practical guidelines for executing DL-based projects. Collapse

Topics: knowledge base IT security artificial intelligence darknet IT security threat

Methods: Cross-Industry Standard Process for Data Mining data transformation autoencoder computational algorithm econometric modeling

Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Ebrahimi, Mohammadreza; Chai, Yidong; Samtani, Sagar; Chen, Hsinchun

Abstract: International dark web platforms operating within multiple geopolitical regions ... Expand

Abstract: International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact for cross-lingual hacker asset detection (CLHAD). CLHAD automatically leverages the knowledge learned from English content to detect hacker assets in non-English dark web platforms. CLHAD encompasses a novel Adversarial deep representation learning (ADREL) method, which generates multilingual text representations using generative adversarial networks (GANs). Drawing upon the state of the art in cross-lingual knowledge transfer, ADREL is a novel approach to automatically extract transferable text representations and facilitate the analysis of multilingual content. We evaluate CLHAD on Russian, French, and Italian dark web platforms and demonstrate its practical utility in hacker asset profiling, and conduct a proof-of-concept case study. Our analysis suggests that cybersecurity managers may benefit more from focusing on Russian to identify sophisticated hacking assets. In contrast, financial hacker assets are scattered among several dominant dark web languages. Managerial insights for security managers are discussed at operational and strategic levels. Collapse

Topics: darknet IT security knowledge sharing shapley additive explanation spoofing

Methods: machine learning design science design artifact case study deep learning

Protection Motivation Theory in Information Systems Security Research: A Review of the Past and a Road Map for the Future

2021 | ACM SIGMIS Database | Citations: 0

Authors: Haag, Steffi; Siponen, Mikko; Liu, Fufan

Abstract: Protection motivation theory (PMT) is one of the most commonly used theories to ... Expand

Abstract: Protection motivation theory (PMT) is one of the most commonly used theories to examine information security behaviors. Our systematic review of the application of PMT in information systems (IS) security and the comparison with its application for decades in psychology identified five categories of important issues that have not yet been examined in IS security research. Discussing these issues in terms of why they are relevant and important for IS security, and to what extent IS research has not considered them, offers new research opportunities associated with the study of PMT and IS security threats. We suggest how future studies can approach each of the open issues to provide a new road map for quantitative and qualitative IS scholars. Collapse

Topics: IT security threat IT security internet technology spyware cybersecurity behavior

Methods: ANOVA survey parametric test structured literature research bibliometric analysis

Theories: protection motivation theory

Local learning from municipal ransomware attacks: A geographically weighted analysis

2021 | Information & Management | Citations: 0

Authors: Marett, Kent; Nabors, Misty

Abstract: The prevalence of security threats like ransomware continues to increase and vic ... Expand

Abstract: The prevalence of security threats like ransomware continues to increase and victimize a wide range of targets, which includes municipal information systems. These attacks are commonly reported in media outlets available in attacked communities. This study seeks to understand how effective news reporting can be toward influencing the behavior of people who live within the proximity of the attack. The results suggest a geographic influence on individual behaviors that could well extend beyond the context of information security into other areas of behavioral IS research. Collapse

Topics: ransomware data security IT security IT security threat website

Methods: autocorrelation analysis analysis of covariance longitudinal research regression analysis method cluster analysis

Cybersecurity compliance behavior: Exploring the influences of individual decision style and other antecedents

2020 | International Journal of Information Management | Citations: 11

Authors: Donalds, Charlette; Osei-Bryson, Kweku-Muata

Abstract: Recent information and cybersecurity research have focused on improving individu ... Expand

Abstract: Recent information and cybersecurity research have focused on improving individuals’ security compliance behavior. However, improved security performance remains a challenge since individuals often fail to comply with security best practices. In this study, we investigate a new individual cybersecurity compliance behavior model proposed by Donalds and Osei-Bryson (2017). Specifically, we investigate the influence of individual decision styles on their cybersecurity compliance behavior and other antecedents of such behavior. To empirically validate the hypotheses in the Donalds & Osei-Bryson model, we used data collected from 248 individuals and then use multiple regression to examine the assertions of the model. Our findings confirm that individual’s decision styles, specifically, dominant orientation and dominant decision style, influence their individual cybersecurity compliance behavior and other antecedents of such behavior. Our research offers new dimensions for investigating individual cybersecurity compliance behavior and new insights into factors that may influence individual’s cybersecurity compliance behavior. Collapse

