Managing Organizational Cyber Security – The Distinct Role of Internalized Responsibility

2023 | HICSS | Citations: 0

Authors: Faltermaier, Stefan; Strunk, Kim; Obermeier, Michaela; Fiedler, Marina

Abstract: Desirable user behavior is key to cyber security in organizations. However, a co ... Expand

Abstract: Desirable user behavior is key to cyber security in organizations. However, a comprehensive overview on how to manage user behavior effectively, in order to support organizational cyber security, is missing. Building on extant research to identify central components of organizational cyber security management and on a qualitative analysis based on 20 semi-structured interviews with users and IT-Managers of a European university, we present an integrated model on this issue. We contribute to understanding the interrelations of namely user awareness, user IT-capabilities, organizational IT, user behavior, and especially internalized responsibility and relation to organizational cyber security. Collapse

Topics: IT security user behavior information technology capability IT security defense password

Methods: qualitative interview personal interview qualitative coding grounded theory case study

A Qualitative Study on Acceptance Factors of Economic Approaches on IT Security Investment Decisions

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Niedzela, Laura; Kuehnel, Stephan; Nastjuk, Ilja; Matschak, Tizian; Sackmann, Stefan; Trang, Simon

Abstract: Cyber-attacks can cause severe economic damages to businesses today. Therefore, ... Expand

Abstract: Cyber-attacks can cause severe economic damages to businesses today. Therefore, every company needs to protect its IT systems from such attacks by implementing effective IT security measures. When it comes to deciding on an IT security measure, there is a variety of decision approaches that can offer support on choosing an economically reasonable one. In practice, however, such approaches are rarely applied and a company's security investment decisions are often based on intuitive factors. We therefore want to explore which factors drive the acceptance and application of economic approaches on IT security investment decisions. For this, we used semi-structured interviews and a moderated focus group to generate insights from both science and practice. Not only did our research yield new scientific knowledge about IT security investment decisions, but the results can also be useful in increasing the application of economic decision approaches. Collapse

Topics: IT security IT security defense decision making real option computer virus protection

Methods: qualitative interview personal interview literature study focus group cluster analysis

Theories: real options theory

Employee responses to information security related stress: Coping and violation intention

2023 | Information Systems Journal | Citations: 1

Authors: Yazdanmehr, Adel; Li, Yuan; Wang, Jingguo

Abstract: Studies on employee responses to the information security policy (ISP) demands t ... Expand

Abstract: Studies on employee responses to the information security policy (ISP) demands to show that employees who experience stress over the demands would resort to emotion-focused coping to alleviate the stress and subsequently violate the ISP. However, their intent to engage in problem-focused coping to meet the ISP demands and possibly reduce ISP violations has yet to be analysed. We argue that both types of coping responses coexist in employee responses to ISP demands and they together influence ISP violation intention. Drawing upon the Transactional Model of Stress and Coping, we examine how security-related stress (SRS) triggers inward and outward emotion-focused coping, and problem-focused coping to the ISP demands, which together influence employee ISP violations. We also examine how ISP-related self-efficacy and organisational support moderate the effects of SRS on coping responses. We surveyed 200 employees in the United States to test our model. The results indicate that SRS triggers all three coping responses, and ISP-related self-efficacy and organisational support reduce the effects of SRS on inward and outward emotion-focused coping. Problem-focused coping then decreases ISP violation intention, whereas inward and outward emotion-focused coping increases it. The model was further verified with ISP compliance as the outcome construct, which yielded consistent results. Understanding various coping responses to SRS and the factors that facilitate or inhibit the responses can assist managers in effectively designing and implementing the ISP to reduce employee ISP violations. Collapse

Topics: self efficacy security policy data security problem solving internet technology

Methods: survey descriptive statistic partial least squares regression survey design factor analysis

Theories: technology threat avoidance theory

A critical assessment of consumer reviews: A hybrid NLP-based methodology

2022 | Decision Support Systems | Citations: 0

Authors: Biswas, Baidyanath; Sengupta, Pooja; Kumar, Ajay; Delen, Dursun; Gupta, Shivam

