Existing Evidence for the Effectiveness of Antivirus in Preventing Cyber Crime Incidents

Antivirus software is a program designed to keep computer devices clean from malicious software (malware) such as viruses, worms, and trojans, and is commonly deployed on computer and smartphone users’ devices as the last line of defense against cyber-dependent crimes). In effort to assess the potential effectiveness of Antivirus products in preventing the development and progression of cyber-dependent crimes we searched in six major academic search engines for studies published between the years 2000-2016 using experimental or quasi-experimental research designs. Our findings reveal that several key approaches are employed by scholars when evaluating antivirus software’s performance

Risk homeostasis in information security:challenges in confirming existence and verifying impact

The central premise behind risk homeostasis theory is that humans adapt their behaviors, based on external factors, to align with a personal risk tolerance level. In essence, this means that the safer or more secure they feel, the more likely it is that they will behave in a risky manner. If this effect exists, it serves to restrict the ability of risk mitigation techniques to effect improvements.The concept is hotly debated in the safety area. Some authors agree that the effect exists, but also point out that it is poorly understood and unreliably predicted. Other re-searchers consider the entire concept fallacious. It is important to gain clarity about whether the effect exists, and to gauge its impact if such evidence can indeed be found.In this paper we consider risk homeostasis in the context of information security. Similar to the safety area, information security could well be impaired if a risk homeostasis effect neutralizes the potential benefits of risk mitigation measures. If the risk homeostasis effect does indeed exist and does impact risk-related behaviors, people will simply elevate risky behaviors in response to feeling less vulnerable due to following security procedures and using protective technologies.Here we discuss, in particular, the challenges we face in confirming the existence and impact of the risk homeostasis effect in information security, especially in an era of ethical research practice

The Role of Trust and Familiarity in Click-through Intention: A Perception Transfer Theory in a Cybersecurity Context

Despite constant reminders, warnings on avoidance techniques, information security policies, and anti-virus software reminders, individuals still fall prey to malware attacks. This paper looks at the role that familiarity and trust play on the individual’s intention to click-through web based malware. Hypotheses are developed to suggest that click-through intention is influenced by trust and familiarity. Two scenarios to test the hypotheses are described. Expected contributions and limitations are noted

Healthcare Facilities: Another Target for Ransomware Attacks

Ransomware is a type of malware used by cyber criminals who encrypt files and then extort money in return for unlocking those files. Without adequate disaster recovery and backup plans, many businesses are forced to pay the ransom. We examine recent ransomware infections in healthcare settings, the liabilities and cost associated with such infections, and discuss possible risk mitigation tactics. Risks associated with ransomware attacks on healthcare facilities include financial, future business loss and damage to reputation. Healthcare facilities should have a disaster plan with adequate data backups and educate employees who are the usual sources of ransomware attacks

SPARC 2018 Internationalisation and collaboration : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2018 SPARC conference. This year we not only celebrate the work of our PGRs but also the launch of our Doctoral School, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 100 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

Prévention des attaques par logiciels malveillants: perspectives de la santé publique

L’augmentation de la connectivité et du développement des infrastructures numériques a contribué à multiplier les motivations et les opportunités des attaques informatiques. Bien que plusieurs progrès aient été réalisés au niveau du développement et de l’implémentation de stratégies de protection, la majorité de ces efforts sont dédiés au développement de nouvelles solutions, et non à leur évaluation et leur promotion. Il devient dès lors essentiel pour les gouvernements, les entreprises, et les individus de définir des modèles et des moyens de coopération permettant d’identifier et d’évaluer les stratégies visant à réduire le risque que posent les menaces informatiques. À cet effet, le domaine de la sécurité des systèmes d’information pourrait bénéficier des leçons apprises et des méthodes utilisées dans le domaine de la santé. En particulier, nous croyons que l’adoption d’une perspective axée sur l’approche de la santé publique permettrait de founir un cadre global pour i) identifier les facteurs qui affectent la sécurité des systèmes d’information et en comprendre les causes sous-jacentes, ii) développer et évaluer des stratégies efficaces visant à améliorer la sécurité des systèmes d’information, et iii) implémenter et disséminer auprès de la population les stratégies développées. Dans le cadre de la présente thèse, nous proposons de nous inspirer des méthodes en santé publique pour développer un modèle de prévention applicable au contexte des attaques par logiciels malveillants. Notamment, nous appliquons notre modèle de prévention afin d’identifier les causes et les corrélats reliés aux attaques par logiciels malveillants, et d’évaluer l’efficacité réelle des solutions antivirus à prévenir ces attaques. À partir de données réelles d’attaques par logiciels malveillants, nous avons réalisé cinq études empiriques ; trois visant à identifier des facteurs de risque et des facteurs de protection, et deux visant à évaluer l’efficacité des antivirus dans un environnement réel. Les résultats de nos travaux de recherche ont, entre autres, permis : i) d’identifier de nouveaux facteurs de risque et de protection reliés aux attaques par logiciels malveillants, ii) d’identifier des sous-populations à risque plus élevé, et iii) de mettre en évidence comment l’effet des facteurs identifiés et des solutions antivirus varie selon le contexte (type de menace, environnement, usager, etc.). Qui plus est, la présente thèse a permis de valider la viabilité et le potentiel d’une approche basée sur la santé publique en sécurité des systèmes d’information.----------ABSTRACT: The increased connectivity and development of digital infrastructures has yielded to increased motivation and opportunities for computer threats. Although there has been some progress in the development and implementation of protection strategies, the majority of these efforts are dedicated to the development of new solutions, and not to their evaluation and promotion. It is therefore essential for governments, businesses, and individuals to develop models and means of cooperation in order to identify and evaluate effective strategies aimed at reducing the risk posed by computer threats. To this end, the field of information security could benefit from lessons learned and methods used in health. In particular, we believe that adopting a public health perspective could provide a comprehensive framework for i) identifying and understanding the factors that affect the information systems security and understand their underlying causes, ii) develop and evaluate effective strategies to improve the security of information systems, and iii) implement and disseminate the strategies developed to the population. In this thesis, we propose to use public health methods to develop a prevention model for the context of malware attacks. In particular, we apply our prevention model to identify the causes and correlates of malware attacks, and evaluate the effectiveness of antivirus solutions in preventing computer threats. Using real-world malware attacks data, we conducted five empirical studies ; three to identify risk factors and protective factors, and two to assess the effectiveness of antivirus in a real-world environment. The results of our research allowed us, among others, to : i) identify new risk and protective factors related to malware attacks, ii) identify high-risk sub-populations, and iii) highlight how the effect of the identified factors and antivirus solutions vary by context (type of threat, environment, user, etc.). In addition, this thesis validated the viability and potential of a public health approach to information security

