Customers repurchase intention formation in e-commerce

Background: Electronic loyalty (e-loyalty) has become important in the context of electronic commerce (e-commerce) in recent years. Loyal customers bring long-term revenue to companies and are known to be a valuable asset to them. However, firms lose their customers in a competitive environment on the Internet because of a lack of trust, satisfaction and loyalty. Objectives: This study explains how e-loyalty, e-trust and e-satisfaction form in e-commerce with a focus on customer purchase intention formation. Method: A conceptual framework was formed based upon the literature review. Data were collected from e-customers of online firms in South Africa. After data clarification, confirmatory factor analysis was conducted. The structural equation modelling was applied to test the hypotheses. IBM SPSS AMOS 20 was used for this purpose. Results: Firstly, convenience, customer benefit and enjoyment affect customer satisfaction in e-commerce. In other words, when customers do business activities easily with enjoyment and take benefit, they are satisfied and they will purchase again in future. Secondly, our study demonstrated that customer perception of security, clear shopping process and reliable payment system have a positive relationship with e-trust. Finally, e-satisfaction and e-trust have a positive and strong relationship with e-loyalty formation in e-commerce. Conclusion: The results of the study shed light on important issues relating to e-loyalty formation from a new perspective. Online companies are interested in launching e-loyalty programmes because of the long-term benefits that come from loyal customers. To remain competitive, e-commerce companies should constantly work at enhancing customer trust, satisfaction and loyalty

A new unified intrusion anomaly detection in identifying unseen web attacks

The global usage of more sophisticated web-based application systems is obviously growing very rapidly. Major usage includes the storing and transporting of sensitive data over the Internet. The growth has consequently opened up a serious need for more secured network and application security protection devices. Security experts normally equip their databases with a large number of signatures to help in the detection of known web-based threats. In reality, it is almost impossible to keep updating the database with the newly identified web vulnerabilities. As such, new attacks are invisible. This research presents a novel approach of Intrusion Detection System (IDS) in detecting unknown attacks on web servers using the Unified Intrusion Anomaly Detection (UIAD) approach. The unified approach consists of three components (preprocessing, statistical analysis, and classification). Initially, the process starts with the removal of irrelevant and redundant features using a novel hybrid feature selection method. Thereafter, the process continues with the application of a statistical approach to identifying traffic abnormality. We performed Relative Percentage Ratio (RPR) coupled with Euclidean Distance Analysis (EDA) and the Chebyshev Inequality Theorem (CIT) to calculate the normality score and generate a finest threshold. Finally, Logitboost (LB) is employed alongside Random Forest (RF) as a weak classifier, with the aim of minimising the final false alarm rate. The experiment has demonstrated that our approach has successfully identified unknown attacks with greater than a 95% detection rate and less than a 1% false alarm rate for both the DARPA 1999 and the ISCX 2012 datasets

The evolution of Blockchain: a bibliometric study

© 2019 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1109/ACCESS.2019.2895646Blockchain as emerging technology is revolutionizing several industries, and its abundant privileges have opened up a bunch of research directions in various industries; thereby, it has acquired many interests from the research community. The rapid evolution of blockchain research papers in recent years has resulted in a need to conduct research studies that investigate a detailed analysis of the current body of knowledge in this field. To address this need, a few review papers have been published to report the latest accomplishments and challenges of blockchain technology from different perspectives. Nonetheless, there has not been any bibliometric analysis of the state of the art in blockchain where Web of Science (WoS) has been taken into consideration as a literature database. Hence, a thorough analysis of the current body of knowledge in blockchain research through a bibliometric study would be needed. In this paper, we performed a bibliometric analysis of all Blockchain’s conference papers, articles, and review papers that have been indexed byWoS from 2013 to 2018. We have analyzed those collected papers against five research questions. The results revealed some valuable insights, including yearly publications and citations trends, hottest research areas, top-ten influential papers, favorite publication venues, and most supportive funding bodies. The findings of this paper offer several implications that can be used as a guideline by both fresh and experienced researchers to establish a baseline before initiating a blockchain research project in the future.Published versio

A LogitBoost-based algorithm for detecting known and unknown web attacks

© 2017 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1109/ACCESS.2017.2766844The rapid growth in the volume and importance of web communication throughout the Internet has heightened the need for better security protection. Security experts, when protecting systems, maintain a database featuring signatures of a large number of attacks to assist with attack detection. However used in isolation, this can limit the capability of the system as it is only able to recognize known attacks. To overcome the problem, we propose an anomaly-based intrusion detection system using an ensemble classification approach to detect unknown attacks on web servers. The process involves removing irrelevant and redundant features utilising a filter and wrapper selection procedure. Logitboost is then employed together with random forests as a weak classifier. The proposed ensemble technique was evaluated with some artificial data sets namely NSL-KDD, an improved version of the old KDD Cup from 1999, and the recently published UNSW-NB15 data set. The experimental results show that our approach demonstrates superiority, in terms of accuracy and detection rate over the traditional approaches, whilst preserving low false rejection rates.Published versio

Motivation and opportunity based model to reduce information security insider threats in organisations

