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This article aims to provide an interactive model for hands on training of malware analysis. Cyberwar games are the highly stylized representation of cyber conflicts in a simulation model. Game models are helpful in understanding the phenomenon of cyber attacks as well as to evolve new techniques of detection. Cyber security trainings are generally very challenging. Cyber test beds make such trainings easy both for trainees and trainers. However, it is not feasible for each organization to build a network for the sole purpose of hands-on training for employees. Therefore, it is desirable to build an interactive environment that is interesting and free of cost as well.

After exploring existing cyberwar games and their techniques, limitation and strengths, this paper presents a design to merge the cyber attacks into a unique model of war game for detection and analysis of malware. This research designs a malware analysis testbed using online free resources. The authors have used the platform of Cyber Defense Technology Experimental Research (DETER). This study proposed model of a testbed that supports malware reverse engineering scenarios, exercise logs and analysis to develop reverse engineering tactics.

The proposed cyber testbed is an automated system that can be used as a platform to train cyber warriors. A few features of the proposed testbed are as follows: testbed provides real or seemingly real malware communication with the real world. It includes automated decisions for the detection of malicious behavior without human intervention. The author gives a design to develop free of cost mechanism for remote learning of highly technical cyber security areas, and this simulation is for malware analysis.

Cyberwar games are built for strengthening offensive and defensive capabilities in cyberspace. For this purpose, many simulations, as well as emulation platforms, can be found. Some are free and open-source, whereas others are commercial and quite expensive. Existing testbeds have limitations in respect of cyberwar games for creating innovative scenarios. Existing literature does not offer any attack and response scenario developed for malware detection through some existing open-source and online simulation or emulation environments. This research includes an analysis of the existing platforms as well as the design of a new model for malware analysis and training.

The paper addresses various cyber threats and their effects on the internet. A review of the literature on intrusion detection systems (IDSs) as a means of mitigating internet attacks is presented, and gaps in the research are identified. The purpose of this paper is to identify the limitations of the current research and presents future directions for intrusion/malware detection research.

The paper presents a review of the research literature on IDSs, prior to identifying research gaps and limitations and suggesting future directions.

The popularity of the internet makes it vulnerable against various cyber-attacks. Ongoing research on intrusion detection methods aims to overcome the limitations of earlier approaches to internet security. However, findings from the literature review indicate a number of different limitations of existing techniques: poor accuracy, high detection time, and low flexibility in detecting zero-day attacks.

This paper provides a review of major issues in intrusion detection approaches. On the basis of a systematic and detailed review of the literature, various research limitations are discovered. Clear and concise directions for future research are provided.

Cyber resilience in cyber supply chain (CSC) systems security has become inevitable as attacks, risks and vulnerabilities increase in real-time critical infrastructure systems with little time for system failures. Cyber resilience approaches ensure the ability of a supply chain system to prepare, absorb, recover and adapt to adverse effects in the complex CPS environment. However, threats within the CSC context can pose a severe disruption to the overall business continuity. The paper aims to use machine learning (ML) techniques to predict threats on cyber supply chain systems, improve cyber resilience that focuses on critical assets and reduce the attack surface.

The approach follows two main cyber resilience design principles that focus on common critical assets and reduce the attack surface for this purpose. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles. The critical assets include Cyber Digital, Cyber Physical and physical elements. We consider Logistic Regression, Decision Tree, Naïve Bayes and Random Forest classification algorithms in a Majority Voting to predicate the results. Finally, we mapped the threats with known attacks for inferences to improve resilience on the critical assets.

The paper contributes to CSC system resilience based on the understanding and prediction of the threats. The result shows a 70% performance accuracy for the threat prediction with cyber resilience design principles that focus on critical assets and controls and reduce the threat.

Therefore, there is a need to understand and predicate the threat so that appropriate control actions can ensure system resilience. However, due to the invincibility and dynamic nature of cyber attacks, there are limited controls and attributions. This poses serious implications for cyber supply chain systems and its cascading impacts.

ML techniques are used on a dataset to analyse and predict the threats based on the CSC resilience design principles.

There are no social implications rather it has serious implications for organizations and third-party vendors.

The originality of the paper lies in the fact that cyber resilience design principles that focus on common critical assets are used including Cyber Digital, Cyber Physical and physical elements to determine the attack surface. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles to reduce the attack surface for this purpose.

Online social network (OSN) users have a high propensity to malware threats due to the trust and persuasive factors that underpin OSN models. The escalation of social engineering malware encourages a growing demand for end-user security awareness measures. The purpose of this paper is to take the theoretical cybersecurity awareness model TTAT-MIP and test its feasibility via a Facebook app, namely social network criminal (SNC).

