http://dl.acm.org/citation.cfm?id=3446838 http://dl.acm.org/citation.cfm?id=3446838 Wed, 30 Jun 2021 00:00:00 GMT 3446838 http://dl.acm.org/citation.cfm?id=3446635
Service liability interconnections among networked IT and IoT-driven service organizations create potential channels for cascading service disruptions due to modern cybercrimes such as DDoS, APT, and ransomware attacks. These attacks are known to inflict cascading catastrophic service disruptions worth billions of dollars across organizations and critical infrastructure around the globe. Cyber-insurance is a risk management mechanism that is gaining increasing industry popularity to cover client (organization) risks after a cyber-attack. However, there is a certain likelihood that the nature of a successful attack is of such magnitude that an organizational client’s insurance provider is not able to cover the multi-party aggregate losses incurred upon itself by its clients and their descendants in the supply chain, thereby needing to re-insure itself via other cyber-insurance firms.]]> Tue, 25 May 2021 00:00:00 GMT 3446635 http://dl.acm.org/citation.cfm?id=3439772
Ontology provides a shared vocabulary of a domain by formally representing the meaning of its concepts, the properties they possess, and the relations among them, which is the state-of-the-art knowledge modeling technique. However, the ontologies in the same domain could differ in conceptual modeling and granularity level, which yields the ontology heterogeneity problem. To enable data and knowledge transfer, share, and reuse between two intelligent systems, it is important to bridge the semantic gap between the ontologies through the ontology matching technique. To optimize the ontology alignment’s quality, this article proposes an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique, which consists of an automatic ontology matching process based on a Compact Genetic Algorithm (CGA) and a collaborative user validating process based on an argumentation framework.]]> Thu, 20 May 2021 00:00:00 GMT 3439772 http://dl.acm.org/citation.cfm?id=3439770
As an emerging measure of proactive talent management, talent turnover prediction is critically important for companies to attract, engage, and retain talents in order to prevent the loss of intellectual capital. While tremendous efforts have been made in this direction, it is not clear how to model the influence of employees’ turnover within multiple organizational social networks. In this article, we study how to exploit turnover contagion by developing a Turnover Influence-based Neural Network (TINN) for enhancing organizational turnover prediction. Specifically, TINN can construct the turnover similarity network which is then fused with multiple organizational social networks. The fusion is achieved either through learning a hidden turnover influence network or through integrating the turnover influence on multiple networks.]]> Thu, 20 May 2021 00:00:00 GMT 3439770 http://dl.acm.org/citation.cfm?id=3439771
This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters.]]> Fri, 19 Feb 2021 00:00:00 GMT 3439771 http://dl.acm.org/citation.cfm?id=3424282
The increase in cybersecurity threats and the challenges for organisations to protect their information technology assets has made adherence to organisational security control processes and procedures a critical issue that needs to be adequately addressed. Drawing insight from organisational theory literature, we develop a multi-theory model, combining the elements of the theory of planned behaviour, competing value framework, and technology—organisational and environmental theory to examine how the organisational mechanisms interact with espoused cultural values and employee cognitive belief to influence cybersecurity control procedures. Using a structured questionnaire, we deployed structural equation modelling (SEM) to analyse the survey data obtained from public sector information technology organisations in Nigeria to test the hypothesis on the relationship of socio-organisational mechanisms and techno-cultural factors with other key determinants of employee security behaviour.]]> Wed, 03 Feb 2021 00:00:00 GMT 3424282 http://dl.acm.org/citation.cfm?id=3418289
Theft of intellectual property is a growing problem—one that is exacerbated by the fact that a successful compromise of an enterprise might only become known months after the hack. A recent solution called FORGE addresses this problem by automatically generating N “fake” versions of any real document so that the attacker has to determine which of the N + 1 documents that they have exfiltrated from a compromised network is real. In this article, we remove two major drawbacks in FORGE: (i) FORGE requires ontologies in order to generate fake documents—however, in the real world, ontologies, especially good ontologies, are infrequently available.]]> Tue, 02 Feb 2021 00:00:00 GMT 3418289 http://dl.acm.org/citation.cfm?id=3424240
Interfirm relationships are crucial to our understanding of firms’ collective and interactive behavior. Many information systems-related phenomena, including the diffusion of innovations, standard alliances, technology collaboration, and outsourcing, involve a multitude of relationships between firms. This study proposes a latent space approach to model temporal change in a dual-view interfirm network. We assume that interfirm relationships depend on an underlying latent space; firms that are close to each other in the latent space are more likely to develop a relationship. We construct the latent space by embedding two dynamic networks of firms in an integrated manner, resulting in a more comprehensive view of an interfirm relationship.]]> Tue, 12 Jan 2021 00:00:00 GMT 3424240 http://dl.acm.org/citation.cfm?id=3430195
Prior works on the trajectory outlier detection problem solely consider individual outliers. However, in real-world scenarios, trajectory outliers can often appear in groups, e.g., a group of bikes that deviates to the usual trajectory due to the maintenance of streets in the context of intelligent transportation. The current paper considers the Group Trajectory Outlier (GTO) problem and proposes three algorithms. The first and the second algorithms are extensions of the well-known DBSCAN and kNN algorithms, while the third one models the GTO problem as a feature selection problem. Furthermore, two different enhancements for the proposed algorithms are proposed. The first one is based on ensemble learning and computational intelligence, which allows for merging algorithms’ outputs to possibly improve the final result.]]> Wed, 06 Jan 2021 00:00:00 GMT 3430195