This is an accepted manuscript of an article published by Elsevier in Journal of Information Security and Applications, available online: https://doi.org/10.1016/j.jisa.2017.11.001 The accepted version of the publication may differ from the final published version.Information technology has brought with it many advantages for organisations, but information security is still a major concern for organisations which rely on such technology. Users, whether with intent or through negligence, are a great source of potential of risk to information assets. A lack of awareness, negligence, resistance, disobedience, apathy and mischievousness are root causes of information security incidents in organisations. As such, insider threats have attracted the attention of a number of experts in this domain. Two particularly important considerations when exploring insider threats are motivation and opportunity. Two fundamental theories relating to these phenomena, and on which the research presented in this paper relies, are Social Bond Theory (SBT), which can be used to help undermine motivation to engage in misbehaviour, and Situational Crime Prevention Theory (SCPT), which can be used to reduce opportunities for misbehaviour. The results of our data analysis show that situational prevention factors such as increasing the effort and risk involved in a crime, reducing the rewards and removing excuses can significantly promotes the adoption of negative attitudes towards misbehaviour, though reducing provocations does not have any effect on attitudes. Further, social bond factors such as a commitment to organisational policies and procedures, involvement in information security activities and personal norms also significantly promotes the adoption of negative attitudes towards misbehaviour. However, attachment does not significantly promote an attitude of misbehaviour avoidance on the part of employees. Finally, our findings also show that a negative attitude towards misbehaviour influences the employees’ intentions towards engaging in misbehaviour positively, and this in turn reduces insider threat behaviour. The outputs of this study shed some light on factors which play a role in reducing misbehaviour in the domain of information security for academics and practitioners.Published versio

A threat based approach to computational offloading for collaborative cruise control

The interaction between discrete components of Internet of Things (IoT) and Intelligent Transportation Systems (ITS) is vital for a collaborative system. The secure and reliable use of Cruise Control (CC) with Cloud and Edge Cloud to achieve complete autonomy for a vehicle is a key component and a major challenge for ITS. This research unravels the complications that arise when Adaptive Cruise Control (ACC) is incorporated into a collaborative environment. It mainly answers the question of where to securely compute Collaborative Cruise Control’s (CCC) data in a connected environment. To address this, the paper initially reviews previous research in the domain of Vehicular Cloud, ITS architecture, related threat modelling approaches, and secure implementations of ACC. An overview application model for CCC is developed for performing a threat analysis with the purpose of investigating the reasons why a vehicle suffers collision. Through the use of interviews, the research analyses and suggests the location of computational data by creating a taxonomy between the Edge Cloud, Cloud and the On-board Unit (OBU) while validating the model

An opportunistic resource management model to overcome resource‐constraint in the Internet of Things

This is an accepted manuscript of an article published by Wiley in Concurrency and Computation: Practice and Experience, available online: https://doi.org/10.1002/cpe.5014 The accepted version of the publication may differ from the final published version.Experts believe that the Internet of Things (IoT) is a new revolution in technology and has brought many advantages for our society. However, there are serious challenges in terms of information security and privacy protection. Smart objects usually do not have malware detection due to resource limitations and their intrusion detection work on a particular network. Low computation power, low bandwidth, low battery, storage, and memory contribute to a resource-constrained effect on information security and privacy protection in the domain of IoT. The capacity of fog and cloud computing such as efficient computing, data access, network and storage, supporting mobility, location awareness, heterogeneity, scalability, and low latency in secure communication positively influence information security and privacy protection in IoT. This study illustrates the positive effect of fog and cloud computing on the security of IoT systems and presents a decision-making model based on the object's characteristics such as computational power, storage, memory, energy consumption, bandwidth, packet delivery, hop-count, etc. This helps an IoT system choose the best nodes for creating the fog that we need in the IoT system. Our experiment shows that the proposed approach has less computational, communicational cost, and more productivity in compare with the situation that we choose the smart objects randomly to create a fog.Published versio

Incremental algorithm for association rule mining under dynamic threshold

© 2019 The Authors. Published by MDPI AG. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/app9245398Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time.This research was funded by University Malaya through a postgraduate research grant (PPP) grant number PG106-2015B.Published onlin

Information security collaboration formation in organisations

This is an accepted manuscript of an article published by The Institution of Engineering and Technology in IET Information Security, available online: https://doi.org/10.1049/iet-ifs.2017.0257 The accepted version of the publication may differ from the final published version.Collaboration between employees in the domain of information security efficiently mitigates the effect of information security attacks on organisations. Collaboration means working together to do or to fulfil a shared goal, the target of which in this paper is the protection of the information assets in organisations. Information Security Collaboration (ISC) aims to aggregate the employees’ contribution against information security threats. This study clarifies how ISC is to be developed and how it helps to reduce the effect of attacks. The socialisation of collaboration in the domain of information security applies two essential theories: Social Bond Theory (SBT) and the Theory of Planned Behaviour (TPB). The results of the data analysis revealed that personal norms, involvement, and commitment significantly influence the employees’ attitude towards ISC intention. However, contrary to our expectation, attachment does not influence the attitude of employees towards ISC. In addition, attitudes towards ISC, perceived behavioural control, and personal norms significantly affect the intention towards ISC. The findings also show that the intention for ISC and organisational support positively influence ISC, but that trust does not significantly affect ISC behaviour.Published versio