Abstract: Online reviews are integral to consumer decision-making while purchasing product ... Expand

Abstract: Online reviews are integral to consumer decision-making while purchasing products on an e-commerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors to­ wards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon’s Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness. We created four predictors - review depth, review divergence, semantic entropy and keyword relevance to build our primary empirical models. We also reported interesting findings from the interaction effects of the reviewer’s credibility, age of review, and review divergence. We also validated the robustness of our results across different product categories and higher thresholds of help­ fulness votes. Our study contributes to the electronic commerce literature with relevant managerial and theo­ retical implications through these findings. Collapse

Topics: online review electronic commerce computer virus protection information retrieval digital media

Methods: natural language processing robustness check term frequency–inverse document frequency topic model moderation analysis

Theories: dual-system theory chaos theory mathematical theory of communication elaboration likelihood model

US Census Block Groups and Location Sharing Behavior: Case of IoT Adoption

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Koohikamali, Mehrdad; Kim, Dan

Abstract: This study seeks to investigate the location sharing behavior in the context of ... Expand

Abstract: This study seeks to investigate the location sharing behavior in the context of US census block groups (BGs). With an increased adoption and usage of smart technologies and Internet-of-Things (IoT) devices, the availability and vulnerability of location information has made several news headlines. From privacy protection standpoint, it is crucial to know how different propensities of BGs influence the aggregated location sharing behavior. Tobler's first law of geography helps integrate technology-organizationenvironment (TOE) framework and spatial awareness technology utilization model (SATUM) and propose a research model for data breaches of BGs. Discovering patterns of BGs prosperity to share location is analyzed based on median household income, occupied household units, internet access, security/antivirus ownership, bachelor's education, and occupation in computer/math, legal, and protective services. For pilot testing, data was collected for 331 BGs in City of Los Angeles. All variables expect economic variables showed a significant impact to location sharing of BGs. Study promises significant theoretical and practical contributions. Collapse

Topics: internet technology computer virus protection

Demystifying analytical information processing capability: The case of cybersecurity incident response

2021 | Decision Support Systems | Citations: 7

Authors: Naseer, Humza; Maynard, Sean B.; Desouza, Kevin C.

Abstract: Little is known about how organizations leverage business analytics (BA) to deve ... Expand

Abstract: Little is known about how organizations leverage business analytics (BA) to develop, process, and exploit analytical information in cybersecurity incident response (CSIR). Drawing on information processing theory (IPT), we conducted a field study using a multiple case study design to answer the following research question: How do organizations exploit analytical information in the process of cybersecurity incident response by using business analytics? We developed a theoretical framework that explains how organizations respond to the dy­ namic cyber threat environment by exploiting analytical information processing capability in the CSIR process. This, in turn, leads to positive outcomes in enterprise security performance, delivering both strategic and financial benefits. Our findings contribute to the BA and cybersecurity literature by providing useful insights into BA applications and the facilitation of analytics-driven decision making in CSIR. Further, they contribute to IPT by providing new insights about analytical information needs, mechanisms to seek analytical information, and analytical information use in the process of CSIR. Collapse

Topics: IT security IT security threat analytical information system decision making malware

Methods: case study qualitative interview theory development multiple case study anomaly detection

Theories: information processing theory

Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning

2021 | Information Systems Frontiers | Citations: 0

Authors: Rathore, Hemant; Sahay, Sanjay K.; Nikam, Piyush; Sewak, Mohit

Abstract: Since the inception of Andoroid OS, smartphones sales have been growing exponent ... Expand

