Journal articles: 'Email phishing'

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Nmachi, Wosah Peace, and Thomas Win. "Phishing Mitigation Techniques: A Literature Survey." International Journal of Network Security & Its Applications 13, no. 2 (March 31, 2021): 63–72. http://dx.doi.org/10.5121/ijnsa.2021.13205.

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Email is a channel of communication which isconsideredto be a confidential medium of communication for exchange of information among individuals and organisations. The confidentiality consideration about email is no longer the case as attackers send malicious emails to users to deceive them into disclosing their private personal information such as username, password, and bank card details, etc. In search of a solution to combat phishing cybercrime attacks, different approaches have been developed. However, the traditional exiting solutions have beenlimited in assisting email users to identify phishing emails from legitimate ones. This paper reveals the different email and website phishing solutions in phishing attack detection. It first provides a literature analysis of different existing phishing mitigation approaches. It then provides a discussion on the limitations of the techniques, before concluding with anexplorationintohow phishing detection can be improved.

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Bullee, Jan-Willem, Lorena Montoya, Marianne Junger, and Pieter Hartel. "Spear phishing in organisations explained." Information & Computer Security 25, no. 5 (November 13, 2017): 593–613. http://dx.doi.org/10.1108/ics-03-2017-0009.

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Purpose The purpose of this study is to explore how the opening phrase of a phishing email influences the action taken by the recipient. Design/methodology/approach Two types of phishing emails were sent to 593 employees, who were asked to provide personally identifiable information (PII). A personalised spear phishing email opening was randomly used in half of the emails. Findings Nineteen per cent of the employees provided their PII in a general phishing email, compared to 29 per cent in the spear phishing condition. Employees having a high power distance cultural background were more likely to provide their PII, compared to those with a low one. There was no effect of age on providing the PII requested when the recipient’s years of service within the organisation is taken into account. Practical implications This research shows that success is higher when the opening sentence of a phishing email is personalised. The resulting model explains victimisation by phishing emails well, and it would allow practitioners to focus awareness campaigns to maximise their effect. Originality/value The innovative aspect relates to explaining spear phishing using four socio-demographic variables.

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Singh, Kuldeep, Palvi Aggarwal, Prashanth Rajivan, and Cleotilde Gonzalez. "What makes phishing emails hard for humans to detect?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 431–35. http://dx.doi.org/10.1177/1071181320641097.

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This research investigates the email features that make a phishing email difficult to detect by humans. We use an existing data set of phishing and ham emails and expand that data set by collecting annotations of the features that make the emails phishing. Using the new, annotated data set, we perform cluster analyses to identify the categories of emails and their attributes. We then analyze the accuracy of detection in each category. Our results indicate that the similarity of the features of phishing emails to benign emails, play a critical role in the accuracy of detection. The phishing emails that are most similar to ham emails had the lowest accuracy while the phishing emails that were most dissimilar to the ham emails were detected more accurately. Regression models reveal the contribution of phishing email’s features to phishing detection accuracy. We discuss the implications of these results to theory and practice.

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Mhaske-Dhamdhere, Vidya, and Sandeep Vanjale. "A novel approach for phishing emails real time classifica-tion using k-means algorithm." International Journal of Engineering & Technology 7, no. 1.2 (December 28, 2017): 96. http://dx.doi.org/10.14419/ijet.v7i1.2.9018.

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The dangers phishing becomes considerably bigger problem in online networking, for example, Facebook, twitter and Google+. The phishing is normally completed by email mocking or texting and it frequently guides client to enter points of interest at a phony sites whose look and feel are practically indistinguishable to the honest to goodness. Non-technical user resists learning of anti-phishing technic. Also not permanently remember phishing learning. Software solutions such as authentication and security warnings are still depending on end user action.In this paper we are mainly focus on a novel approach of real time phishing email classification using K-means algorithm. For this we uses 160 emails of last year computer engineering students. we get True positive of legitimate and phishing as 67% and 80% and true negative is 30 % and 20%.,which is very high so we ask same users reasons which I mainly categories into three categories ,look and feel of email, email technical parameters, and email structure.

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Welk, Allaire K., Kyung Wha Hong, Olga A. Zielinska, Rucha Tembe, Emerson Murphy-Hill, and Christopher B. Mayhorn. "Will the “Phisher-Men” Reel You In?" International Journal of Cyber Behavior, Psychology and Learning 5, no. 4 (October 2015): 1–17. http://dx.doi.org/10.4018/ijcbpl.2015100101.