The research employs a mixed-methods approach to evaluate the SNC app. A system usability scale measures the usability of SNC. Paired samples t-tests were administered to 40 participants to measure security awareness – before and after the intervention. Finally, 20 semi-structured interviews were deployed to obtain qualitative data about the usefulness of the App itself.

Results validate the effectiveness of OSN apps utilising a TTAT-MIP model – specifically the mass interpersonal persuasion (MIP) attributes. Using TTAT-MIP as a guidance, practitioners can develop security awareness systems that better leverage the intra-relationship model of OSNs.

The primary limitation of this study is the experimental settings. Although the results testing the TTAT-MIP Facebook app are promising, these were set under experimental conditions.

SNC enable persuasive security behaviour amongst employees and avoid potential malware threats. SNC support consistent security awareness practices by the regular identification of new threats which may inspire the creation of new security awareness videos.

The structure of OSNs is making it easier for malicious users to carry out their activities without the possibility of detection. By building a security awareness programme using the TTAT-MIP model, organisations can proactively manage security awareness.

Many security systems are cumbersome, inconsistent and non-specific. The outcome of this research provides organisations and security practitioners with a framework for designing and developing proactive and tailored security awareness systems.

This paper aims to investigate the impact of perceived price level and information security awareness on computer users’ attitude. Moreover, this study aims to investigate the effect of attitude, subjective norms and perceived behavioral control (PBC) on intention to use anti-malware software.

Data were collected using a structured questionnaire from 225 students of five public universities in Malaysia. Purposive sampling technique was used in this study. AMOS 24 was used to test the research framework using a two-step approach.

Findings give support to some of the hypotheses developed with R2 values of 0.521 for attitude and 0.740 for intention. Perceived price level had a negative effect on attitude while information security awareness had a positive effect on attitude and intention. Attitude, subjective norms and PBC were all positively related to intention, but perceived price level did not affect intention. This suggests that benefits of using anti-malware are more than its price value. Therefore, the price has no direct effect on intention to use.

University computer networks are as open and inviting as their campuses. Therefore, this research can be helpful to the universities to safeguard their networks and encourage the students to use anti-malware. However, using anti-malware software will enable an individual to identify and prioritize security risks, quickly detect and mitigate security breaches, improve the understanding of security gaps and safeguard the sensitive data by minimizing the risks related to malware.

This study ventured to model the information security behavior of anti-malware usage by individual users by using the theory of planned behavior with the addition of two new variables, perceived price level and information security awareness to explain the behavior better.

An organization’s ability to successfully manage intellectual capital is determined by the actions of its employees to prevent or minimize information security incidents. To prevent more data breaches to intellectual capital, organizations must provide regular cybersecurity awareness training for all personnel. The purpose of this paper is to investigate the effect of different evidence-based cybersecurity training methods on employees’ cybersecurity risk perception and self-reported behavior.

The study participants were randomly assigned into four groups (i.e. malware report, malware videos, both malware report and malware videos and no interventions) to assess the effects of cybersecurity training on their perceptions of vulnerability, severity, self-efficacy, security intention as well as their self-reported cybersecurity behaviors.

The results show that evidence-based malware report is a relatively better training method in affecting employees’ intentions of engaging in recommended cybersecurity behaviors comparing with the other training methods used in this study. A closer analysis suggests whether the training method contains self-relevant information could make a difference to the training effects.

This paper reports an in-depth investigation on how different evidence-based cybersecurity training methods impact employees’ perceptions of susceptibility, severity, self-efficacy, security intention as well as on their self-reported cybersecurity behaviors.

The purpose of this paper is to review current evidence in relation to scale and impacts of cyber crime, including various approaches to defining and measuring the problem.

A review and analysis of survey evidence is used to enable an understanding of the scope and scale of the cyber crime problem, and its effect upon those experiencing it.

The analysis evidences that cyber crime exists in several dimensions, with costs and harms that can be similarly varied. There is also a sense that, moving forward, the “cyber” label will become somewhat redundant as many crimes have the potential to have a technology component.

The key evidence in this particular discussion has some geographic limitations, with much of the discussion focussed upon data drawn from the Crime Survey for England and Wales, as well as other UK-based sources. However, many of the broader points still remain more widely relevant.

This study helps in: better understanding the range and scale of cyber crime threats; understanding how the cyber element fits into the wider context of crime; improving the appreciation of what cyber crime can mean for potential victims; and recognising the cost dimensions, and the implications for protection and response.

The discussion will help businesses and individuals to have a better appreciation of the cyber crime threat, and what ought to be considered in response to it.

The discussion is based upon recent evidence, and therefore represents a more up-to-date view of the cyber crime landscape than reviews already available in earlier literature.

