Biblio

The main objective of this research is to increase security awareness against phishing attacks in the education sector by teaching users about phishing URLs. The educational media was made based on references from several previous studies that were used as basic references. Development of antiphishing game framework educational media using the extended DPE framework. Participants in this study were vocational and college students in the technology field. The respondents included vocational and college students, each with as many as 30 respondents. To assess the level of awareness and understanding of phishing, especially phishing URLs, participants will be given a pre-test before playing the game, and after completing the game, the application will be given a posttest. A paired t-test was used to answer the research hypothesis. The results of data analysis show differences in the results of increasing identification of URL phishing by respondents before and after using educational media of the anti-phishing game framework in increasing security awareness against URL phishing attacks. More serious game development can be carried out in the future to increase user awareness, particularly in phishing or other security issues, and can be implemented for general users who do not have a background in technology.

As edge computing has been widely used in IoT (Internet of Things) systems, the security has become one of important issues for IoT. Because of a large amount of private information stored in edge computing devices, it makes edge computing devices attractive to various kinds attacks. To deal with this challenge, this paper proposes a security awareness scheme for edge computing devices in IoT system. Test results show that the proposed approach can improve services-oriented security situation of IoT systems based on edge computing.

The dependability of Cyber Physical Systems (CPS) solely lies in the secure and reliable functionality of their backbone, the computing platform. Security of this platform is not only threatened by the vulnerabilities in the software peripherals, but also by the vulnerabilities in the hardware internals. Such threats can arise from malicious modifications to the integrated circuits (IC) based computing hardware, which can disable the system, leak information or produce malfunctions. Such modifications to computing hardware are made possible by the globalization of the IC industry, where a computing chip can be manufactured anywhere in the world. In the complex computing environment of CPS such modifications can be stealthier and undetectable. Under such circumstances, design of these malicious modifications, and eventually their detection, will be tied to the functionality and operation of the CPS. So it is imperative to address such threats by incorporating security awareness in the computing hardware design in a comprehensive manner taking the entire system into consideration. In this paper, we present a study in the influence of hardware Trojans on closed-loop systems, which form the basis of CPS, and establish threat models. Using these models, we perform a case study on a critical CPS application, gas pipeline based SCADA system. Through this process, we establish a completely virtual simulation platform along with a hardware-in-the-loop based simulation platform for implementation and testing.

Phishing attacks continue to be one of the most common attack vectors used online today to deceive users, such that attackers can obtain unauthorised access or steal sensitive information. Phishing campaigns often vary in their level of sophistication, from mass distribution of generic content, such as delivery notifications, online purchase orders, and claims of winning the lottery, through to bespoke and highly-personalised messages that convincingly impersonate genuine communications (e.g., spearphishing attacks). There is a distinct trade-off here between the scale of an attack versus the effort required to curate content that is likely to convince an individual to carry out an action (typically, clicking a malicious hyperlink). In this short paper, we conduct a preliminary study on a recent realworld incident that strikes a balance between attacking at scale and personalised content. We adopt different visualisation tools and techniques for better assessing the scale and impact of the attack, that can be used both by security professionals to analyse the security incident, but could also be used to inform employees as a form of security awareness and training. We pitched the approach to IT professionals working in information security, who believe this may provide improved awareness of how targeted phishing campaigns can impact an organisation, and could contribute towards a pro-active step of how analysts will examine and mitigate the impact of future attacks across the organisation.

Recently, several health care wearable devices which can intervene in health and collect personal health data have emerged in the medical market. Although health care wearable devices promote the integration of multi-layer medical resources and bring new ways of health applications for users, it is inevitable that some problems will be brought. This is mainly manifested in the safety protection of medical and health data and the protection of user's privacy. From the users' point of view, the irrational use of medical and health data may bring psychological and physical negative effects to users. From the government's perspective, it may be sold by private businesses in the international arena and threaten national security. The most direct precaution against the problem is users' initiative. For better understanding, a research model is designed by the following five aspects: Security knowledge (SK), Security attitude (SAT), Security practice (SP), Security awareness (SAW) and Security conduct (SC). To verify the model, structural equation analysis which is an empirical approach was applied to examine the validity and all the results showed that SK, SAT, SP, SAW and SC are important factors affecting users' data security and privacy protection awareness.