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Phishing is an act of technology-based deception that targets individuals to obtain information. To minimize the number of phishing attacks, factors that influence the ability to identify phishing attempts must be examined. The present study aimed to determine how individual differences relate to performance on a phishing task. Undergraduate students completed a questionnaire designed to assess impulsivity, trust, personality characteristics, and Internet/security habits. Participants performed an email task where they had to discriminate between legitimate emails and phishing attempts. Researchers assessed performance in terms of correctly identifying all email types (overall accuracy) as well as accuracy in identifying phishing emails (phishing accuracy). Results indicated that overall and phishing accuracy each possessed unique trust, personality, and impulsivity predictors, but shared one significant behavioral predictor. These results present distinct predictors of phishing susceptibility that should be incorporated in the development of anti-phishing technology and training.

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Verma, Priyanka, Anjali Goyal, and Yogita Gigras. "Email phishing: text classification using natural language processing." Computer Science and Information Technologies 1, no. 1 (May 1, 2020): 1–12. http://dx.doi.org/10.11591/csit.v1i1.p1-12.

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Phishing is networked theft in which the main motive of phishers is to steal any person’s private information, its financial details like account number, credit card details, login information, payment mode information by creating and developing a fake page or a fake web site, which look completely authentic and genuine. Nowadays email phishing has become a big threat to all, and is increasing day by day. Moreover detection of phishing emails have been considered an important research issue as phishing emails have been increasing day by day. Various techniques have been introduced and applied to deal with such a big issue. The major objective of this research paper is giving a detailed description on the classification of phishing emails using the natural language processing concepts. NLP (natural language processing) concepts have been applied for the classification of emails, along with that accuracy rate of various classifiers have been calculated. The paper is presented in four sections. An introduction about phishing its types, its history, statistics, life cycle, motivation for phishers and working of email phishing have been discussed in the first section. The second section covers various technologies of phishing- email phishing and also description of evaluation metrics. An overview of the various proposed solutions and work done by researchers in this field in form of literature review has been presented in the third section. The solution approach and the obtained results have been defined in the fourth section giving a detailed description about NLP concepts and working procedure.

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Rastenis, Justinas, Simona Ramanauskaitė, Ivan Suzdalev, Kornelija Tunaitytė, Justinas Janulevičius, and Antanas Čenys. "Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation." Electronics 10, no. 6 (March 12, 2021): 668. http://dx.doi.org/10.3390/electronics10060668.

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Spamming and phishing are two types of emailing that are annoying and unwanted, differing by the potential threat and impact to the user. Automated classification of these categories can increase the users’ awareness as well as to be used for incident investigation prioritization or automated fact gathering. However, currently there are no scientific papers focusing on email classification concerning these two categories of spam and phishing emails. Therefore this paper presents a solution, based on email message body text automated classification into spam and phishing emails. We apply the proposed solution for email classification, written in three languages: English, Russian, and Lithuanian. As most public email datasets almost exclusively collect English emails, we investigate the suitability of automated dataset translation to adapt it to email classification, written in other languages. Experiments on public dataset usage limitations for a specific organization are executed in this paper to evaluate the need of dataset updates for more accurate classification results.

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Lawson, Patrick, Olga Zielinska, Carl Pearson, and Christopher B. Mayhorn. "Interaction of Personality and Persuasion Tactics in Email Phishing Attacks." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1331–33. http://dx.doi.org/10.1177/1541931213601815.

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Phishing is a social engineering tactic where a malicious actor impersonates a trustworthy third party with the intention of tricking the user into divulging sensitive information. Previous social engineering research has shown an interaction between personality and the persuasion principle used. This study was conducted to investigate whether this interaction is present in the realm of email phishing. To investigate this, we used a personality inventory and an email identification task (phishing or legitimate). The emails used in the identification task utilize four of Cialdini’s persuasion principles. Our data confirms previous findings that high extroversion is predictive of increased susceptibility to phishing attacks. In addition, we identify multiple interactions between personality and specific persuasion principles. We also report the overarching efficacy of various persuasion principles on phishing email identification accuracy.

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B, Manoj, and Fancy C. "Checksec Email Phishi Trasher Tool." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 363. http://dx.doi.org/10.14419/ijet.v7i4.6.28442.

