Draw a model and send it to me. Showing how your independent variables will flow into your dependent variables. What type of ass

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Topic

AN ASSESSMENT ON THE METHODS ADOPTED TO HELP INCREASE THE SECURITY AND PRIVACY OF SMARTPHONES AND PREVENT PHISHING ATTACKS.

Problem Definition

Phishing is a cybercrime that involves sending fraudulent communication that appears to emanate from a reputable source. It is often performed by email with the motive of stealing confidential data such as login information and credit card. There are various types of phishing attacks, including spear-phishing and social media phishing (Kwak et al., 2020). This presentation mainly focuses on spear phishing.

General problem statement

We live in an era that has never been before regarding technological advancement, ranging from the internet, laptops, PCs, tablets, and mobile phones. As much as they are of advantage in easing of our life especially in communication domain, they come with several disadvantages: privacy and security concerns (Kwak et al., 2020). An insecure communication system or gadget threatens the use more than it benefits.

In general, deception research and phishing smartphone investigations, several variables were investigated or discovered. There has been no study on the effect of particular qualities on consumers' ability to detect deception in phishing emails. Other factors that may affect consumers' ability to identify phishing emails have received less attention. Additionally, some studies provide contradictory results or use erroneous measurements. What distinguishes users are their characteristics. Our research focuses on the user characteristics that predispose users to phishing attempts and their impact on detection. This may aid in identifying potential victims who can be better protected. The primary variables examined are personality, culture, and life experience.

Many Variables have been mentioned or explored in past research in relation to the capacity of smartphone users to recognize and avoid phishing scams (Kwak et al., 2020). These are described in further detail below and summarized. These characteristics are as follows: age, gender, education, Internet use and experience, email experience, and understanding of smartphone phishing, all of which are explored in further detail further down.

Specific problems

There are several security and privacy concerns with smartphones, but the most prominent of them will be the Phishing Attacks. In a world where data is transmitted so easily for efficient communication, it is of utmost importance that the consumers understand what data they are sharing (Martin et al., 2019). Phishing attacks refer to the sending of illegitimate links or emails that appear legitimate for stealing the genuine credentials of the receiver (Basit et al., 2021).

Independent variables identified in research on smartphones Phishing

In general, we must consider the dependent and independent variables as a part of the quantitative methodology. There has been no metric on consumers' skill to detect a threat in phishing texts or emails on smartphones. A few factors have received significantly less attention than might affect consumers' ability. What distinguishes users are their characteristics. Our research focuses on the user characteristics that influence users to phishing attempts and their impact on identifying the threat. This may aid in identifying potential victims who can be better protected. The primary variables examined are personality, culture, and life experience

Culture has not been explicitly explored in smartphone phishing research studies. Geographic boundaries no longer exist, and eCommerce enterprises increasingly seek worldwide clients. Smartphone users come from diverse cultures. The ineffectiveness of email as a medium makes knowledge to identifying phishing emails difficult. Cultural differences may assist in differentiating between the phishers and the users. Users from the same culture are more likely to be knowledgeable and identify fraud. For example, international students know their school would never ask for passwords.

The findings revealed a relationship between personality and smartphone phishing recognition. The study discovered Big-Five personality domains as variables. When asked to disclose their SSN, phishers showed extreme sensitivity and concern for their privacy. Conformity may influence whether users become detectors. They were ordered not to divulge their SSN to anybody. They didn't respond to the email because they were told to hide it.

Dependent Variables identified in research on smartphones Phishing

Many Variables have been mentioned or explored in past research concerning the capacity of smartphone users to recognize and avoid phishing scams (Kwak et al., 2020). These are described in further detail below and summarized. These characteristics are as follows: age, education, Internet usage, email experience, and understanding of smartphone phishing, all of which are explored in further detail further down.

Knowledge of smartphone phishing differs by age. Other studies found generation had no effect. Age divides these two study groups. The age groups in these two studies may have impacted the results. They behave differently than older users, maybe because they take more risks. Educating customers about phishing emails helped them spot them.

The number of years spent on the internet has little influence on detecting phishing attempts for smartphone users. Users may utilize the internet to shop, bank, read newspapers, and interact with friends. Online buyers are increasingly suspicious of emails. This may be because online shopping enhances consumers' security and detection awareness.

Other studies found no correlation between smartphone experience and consumers' ability to spot phishing communications (Basit et al., 2021). Users may use smartphones often but lack analytical skills. Securing emails with a lock symbol is not enough, even if users know how to send and receive them. However, Vishwanath et al. (2021) found that receiving more emails increased customers' vulnerability to Phishing. Learning phishing threats like hidden IP addresses has helped customers spot Phishing emails on smartphones.

Scope

Because Android and iOS are the most frequently used smartphone operating systems, we have confined our analysis to devices that are powered by these operating systems alone (Basit et al., 2021). In our research, we are particularly interested of the website spoofing portion of phishing, rather than the phishing messages themselves, which are commonly deployed as the initial step in a phishing operation.

Theoretical framework

The Protection Motivation Theory well aligns with the topic of Phishing since it serves as the foundation for our research.

PMT Theory

Fear appeals are persuasive communications intended to terrify individuals by highlighting the dire consequences of failing to follow the message's directions. PMT was developed to understand better how individuals react to fear and was previously used to encourage people to abstain from harmful activity. PMT is often employed in information security since it may be used to rectify any kind of attitude.