For a good number of Indians, their smartphone is their first digital computing device. They have less experience in dealing with the Internet-enabled device and hence less experience in handling security threats like malware as compared to users of other countries who have gone through the learning curve of handling such security threats using other Internet-enabled devices such as laptop and desktop. Because of this, the inexperienced Indian smartphone user may be vulnerable to Internet-related security breaches, as compared to the citizens of developed economies. Hence, it is essential to understand the attitude, behaviour and security practices of smartphone users in India. Limited research is available about the security behaviour of smartphone users in India as the majority of research in this domain is done outside India.

In this empirical study, the researchers identified 28 cybersecurity behaviours and practices through a survey of relevant literature. An online survey of identified cybersecurity behaviours and practices was administered to 300 smartphone users. Frequency analysis of the respondent data was done to understand the adoption of recommended cybersecurity behaviours and practices. Pearson’s chi-square with 5% level of significance has been used to test the hypotheses. Post hoc analysis with Bonferroni correction was conducted for statistically significant associations.

Overall, the respondents did not exhibit good cybersecurity behaviour. Respondents have adopted some of the most popular security features of the smartphone such as the use of screen lock. However, respondents have not adopted or are not aware of the technical security controls such as encryption and remote wipe. Statistically significant differences were found between the cybersecurity behaviour and practices and independent variables such as gender, age, mobile operating system (OS) and mother tongue. Respondents reported high level of motivation to protect their device and data, whereas they reported moderate level of threat awareness and the ability to protect to their device and data. Results of the comparative analysis with a similar study in China and the USA are also reported in this study.

The main limitations of this study are as follows: the respondents' perceptions about their cybersecurity behaviours and practices were measured as opposed to their actual behaviours and practices and the generalizability of the study is limited because the sample size is small as compared to the total number of smartphone users in India.

The findings of this study may be useful for the design of effective cybersecurity prevention and intervention programs for general smartphone users of India.

This study provides an insight about cybersecurity behaviour of smartphone users in India. To the knowledge of the researchers, this is the first study to collect such quantitative data of smartphone users in India for a better understanding of the cybersecurity behaviours and practices. This study identified 28 cybersecurity behaviours and practices, which smartphone users should follow to improve cybersecurity.

This paper aims to demonstrate the utility of a target-centric approach to intelligence collection and analysis in the prevention and investigation of ransomware attacks that involve cryptocurrencies. The paper uses the May 2017 WannaCry ransomware usage of the Bitcoin ecosystem as a case study. The approach proves particularly beneficial in facilitating information sharing and an integrated analysis across intelligence domains.

This study conducted data collection and analysis of the component Bitcoin elements of the WannaCry ransomware attack. A note of both technicalities of Bitcoin operations and current models for sharing cyber intelligence was made. Our analysis builds on and further develops current definitions and strategies for sharing cyber threat intelligence. It uses the problem definition model (PDM) and generic target network model (TNM) to create an analytic framework for the WannaCry ransomware attack scenario, allowing analysts the ability to test their hypotheses and integrate and share data for collaborative investigation.

Using a target-centric intelligence approach to WannaCry 2.0 shows that it is possible to model the intelligence problem of collecting and analysing data related to inflows and outflows of Bitcoin-related ransomware transactions. Bitcoin transactions form graph networks and allow to build a target network model for collecting, analysing and sharing intelligence with multiple stakeholders. Although attribution and anonymity prevail under cryptocurrency usage, there is a means for developing transaction walks using this method to target nefarious cryptocurrency exchanges where criminals are inclined to cash out their proceeds of crime.

The application of a target-centric intelligence approach to the cryptocurrency components of a ransomware attack provides a framework for intelligence units to break down the problem in the financial domain and model the network behaviour of illicit Bitcoin transactions relating to ransomware.

Opportunities for malicious cyber activities have expanded with the globalisation and advancements in information and communication technology. Such activities will increasingly affect the security of businesses with online presence and/or connected to the internet. Although the real estate sector is a potential attack vector for and target of malicious cyber activities, it is an understudied industry. This paper aims to contribute to a better understanding of the information security threats, awareness, and risk management standards currently employed by the real estate sector in South Australia.

The current study comprises both quantitative and qualitative methodologies, which include 20 survey questionnaires and 20 face-to-face interviews conducted in South Australia.

There is a lack of understanding about the true magnitude of malicious cyber activities and its impact on the real estate sector, as illustrated in the findings of 40 real estate organisations in South Australia. The findings and the escalating complexities of the online environment underscore the need for regular ongoing training programs for basic online security (including new cybercrime trends) and the promotion of a culture of information security (e.g. when using smart mobile devices to store and access sensitive data) among staff. Such initiatives will enable staff employed in the (South Australian) real estate sector to maintain the current knowledge of the latest cybercrime activities and the best cyber security protection measures available.

This is the first academic study focusing on the real estate organisations in South Australia. The findings will contribute to the evidence on the information security threats faced by the sector as well as in develop sector-specific information security risk management guidelines.