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In this faster networking world, Phishing has become the most popular practice among the criminals of the web. Various phishing types are deceptive, spear phishing, Email phishing, malware-based phishing, key loggers, session hijacking, man in middle, Trojan, DNS poisoning, cross-site scripting attacks. There is a need for automated tools to solve the problem by the victim side. Existing methods are regularly too tedious to be utilized in reality as far as recognition and relief session. Hence it is decided to propose a model which focuses on detecting and preventing the email phishing attack. In this paper, we present PhishiTrasher, another discovery and relief approach, where we initially propose another system for Deep Packet Inspection afterward use in phishing exercises through email and electronic correspondence. The proposed packet inspection approach comprises parts, vulnerable mark arrangement then continuous DPI. With the help of the phishing assault marks, outline the continuous DPI with the goal that PhishiTrasher can adapt to address the elements of phishing assaults in reality. PhishiTrasher gives better system movement administration to containing phishing assaults since it has the worldwide perspective of a system. Moreover, we assess PhishiTrasher utilizing a true test bed condition and databases comprising of genuine email with installed joins. Our broad test contemplate demonstrates that PhishiTrasher gives a powerful and effective answer for prevent phishing attacks through email. Results demonstrate that profiling should be possible with very high genuine.

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Vishwanath, Arun, Brynne Harrison, and Yu Jie Ng. "Suspicion, Cognition, and Automaticity Model of Phishing Susceptibility." Communication Research 45, no. 8 (February 10, 2016): 1146–66. http://dx.doi.org/10.1177/0093650215627483.

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Social-psychological research on phishing has implicated ineffective cognitive processing as the key reason for individual victimization. Interventions have consequently focused on training individuals to better detect deceptive emails. Evidence, however, points to individuals sinking into patterns of email usage that within a short period of time results in an attenuation of the training effects. Thus, individual email habits appear to be another predictor of their phishing susceptibility. To comprehensively account for all these influences, we built a model that accounts for the cognitive, preconscious, and automatic processes that potentially leads to phishing-based deception. The resultant suspicion, cognition, and automaticity model (SCAM) was tested using two experimental studies in which participants were subjected to different types of email-based phishing attacks.

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Cooper, Molly, Yair Levy, Ling Wang, and Laurie Dringus. "Subject matter experts’ feedback on a prototype development of an audio, visual, and haptic phishing email alert system." Online Journal of Applied Knowledge Management 8, no. 2 (December 29, 2020): 107–21. http://dx.doi.org/10.36965/ojakm.2020.8(2)107-121.

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Phishing emails, also defined as email spam messages, present a threat to both personal and organizational data loss. About 93% of cybersecurity incidents are due to phishing and/or social engineering. Users are continuing to click on phishing links in emails even after phishing awareness training. Thus, it appears that there is a strong need for creative ways to alert and warn users to signs of phishing in emails. ‘System 2 Thinking Mode’ (S2) describes an individual in a more aware state of mind when making important decisions. Ways to trigger S2 include audio alerts, visual alerts, and haptic/vibrations. Assisting the user in noticing signs of phishing in emails could possibly be studied through the delivery of audio, visual, and haptic (vibration) alerts and warnings. This study outlines the empirical results from 32 Subject Matter Experts (SMEs) on an initial prototype design and development of an email phishing alert and warning system. The prototype will be developed to alert and warn users to the signs of phishing in emails in an attempt to switch them to an S2 state of mind. The preliminary results of the SMEs indicated that several features for a phishing alert and warning system could be assembled, resulting in a mobile phishing alert and warning prototype. Visual icons were chosen for each sign of phishing used in the prototype, as well as voice over warnings and haptic vibrations. The preliminary results also determined task measurements, ‘ability to notice’, and ‘time to notice’ signs of phishing in emails.

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Akinyelu, Andronicus A., and Aderemi O. Adewumi. "Classification of Phishing Email Using Random Forest Machine Learning Technique." Journal of Applied Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/425731.

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Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

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Lötter, André, and Lynn Futcher. "A framework to assist email users in the identification of phishing attacks." Information & Computer Security 23, no. 4 (October 12, 2015): 370–81. http://dx.doi.org/10.1108/ics-10-2014-0070.