Fear is evaluated in two stages: threat assessment and coping evaluation. These two variables will influence whether someone takes fear-related action. Threat appraisal (TA) is the process of determining how individuals feel about dangers. If a threat has been detected effectively, the anxiety-relieving impact is complete. If the person takes security precautions because of the threat, they have identified and grasped. The PMT divides threat evaluation into two categories: perceived severity and perceived vulnerability (Verkijika, 2018). When danger is identified, a series of circumstances happen that expose a person to a predetermined amount of risk.

The phrase "perceived severity of threat" (PSOT) refers to individuals' repercussions if an assault is successfully carried out. Individuals will exercise more caution if they believe the consequences of inactivity will be severe or if they see a significant threat. For instance, if customers are aware that opening an email attachment may result in a data breach, they exercise more caution. The term "perceived vulnerability" refers to employees' views of their risk exposure. If their business has never had a security breach, they will be exempt from future compliance requirements.

When smartphone user is informed of a potential hazard, their reactions vary. For instance, some say the likelihood of it occurring is relatively low, while others believe it is rather high, and so on. If smartphone users believe they are not at risk from a particular threat, they will adhere to information security rules. According to Workman, Bommer, and Straub (2018), the illusion of invulnerability occurs when certain people opt to ignore a current risk to maintain the idea that the environment is still safe and secure.

Individuals will always play a role in a security system's efficiency, regardless of how well designed or implemented it is. When it comes to avoiding system breaches, human considerations are crucial. We may create and deploy a secure technology, but we will fail until we address the human element that interacts with it. To effectively prevent malware transmission, it is critical to understand the factors that impact human decision-making regarding security countermeasures and how they influence human decision-making. The protection motivation theory (PMT) was employed in this study to understand better how to encourage people to take countermeasures against an attack. After surveying the literature, we noticed that most countermeasures proposed in earlier studies are technical, implying that this hypothesis applies to our research. Chenoweth, Minch, and Gattiker believe that this concept may be used to forecast the future adoption of protective devices (2019). After studying the PMT theory, we understood how attitudes and actions change in the presence of a clear threat. According to Rogers (2019), when the PMT is used, a threat will alter a victim's mindset, leading to an intention to engage in the protection-suggested behavior. We noticed that when a user knows the dangers presented by malware, the potential consequences of a successful attack, and the knowledge that they are vulnerable to attack, the user is more likely to take preventative measures.

Additionally, this theory examines various factors that determine how individuals react to dangers, and it may provide light on what humans need to carry out efficient countermeasures. Maar (2019) recommends that firms increase employee awareness of information security risks and educate them on preventive measures to improve the efficacy of their information security. The findings indicate that self-efficacy in social network protection is influenced by the development of information protection skills and the ease with which information protection instruments can be used, resulting in a greater uptake of information protection approaches on social networking sites (Martin et al., 2019).

Research questions

The following are the basic research question that will determine the research flow

What is a phishing attack?

How can I identify phishing attacks?

Why is phishing attack research relevant and vital?

How can I know that a phishing link has attacked my smartphone?

Where on the internet are phishing attacks common?

What can I do to avoid phishing attacks?

What should a phishing attack victim do immediately they encounter the problem?

What are the results or consequences of phishing to an individual? (Mahdi et al., 2019)

Are there different types of phishing?

Research methods-Quantitative Methodology

Using the technology threat avoidance theory, this study investigated the relationship between perceived severity, perceived susceptibility, perceived threat, safeguard effectiveness, safeguard cost, self-efficacy, avoidance motivation, avoidance behaviour, and security behaviour of mobile device users in relation to phishing attacks. The study contributes to an area of research that has remained largely unexplored: what drives mobile device users to avoid phishing attempts. It is widely accepted that the correlation between mobile device usage and assaults has sparked academic interest. Because of this study, researchers and practitioners will get a fresh perspective on how mobile device users' behaviour affects the success and frequency of phishing attempts in the future.

As part of our investigation into the issues raised by our problem statements I decided to use a Quantitative methodology, we interviewed twenty persons about their experiences with and reasons for real and manufactured online sites In order to relate phishing with the smartphone users. We use a number of techniques to spoof eight well-known websites and their respective URLs. Additionally, the research consulted nine reputable sources (Basit et al., 2021). After submitting their email addresses, users were sent to a navigation page containing links to all 17 websites in random order. We allowed participants to do the survey using their own telephones. All interviews took place through Zoom, a video conferencing application that enabled participants to share their screen with us, allowing us to see and question their behavior more easily.

Population and Sample

Participants in this research have to be 18 years of age or older and reside in the United States at the time of the study. The surveys were done online in response to the growing usage of mobile devices in the United States, and the results suggested a demographically diversified sample group, as predicted. According to the Pew Research Center, more than 60% of individuals use a mobile device to access the internet, and 15% of young adults between the ages of 18 and 29 depend significantly on their cellphones for online access. If participants were unfamiliar with phishing attacks, the survey's TTAT constructs may exhibit a high degree of variation, which will be utilized to identify moderating effects, as previously noted. Men and women aged 18 and over who used their mobile devices to access the internet and completed a web-based survey questionnaire were included in this research.

References

Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76(1), 139-154.

Burns, A. J., Johnson, M. E., & Caputo, D. D. (2019). Spear phishing in a barrel: Insights from a targeted phishing campaign. Journal of Organizational Computing and Electronic Commerce, 29(1), 24-39.

Butavicius, M., Parsons, K., Pattinson, M., & McCormac, A. (2016). Breaching the human firewall: Social engineering in phishing and spear-phishing emails. arXiv preprint arXiv:1606.00887.

Kwak, Y., Lee, S., Damiano, A., & Vishwanath, A. (2020). Why do users not report spear phishing emails?. Telematics and Informatics, 48, 101343.

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