Undeterred by numerous efforts deployed by antivirus software that shields users from various security threats, ransomware is constantly evolving as technology advances. The impact includes hackers hindering the user's accessibility to their data, and the user will pay ransom to retrieve their data. Ransomware also targets multimillion-dollar organizations, and it can cause colossal data loss. The organizations could face catastrophic consequences, and business operations could be ceased. This research contributes by spreading awareness of ransomware to alert people to tackle ransomware. The solution of this research is the conceptual development of a browser extension that provides assistance to warn users of plausible dangers while surfing the Internet. It allows the users to surf the web safely. Since the contribution of this research is conceptual, we can assume that technology users will adopt the proposed idea to prevent ransomware attacks on their personal computers once the solution is fully implemented in future research.

A lack of awareness surrounding secure online behaviour can lead to end-users, and their personal details becoming vulnerable to compromise. This paper describes an ongoing research project in the field of usable security, examining the relationship between end-user-security behaviour, and the use of affective feedback to educate end-users. Part of the aforementioned research project considers the link between categorical information users reveal about themselves online, and the information users believe, or report that they have revealed online. The experimental results confirm a disparity between information revealed, and what users think they have revealed, highlighting a deficit in security awareness. Results gained in relation to the affective feedback delivered are mixed, indicating limited short-term impact. Future work seeks to perform a long-term study, with the view that positive behavioural changes may be reflected in the results as end-users become more knowledgeable about security awareness.

Utility networks are part of every nation's critical infrastructure, and their protection is now seen as a high priority objective. In this paper, we propose a threat awareness architecture for critical infrastructures, which we believe will raise security awareness and increase resilience in utility networks. We first describe an investigation of trends and threats that may impose security risks in utility networks. This was performed on the basis of a viewpoint approach that is capable of identifying technical and non-technical issues (e.g., behaviour of humans). The result of our analysis indicated that utility networks are affected strongly by technological trends, but that humans comprise an important threat to them. This provided evidence and confirmed that the protection of utility networks is a multi-variable problem, and thus, requires the examination of information stemming from various viewpoints of a network. In order to accomplish our objective, we propose a systematic threat awareness architecture in the context of a resilience strategy, which ultimately aims at providing and maintaining an acceptable level of security and safety in critical infrastructures. As a proof of concept, we demonstrate partially via a case study the application of the proposed threat awareness architecture, where we examine the potential impact of attacks in the context of social engineering in a European utility company.

Ransomwares have become a growing threat since 2012, and the situation continues to worsen until now. The lack of security mechanisms and security awareness are pushing the systems into mire of ransomware attacks. In this paper, a new framework called 2entFOX' is proposed in order to detect high survivable ransomwares (HSR). To our knowledge this framework can be considered as one of the first frameworks in ransomware detection because of little publicly-available research in this field. We analyzed Windows ransomwares' behaviour and we tried to find appropriate features which are particular useful in detecting this type of malwares with high detection accuracy and low false positive rate. After hard experimental analysis we extracted 20 effective features which due to two highly efficient ones we could achieve an appropriate set for HSRs detection. After proposing architecture based on Bayesian belief network, the final evaluation is done on some known ransomware samples and unknown ones based on six different scenarios. The result of this evaluations shows the high accuracy of 2entFox in detection of HSRs.

The relevance of identity data leaks on the Internet is more present than ever. Almost every month we read about leakage of databases with more than a million users in the news. Smaller but not less dangerous leaks happen even multiple times a day. The public availability of such leaked data is a major threat to the victims, but also creates the opportunity to learn not only about security of service providers but also the behavior of users when choosing passwords. Our goal is to analyze this data and generate knowledge that can be used to increase security awareness and security, respectively. This paper presents a novel approach to automatic analysis of a vast majority of bigger and smaller leaks. Our contribution is the concept and a prototype implementation of a parser, composed of a syntactic and a semantic module, and a data analyzer for identity leaks. In this context, we deal with the two major challenges of a huge amount of different formats and the recognition of leaks' unknown data types. Based on the data collected, this paper reveals how easy it is for criminals to collect lots of passwords, which are plain text or only weakly hashed.

The relevance of identity data leaks on the Internet is more present than ever. Almost every month we read about leakage of databases with more than a million users in the news. Smaller but not less dangerous leaks happen even multiple times a day. The public availability of such leaked data is a major threat to the victims, but also creates the opportunity to learn not only about security of service providers but also the behavior of users when choosing passwords. Our goal is to analyze this data and generate knowledge that can be used to increase security awareness and security, respectively. This paper presents a novel approach to automatic analysis of a vast majority of bigger and smaller leaks. Our contribution is the concept and a prototype implementation of a parser, composed of a syntactic and a semantic module, and a data analyzer for identity leaks. In this context, we deal with the two major challenges of a huge amount of different formats and the recognition of leaks' unknown data types. Based on the data collected, this paper reveals how easy it is for criminals to collect lots of passwords, which are plain text or only weakly hashed.