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Purpose – The purpose of this paper is to propose a framework to address the problem that email users are not well-informed or assisted by their email clients in identifying possible phishing attacks, thereby putting their personal information at risk. This paper therefore addresses the human weakness (i.e. the user’s lack of knowledge of phishing attacks which causes them to fall victim to such attacks) as well as the software related issue of email clients not visually assisting and guiding the users through the user interface. Design/methodology/approach – A literature study was conducted in the main field of information security with a specific focus on understanding phishing attacks and a modelling technique was used to represent the proposed framework. This paper argues that the framework can be suitably implemented for email clients to raise awareness about phishing attacks. To validate the framework as a plausible mechanism, it was reviewed by a focus group within the School of Information and Communication Technology (ICT) at the Nelson Mandela Metropolitan University (NMMU). The focus group consisted of academics and research students in the field of information security. Findings – This paper argues that email clients should make use of feedback mechanisms to present security related aspects to their users, so as to make them aware of the characteristics pertaining to phishing attacks. To support this argument, it presents a framework to assist email users in the identification of phishing attacks. Research limitations/implications – Future research would yield interesting results if the proposed framework were implemented into an existing email client to determine the effect of the framework on the user’s level of awareness of phishing attacks. Furthermore, the list of characteristics could be expanded to include all phishing types (such as clone phishing, smishing, vishing and pharming). This would make the framework more dynamic in that it could then address all forms of phishing attacks. Practical implications – The proposed framework could enable email clients to provide assistance through the user interface. Visibly relaying the security level to the users of the email client, and providing short descriptions as to why a certain email is considered suspicious, could result in raising the awareness of the average email user with regard to phishing attacks. Originality/value – This research presents a framework that email clients can use to identify common forms of normal and spear phishing attacks. The proposed framework addresses the problem that the average Internet user lacks a baseline level of online security awareness. It argues that the email client is the ideal place to raise the awareness of users regarding phishing attacks.

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Greitzer, Frank L., Wanru Li, Kathryn B. Laskey, James Lee, and Justin Purl. "Experimental Investigation of Technical and Human Factors Related to Phishing Susceptibility." ACM Transactions on Social Computing 4, no. 2 (June 26, 2021): 1–48. http://dx.doi.org/10.1145/3461672.

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This article reports on a simulated phishing experiment targeting 6,938 faculty and staff at George Mason University. The three-week phishing campaign employed three types of phishing exploits and examined demographic, linked workstation/network monitoring audit data, and a variety of behavioral and psychological factors measured via pre- and post-campaign surveys. While earlier research studies have reported disparate effects of gender and age, the present results suggest that these effects are not significant or are of limited strength and that other underlying factors may be more important. Specifically, significant differences in phishing susceptibility were obtained for different email contexts and based on whether individuals have been successfully phished before (these people were more likely to succumb to subsequent phishing emails in our study). Further, participants who responded to phishing exploits scored higher on impulsivity than the non-clickers. Also, participants whose survey responses indicated that they had more appropriate online “security hygiene habits,” such as checking the legitimacy of links, were less likely to be successfully phished in our campaign. Participants whose post-campaign survey responses indicated that they were suspicious of a phishing email message in our campaign were far less likely to click on the phishing link than those who were not suspicious. Similar results were obtained for judgments of pertinence of the email. Participants who indicated that they thought about the negative consequences of clicking the link were less likely to do so than participants who did not think about the negative consequences. Implications for effective training and awareness are discussed.

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Wang, Jingguo, Yuan Li, and H. Raghav Rao. "Overconfidence in Phishing Email Detection." Journal of the Association for Information Systems 17, no. 11 (November 2016): 759–83. http://dx.doi.org/10.17705/1jais.00442.

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Priestman, Ward, Tony Anstis, Isabel G. Sebire, Shankar Sridharan, and Neil J. Sebire. "Phishing in healthcare organisations: threats, mitigation and approaches." BMJ Health & Care Informatics 26, no. 1 (September 2019): e100031. http://dx.doi.org/10.1136/bmjhci-2019-100031.

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IntroductionHealthcare data have significant value as a potential target for hackers. Phishing is a method of exploitation for malicious reasons using targeted communications (email/messaging). This study reports on an internal evaluation targeting hospital staff and summarises peer-reviewed literature regarding phishing and healthcare.MethodsAn assessment was performed as part of cybersecurity activity during a designated test period using multiple credential harvesting approaches through staff email. We also searched the medical-related literature to identify relevant phishing-related publications.ResultsDuring the 1-month testing period, the organisation received 858 200 emails: 139 400 (16%) marketing, 18 871 (2%) identified as potential threats. Of 143 million internet transactions, around 5 million (3%) were suspected threats. 468 employee email addresses were identified from public data and targeted through phishing using a range of payloads including attachments and malicious links; however, no credentials were recovered or malicious files downloaded. Several hospital employees were, however, identified on social media profiles, including some tricked into accepting false friend requests.DiscussionHealthcare organisations are increasingly moving to digital systems, but healthcare professionals have limited awareness of threats. Increasing emphasis on ‘cyberhygiene’ and information governance through mandatory training increases understanding of these risks. While no credentials were harvested in this study, since up to 5% of emails/internet traffic are suspicious, the need for robust firewalls, cybersecurity infrastructure, IT policies and, most importantly of all, staff training, is emphasised.ConclusionHospitals receive a significant volume of potentially malicious emails. While many staff appear to be aware of phishing and respond appropriately, ongoing education is required across the spectrum of cybersecurity, with specific emphasis around ‘leakage’ of information on social media.