Topics: IT security self efficacy password decision making information system use

Methods: survey regression analysis method theory development decision tree classification data transformation

Theories: theory of abduction measurement theory

Local Learning from Municipal Ransomware Attacks

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Marett, Kent; Nabors, Misty

Abstract: Ransomware is not only the bane of private businesses and publicly-traded compa ... Expand

Abstract: Ransomware is not only the bane of private businesses and publicly-traded companies, it has steadily targeted municipal governmental entities over the past couple of years. Such attacks can cripple a city's ability to provide services and support for its residents. Since these attacks are reported upon in the press, do an attacked community's citizens change their behavior in the wake of the attack?In this study, the security precautions being taken by residents within 20 miles of an attacked community were compared with Internet users living further away from the attacks. Using spatial analysis software like ArcGIS and GWR, we analyzed the data procured from an online survey of American Internet users and found that local learning complements users' prior victimization from cybercrime and their own selfinterest to influence their cautious behavior online. KeywordsRansomware, spatial analysis, precautionary behavior, local learning. Collapse

Topics: ransomware IT security threat IT security internet technology social media

Methods: analysis of covariance survey regression analysis method cross sectional survey digital trace data

A Data-driven Characterization of Modern Android Spyware

2020 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Pierazzi, Fabio; Mezzour, Ghita; Han, Qian; Colajanni, Michele; Subrahmanian, V. S.

Abstract: According to Nokia’s 2017 Threat Intelligence Report, 68.5% of malware targets t ... Expand

Abstract: According to Nokia’s 2017 Threat Intelligence Report, 68.5% of malware targets the Android platform; Windows is second with 28%, followed by iOS and other platforms with 3.5%. The Android spyware family UAPUSH was responsible for the most infections, and several of the top 20 most common Android malware were spyware. Simply put, modern spyware steals the basic information needed to fuel more deadly attacks such as ransomware and banking fraud. Not surprisingly, some forms of spyware are also classified as banking trojans (e.g., ACECARD). We present a data-driven characterization of the principal factors that distinguish modern Android spyware (July 2016–July 2017) both from goodware and other Android malware, using both traditional and deep ML. First, we propose an Ensemble Late Fusion (ELF) architecture that combines the results of multiple classifiers’ predicted probabilities to generate a final prediction. We show that ELF outperforms several of the best-known traditional and deep learning classifiers. Second, we automatically identify key features that distinguish spyware both from goodware and from other malware. Finally we present a detailed analysis of the factors distinguishing five important families of Android spyware: UAPUSH, PINCER, HEHE, USBCLEAVER, and ACECARD (the last is a hybrid spyware-banking trojan). Collapse

Topics: spyware malware Android mobile application data breach

Methods: machine learning deep learning computational algorithm decision tree classification experiment

The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach

2020 | Communications of the Association for Information Systems | Citations: 0

Authors: Sharma, Shweta; Kumar, Naveen; Kumar, Rakesh; Krishna, C Rama

Abstract: The increase in the number of mobile devices that use the Android operating syst ... Expand

Abstract: The increase in the number of mobile devices that use the Android operating system has attracted the attention of cybercriminals who want to disrupt or gain unauthorized access to them through malware infections. To prevent such malware, cybersecurity experts and researchers require datasets of malware samples that most available antivirus software programs cannot detect. However, researchers have infrequently discussed how to identify evolving Android malware characteristics from different sources. In this paper, we analyze a wide variety of Android malware datasets to determine more discriminative features such as permissions and intents. We then apply machine-learning techniques on collected samples of different datasets based on the acquired features’ similarity. We perform random sampling on each cluster of collected datasets to check the antivirus software’s capability to detect the sample. We also discuss some common pitfalls in selecting datasets. Our findings benefit firms by acting as an exhaustive source of information about leading Android malware datasets. Collapse

Topics: malware computer virus protection Android mobile application ransomware

Methods: cluster analysis experiment machine learning computational algorithm literature study