Abstract: Since the inception of Andoroid OS, smartphones sales have been growing exponentially, and today it enjoys the monopoly in the smartphone marketplace. The widespread adoption of Android smartphones has drawn the attention of malware designers, which threatens the Android ecosystem. The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are susceptible to adversarial attack. Therefore in this paper, we developed eight Android malware detection models based on machine learning and deep neural network and investigated their robustness against the adversarial attacks. For the purpose, we created new variants of malware using Reinforcement Learning, which will be misclassified as benign by the existing Android malware detection models. We propose two novel attack strategies, namely single policy attack and multiple policy attack using reinforcement learning for white-box and grey-box scenario respectively. Putting ourselves in adversary’ shoes, we designed adversarial attacks on the detection models with the goal of maximising fooling rate, while making minimum modifications to the Android application and ensuring that the app’s functionality and behaviour does not change. We achieved an average fooling rate of 44.21% and 53.20% across all the eight detection models with maximum five modifications using a single policy attack and multiple policy attack, respectively. The highest fooling rate of 86.09% with five changes was attained against the decision tree based model using the multiple policy approach. Finally, we propose an adversarial defence strategy which reduces the average fooling rate by threefold to 15.22% against a single policy attack, thereby increasing the robustness of the detection models i.e. the proposed model can effectively detect variants (metamorphic) of malware. The experimental analysis shows that our proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks. Collapse

Topics: malware Android mobile application computer virus protection database system

Methods: classification method computational algorithm experiment reinforcement learning support vector machine

Breaking Bad in Cyberspace: Understanding why and how Black Hat Hackers Manage their Nerves to Commit their Virtual Crimes

2021 | Information Systems Frontiers | Citations: 0

Authors: Silic, Mario; Lowry, Paul Benjamin

Abstract: What is happening in hacker’s minds when they are committing criminal activities ... Expand

Abstract: What is happening in hacker’s minds when they are committing criminal activities? How black hat hackers manage nerves, which is about managing fear and underlying emotions, and which tactics they employ during their decision-making process before, during and after committing a crime, is the question that could provide some initial insights on hacker’s trajectories, their switch from black hat to white hat and ultimately about their behaviors and motivations. The main difficulty in answering this question resides with the access to hacker’s data. To address this gap, we conducted interviews with 16 black hat hackers. Supported by the general strain theory and routine activity theory, we identified five techniques that they use to manage their nerves: shunting, minimization, plan B, thrill, and lens widening techniques. Each of these techniques help hackers to better manage their nerves and consequently, learn how to better cope with the fear. During their psychological decision-making processes, hackers use these five techniques to create a new mindset, behind which they hide, with the objective of minimizing and mitigating the inherent risks they encounter during their criminal activities. The theoretical importance of nerve is the key to a better understanding of black hat hacker’s illegal acts, their behaviors and ultimately their actions. Collapse

Topics: criminality decision making computer crime IT policy anonymity

Methods: qualitative interview grounded theory theory development qualitative coding cluster analysis

Theories: general strain theory activity theory

The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis

2020 | Information Systems Frontiers | Citations: 0

Authors: Ismagilova, Elvira; Slade, Emma L.; Rana, Nripendra P.; Dwivedi, Yogesh K.

Abstract: The aim of this research is to synthesise findings from previous studies by empl ... Expand

Abstract: The aim of this research is to synthesise findings from previous studies by employing weight and meta-analysis to reconcile conflicting evidence and draw a “big picture” of eWOM factors influencing consumers’ intention to buy. By using the findings from 69 studies, this research identified best (e.g. argument quality, valence, eWOM usefulness, trust in message), promising (e.g. eWOM credibility, emotional trust, attitude towards website) and least effective (e.g. volume, existing eWOM, source credibility) predictors of intention to buy in eWOM research. Additionally, the effect size of each predictor was calculated by performing meta-analysis. For academics, understanding what influences consumers’ intention to buy will help set the agenda for future research directions; for practitioners, it will provide benefit in terms of practical guidance based on detailed analysis of specific factors influencing consumers’ intention to buy, which could enhance their marketing activities. Collapse

Topics: online word of mouth word of mouth website perceived ease of use electronic commerce

Methods: meta analysis descriptive statistic experiment survey design survey

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