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Fatima, Rubia, Affan Yasin, Lin Liu, and Jianmin Wang. "How persuasive is a phishing email? A phishing game for phishing awareness." Journal of Computer Security 27, no. 6 (October 11, 2019): 581–612. http://dx.doi.org/10.3233/jcs-181253.

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Dadvandipour, Samad, and Aadil Gani Ganie. "Analyzing and predicting spear-phishing using machine learning methods." Multidiszciplináris tudományok 10, no. 4 (2020): 262–73. http://dx.doi.org/10.35925/j.multi.2020.4.30.

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Phishing implies misdirecting the client by masking himself/herself as a reliable individual, to take the Critical material, for example, bank account number, credit card numbers, and so on; one of the noticeably utilized Phishing these days is spear phishing, and it is one of the effective phishing assaults given its social, mental boundaries. In this paper, we will mitigate the impact of spear phishing by utilizing the multi-layer approach. The multi-layer approach is the best method of managing the web interruption, as the intruder needs to experience shift levels. Practically all the scientists are dealing with the content of the email; however, this paper picks a novel method to counter the phishing messages by utilizing both the attachment and content of an email. We applied sentimental analysis on emails, including both content of the email and the attachment, to check whether they are spam or not using SVM classifier and Randomforest Classifier; the former showed 96 percent accuracy while, as later offers 97.66 percent accuracy. SVM showed false-positive 0 percent and false-negative 4 percent, while RandomForest showed 0 percent false-positive and 2.33 percent false-negative ratios. We also performed topic modeling using LDA(Latent Dirichlet Allocation)) from Gensim package to get the dominant topics in our dataset. We visualized the results of our topic model using pyLDvis. The perplexity and coherence score of our topic model is -12.897670565510511 and 0.44700287476452394, respectively.

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Kikerpill, Kristjan, and Andra Siibak. "Living in a Spamster's Paradise: Deceit and Threats in Phishing Emails." Masaryk University Journal of Law and Technology 13, no. 1 (June 30, 2019): 45–66. http://dx.doi.org/10.5817/mujlt2019-1-3.

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The prevalence of using email as a communication tool for personal and professional purposes makes it a significant attack vector for cybercriminals. Consensus exists that phishing, i.e. use of socially engineered messages to convince recipients into performing actions that benefit the sender, is widespread as a negative phenomenon. However, little is known about its true extent from a criminal law perspective. Similar to how the treatment of phishing in a generic manner does not adequately inform the relevant law, a case-by-case legal analysis of seemingly independent offences would not reveal the true scale and extent of phishing as a social phenomenon. The current research addresses this significant gap in the literature. To study this issue, a qualitative text analysis was performed on (N=42) emails collected over a 30-day period from two email accounts. Secondly, the phishing emails were analysed from an Estonian criminal law perspective. The legal analysis shows that in the period of only one month, the accounts received what amounts to 3 instances of extortion, 29 fraud attempts and 10 cases of personal data processing related misdemeanour offences.

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Anandpara, Rahul. "Secured Mail Transformation System Using Machine Learnin." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 20, 2021): 1880–86. http://dx.doi.org/10.22214/ijraset.2021.36764.

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Today, Email Spam has become a major problem, with Rapid increament of internet users, Email spams is also increasing. People are using email spam for illegal and unethical conducts, phishing and fraud. Sending malicious link through spam emails which can damage the system and can also seek in into your system. Spammer creates a fake profile and email account which is easier for them. These spammers target those peoples who are not aware about frauds. So there is a need to identify the fraud in terms of spam emails. In this paper we will identify the spam by using machine learning algorithms.

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Adewumi, Oluyinka Aderemi, and Ayobami Andronicus Akinyelu. "A hybrid firefly and support vector machine classifier for phishing email detection." Kybernetes 45, no. 6 (June 6, 2016): 977–94. http://dx.doi.org/10.1108/k-07-2014-0129.