Shedding Light on the Targeted Victim Profiles of Malicious Downloaders

Malware affects millions of users worldwide, impacting the daily lives of many people as well as businesses. Malware infections are increasing in complexity and unfold over a number of stages. A malicious downloader often acts as the starting point as it fingerprints the victim's machine and downloads one or more additional malware payloads. Although previous research was conducted on these malicious downloaders and their Pay-Per-Install networks, limited work has investigated how the profile of the victim machine, e.g., its characteristics and software configuration, affect the targeting choice of cybercriminals. In this paper, we operate a large-scale investigation of the relation between the machine profile and the payload downloaded by droppers, through 151,189 executions of malware downloaders over a period of 12 months. We build a fully automated framework which uses Virtual Machines (VMs) in sandboxes to build custom user and machine profiles to test our malicious samples. We then use changepoint analysis to model the behavior of different downloader families, and perform analyses of variance (ANOVA) on the ratio of infections per profile. With this, we identify which machine profile is targeted by cybercriminals at different points in time. Our results show that a number of downloaders present different behaviors depending on a number of features of a machine. Notably, a higher number of infections for specific malware families were observed when using different browser profiles, keyboard layouts and operating systems, while one keyboard layout obtained fewer infections of a specific malware family. Our findings bring light to the importance of the features of a machine running malicious downloader software, particularly for malware research

Evidence-based Cybersecurity: Data-driven and Abstract Models

Achieving computer security requires both rigorous empirical measurement and models to understand cybersecurity phenomena and the effectiveness of defenses and interventions. To address the growing scale of cyber-insecurity, my approach to protecting users employs principled and rigorous measurements and models. In this dissertation, I examine four cybersecurity phenomena. I show that data-driven and abstract modeling can reveal surprising conclusions about longterm, persistent problems, like spam and malware, and growing threats like data-breaches and cyber conflict. I present two data-driven statistical models and two abstract models. Both of the data-driven models show that the presence of heavy-tailed distributions can make naive analysis of trends and interventions misleading. First, I examine ten years of publicly reported data breaches and find that there has been no increase in size or frequency. I also find that reported and perceived increases can be explained by the heavy-tailed nature of breaches. In the second data-driven model, I examine a large spam dataset, analyzing spam concentrations across Internet Service Providers. Again, I find that the heavy-tailed nature of spam concentrations complicates analysis. Using appropriate statistical methods, I identify unique risk factors with significant impact on local spam levels. I then use the model to estimate the effect of historical botnet takedowns and find they are frequently ineffective at reducing global spam concentrations and have highly variable local effects. Abstract models are an important tool when data are unavailable. Even without data, I evaluate both known and hypothesized interventions used by search providers to protect users from malicious websites. I present a Markov model of malware spread and study the effect of two potential interventions: blacklisting and depreferencing. I find that heavy-tailed traffic distributions obscure the effects of interventions, but with my abstract model, I showed that lowering search rankings is a viable alternative to blacklisting infected pages. Finally, I study how game-theoretic models can help clarify strategic decisions in cyber-conflict. I find that, in some circumstances, improving the attribution ability of adversaries may decrease the likelihood of escalating cyber conflict

Impersonation-as-a-Service: Characterizing the Emerging Criminal Infrastructure for User Impersonation at Scale

In this paper we provide evidence of an emerging criminal infrastructure enabling impersonation attacks at scale. Impersonation-as-a-Service (ImpaaS) allows attackers to systematically collect and enforce user profiles (consisting of user credentials, cookies, device and behavioural fingerprints, and other metadata) to circumvent risk-based authentication system and effectively bypass multi-factor authentication mechanisms. We present the ImpaaS model and evaluate its implementation by analysing the operation of a large, invite-only, Russian ImpaaS platform providing user profiles for more than $260'000$ Internet users worldwide. Our findings suggest that the ImpaaS model is growing, and provides the mechanisms needed to systematically evade authentication controls across multiple platforms, while providing attackers with a reliable, up-to-date, and semi-automated environment enabling target selection and user impersonation against Internet users as scale.Comment: Presented at ACM CCS 2020. Appendix on "Deriving a Threat Model from Observation" available at https://michelecampobasso.github.io/publication/2020-11-10-impaa