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Purpose – Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars has been lost by many companies and individuals. The global impact of phishing attacks will continue to be on the increase and thus a more efficient phishing detection technique is required. The purpose of this paper is to investigate and report the use of a nature inspired based-machine learning (ML) approach in classification of phishing e-mails. Design/methodology/approach – ML-based techniques have been shown to be efficient in detecting phishing attacks. In this paper, firefly algorithm (FFA) was integrated with support vector machine (SVM) with the primary aim of developing an improved phishing e-mail classifier (known as FFA_SVM), capable of accurately detecting new phishing patterns as they occur. From a data set consisting of 4,000 phishing and ham e-mails, a set of features, suitable for phishing e-mail detection, was extracted and used to construct the hybrid classifier. Findings – The FFA_SVM was applied to a data set consisting of up to 4,000 phishing and ham e-mails. Simulation experiments were performed to evaluate and compared the performance of the classifier. The tests yielded a classification accuracy of 99.94 percent, false positive rate of 0.06 percent and false negative rate of 0.04 percent. Originality/value – The hybrid algorithm has not been earlier apply, as in this work, to the classification and detection of phishing e-mail, to the best of the authors’ knowledge.

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Bergholz, André, Jan De Beer, Sebastian Glahn, Marie-Francine Moens, Gerhard Paaß, and Siehyun Strobel. "New filtering approaches for phishing email." Journal of Computer Security 18, no. 1 (January 1, 2010): 7–35. http://dx.doi.org/10.3233/jcs-2010-0371.

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Rohini, P., and K. Ramya. "Phishing Email Filtering Techniques A Survey." International Journal of Computer Trends and Technology 17, no. 1 (November 25, 2014): 18–21. http://dx.doi.org/10.14445/22312803/ijctt-v17p105.

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Islam, Muhammad Nazrul, Tarannum Zaki, Md Sami Uddin, and Md Mahedi Hasan. "Security Threats for Big Data." International Journal of Information Communication Technologies and Human Development 10, no. 4 (October 2018): 1–18. http://dx.doi.org/10.4018/ijicthd.2018100101.

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With the advancement of modern science and technology, data emerging from different fields are escalating gradually. Recently, with this huge amount of data, Big Data has become a source of immense opportunities for large scale organizations related to various business sectors as well as to information technology (IT) professionals. Hence, one of the biggest challenges of this context is the security of this big set of data in different ICT based organizations. The fundamental objective of this article is to explore how big data may create security challenges in email communication. As an outcome, this article first shows that big data analysis helps to understand the behavior or interest of email users, which in turn can help phishers to create the phishing sites or emails that result in IT security threat; second, the article finds that phishing e-mail generation based on the (email) users' behavior can break an organization's IT security; and finally, a framework was proposed that would help to enhance the security of email communication.

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Zielinska, Olga A., Allaire K. Welk, Christopher B. Mayhorn, and Emerson Murphy-Hill. "A Temporal Analysis of Persuasion Principles in Phishing Emails." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 765–69. http://dx.doi.org/10.1177/1541931213601175.

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Eight hundred eighty-seven phishing emails from Arizona State University, Brown University, and Cornell University were assessed by two reviewers for Cialdini’s six principles of persuasion: authority, social proof, liking/similarity, commitment/consistency, scarcity, and reciprocation. A correlational analysis of email characteristics by year revealed that the persuasion principles of commitment/consistency and scarcity have increased over time, while the principles of reciprocation and social proof have decreased over time. Authority and liking/similarity revealed mixed results with certain characteristics increasing and others decreasing. Results from this study can inform user training of phishing emails and help cybersecurity software to become more effective.

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Molinaro, Kylie A., and Matthew L. Bolton. "Using the Lens Model and Cognitive Continuum Theory to Understand the Effects of Cognition on Phishing Victimization." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 173–77. http://dx.doi.org/10.1177/1071181319631044.

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With the growing threat of phishing emails and the limited effectiveness of current mitigation approaches, there is an urgent need to better understand what leads to phishing victimization. There is a limited body of phishing research that identified cognitive automaticity as a potential factor, but more research on the relationship between user cognition and victimization is needed. Additionally, the current phishing research has not considered the characteristics of the environment in which phishing judgments are made. To fill these gaps, this work used the analysis capabilities afforded by the double system lens model (a judgment analysis technique) and the cognitive continuum theory, specifically the task continuum index and the cognitive continuum index. By calculating a task continuum index score, the cognition best suited for the email sorting task was identified. This calculation resulted in a value which indicated that more analytical cognition was most effective. The cognitive continuum index score evaluated the participants’s cognition level while making judgments. The relationships between these measures and achievement were evaluated. Results indicated that more analytical cognition was associated with lower rates of phishing victimization. This work provides a deeper insight into the phishing problem and has implications for combating phishing.

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Yang, Zhuorao, Chen Qiao, Wanling Kan, and Junji Qiu. "Phishing Email Detection Based on Hybrid Features." IOP Conference Series: Earth and Environmental Science 252 (July 9, 2019): 042051. http://dx.doi.org/10.1088/1755-1315/252/4/042051.

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Almomani, Ammar, B. B. Gupta, Samer Atawneh, A. Meulenberg, and Eman Almomani. "A Survey of Phishing Email Filtering Techniques." IEEE Communications Surveys & Tutorials 15, no. 4 (2013): 2070–90. http://dx.doi.org/10.1109/surv.2013.030713.00020.

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A. Abdullah, Amina, Loay E. George, and J. Mohammed. "Email Phishing Detection System Using Neural Network." Research Journal of Information Technology 6, no. 3 (August 5, 2015): 39–43. http://dx.doi.org/10.19026/rjit.6.2164.

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Morovati, Kamran. "Detection of Phishing Emails with Email Forensic Analysis and Machine Learning Techniques." International Journal of Cyber-Security and Digital Forensics 8, no. 2 (2019): 98–107. http://dx.doi.org/10.17781/p002568.

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Mohammed, Mazin Abed, Dheyaa Ahmed Ibrahim, and Akbal Omran Salman. "Adaptive intelligent learning approach based on visual anti-spam email model for multi-natural language." Journal of Intelligent Systems 30, no. 1 (January 1, 2021): 774–92. http://dx.doi.org/10.1515/jisys-2021-0045.

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Abstract Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest.

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Broadhurst, Roderic, Katie Skinner, Nicholas Sifniotis, Bryan Matamoros-Macias, and Yuguang Ipsen. "Phishing and Cybercrime Risks in a University Student Community." International Journal of Cybersecurity Intelligence and Cybercrime 2, no. 1 (February 1, 2019): 4–23. http://dx.doi.org/10.52306/02010219rzex445.

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In an exploratory quasi-experimental observational study, 138 participants recruited during a university orientation week were exposed to social engineering directives in the form of fake email or phishing attacks over several months in 2017. These email attacks attempted to elicit personal information from participants or entice them into clicking links which may have been compromised in a real-world setting. The study aimed to determine the risks of cybercrime for students by observing their responses to social engineering and exploring attitudes to cybercrime risks before and after the phishing phase. Three types of scam emails were distributed that varied in the degree of individualization: generic, tailored, and targeted or ‘spear.’ To differentiate participants on the basis of cybercrime awareness, participants in a ‘Hunter’ condition were primed throughout the study to remain vigilant to all scams, while participants in a ‘Passive’ condition received no such instruction. The study explored the influence of scam type, cybercrime awareness, gender, IT competence, and perceived Internet safety on susceptibility to email scams. Contrary to the hypotheses, none of these factors were associated with scam susceptibility. Although, tailored and individually crafted email scams were more likely to induce engagement than generic scams. Analysis of all the variables showed that international students and first year students were deceived by significantly more scams than domestic students and later year students. A Generalized Linear Model (GLM) analysis was undertaken to further explore the role of all the variables of interest and the results were consistent with the descriptive findings showing that student status (domestic compared to international) and year of study (first year student compared to students in second, third and later years of study) had a higher association to the risk of scam deception. Implications and future research directions are discussed.

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ALmomani, Ammar. "Phishing Dynamic Evolving Neural Fuzzy Framework for Online Detection “Zero-day” Phishing Email." Indian Journal of Science and Technology 6, no. 1 (January 20, 2013): 1–5. http://dx.doi.org/10.17485/ijst/2013/v6i1.18.

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Yasin, Adwan, and Abdelmunem Abuhasan. "An Intelligent Classification Model for Phishing Email Detection." International Journal of Network Security & Its Applications 8, no. 4 (July 30, 2016): 55–72. http://dx.doi.org/10.5121/ijnsa.2016.8405.

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Derouet, Estelle. "Fighting phishing and securing data with email authentication." Computer Fraud & Security 2016, no. 10 (October 2016): 5–8. http://dx.doi.org/10.1016/s1361-3723(16)30079-3.

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Perrault, Evan K. "Using an Interactive Online Quiz to Recalibrate College Students’ Attitudes and Behavioral Intentions About Phishing." Journal of Educational Computing Research 55, no. 8 (March 23, 2017): 1154–67. http://dx.doi.org/10.1177/0735633117699232.

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Despite improved spam filtering technology, phishing continues to be a prevalent threat for college students. The current study found that approximately 4-in-10 of the students surveyed ( N = 462) indicate they do not know what phishing is and the threat it poses. Students also report initially overestimating their confidence to successfully recognize phishing attempts, and underestimating their susceptibility to being the victim of an attack. By completing an interactive online phishing quiz, which explained what to look for in both counterfeit and legitimate email messages, students’ self-efficacy to identify phishing attempts increased, as did their perceived susceptibility to phishing attacks, their perceptions of the severity of phishing, their intentions to learn more about the topic, and their intentions to discuss phishing with others. These results indicate that a simple, interactive online phishing quiz could be used as an effective teaching tool to supplement existing educational attempts regarding phishing on college campuses.

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Shubhankar, Shubhankar, Siddhartha Bhaumik, and Prakash Biswagar. "Detection and Classification of Malicious Websites." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 1, 2021): 120–31. http://dx.doi.org/10.51201/jusst/21/05228.

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Phishing is quite possibly the most appealing technique used by attackers in the point of taking the individual subtleties of unsuspected individuals. Phishing sites are essentially tricks that are used by data fraud hoodlums and fakes. They use spam, fake sites made to look like the first sites, email, and direct messages to trick somebody into sharing significant information, like passwords and secret information. New enemies of phishing techniques are coming out each day, yet attackers think of new ways by focusing on all the new enemies of phishing techniques. So there is an earnest requirement for new strategies for the expectation of phishing sites. The paper portrays the correlation models in the classification of phishing sites for expectation utilizing distinctive Machine learning models. Different models are used for predicting which model gives the best exactness in phishing site classification. All the information is classified as either Benign for substantial Websites or Phish as Phishing Websites. Results have generated that show RF gives the best performance on this dataset for the classification of phishing sites.

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Kumar, Abhishek, Jyotir Moy Chatterjee, and Vicente García Díaz. "A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (February 1, 2020): 486. http://dx.doi.org/10.11591/ijece.v10i1.pp486-493.

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Phishing attacks are one of the slanting cyber-attacks that apply socially engineered messages that are imparted to individuals from expert hackers going for tricking clients to uncover their delicate data, the most mainstream correspondence channel to those messages is through clients' emails. Phishing has turned into a generous danger for web clients and a noteworthy reason for money related misfortunes. Therefore, different arrangements have been created to handle this issue. Deceitful emails, also called phishing emails, utilize a scope of impact strategies to convince people to react, for example, promising a fiscal reward or summoning a feeling of criticalness. Regardless of far reaching alerts and intends to instruct clients to distinguish phishing sends, these are as yet a pervasive practice and a worthwhile business. The creators accept that influence, as a style of human correspondence intended to impact others, has a focal job in fruitful advanced tricks. Cyber criminals have ceaselessly propelling their techniques for assault. The current strategies to recognize the presence of such malevolent projects and to keep them from executing are static, dynamic and hybrid analysis. In this work we are proposing a hybrid methodology for phishing detection incorporating feature extraction and classification of the mails using SVM. At last, alongside the chose features, the PNN characterizes the spam mails from the genuine mails with more exactness and accuracy.

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Ali, Ghassan Ahmed. "Protecting Users from Phishing Email through Awareness and Training." Indian Journal of Science and Technology 12, no. 25 (July 1, 2019): 1–9. http://dx.doi.org/10.17485/ijst/2019/v12i25/145743.

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Chowdhury, M. U., J. H. Abawajy, A. V. Kelarev, and T. Hochin. "Multilayer hybrid strategy for phishing email zero-day filtering." Concurrency and Computation: Practice and Experience 29, no. 23 (July 22, 2016): e3929. http://dx.doi.org/10.1002/cpe.3929.

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C.Tayade, Pranal, and Prof Avinash P.Wadhe. "Review Paper on Privacy Preservation through Phishing Email Filter." International Journal of Engineering Trends and Technology 9, no. 12 (March 25, 2014): 600–604. http://dx.doi.org/10.14445/22315381/ijett-v9p314.

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Volkamer, Melanie, Karen Renaud, Benjamin Reinheimer, and Alexandra Kunz. "User experiences of TORPEDO: TOoltip-poweRed Phishing Email DetectiOn." Computers & Security 71 (November 2017): 100–113. http://dx.doi.org/10.1016/j.cose.2017.02.004.

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Wang, Jingguo, Tejaswini Herath, Rui Chen, Arun Vishwanath, and H. Raghav Rao. "Research Article Phishing Susceptibility: An Investigation Into the Processing of a Targeted Spear Phishing Email." IEEE Transactions on Professional Communication 55, no. 4 (December 2012): 345–62. http://dx.doi.org/10.1109/tpc.2012.2208392.

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Chen, Jing, Scott Mishler, and Bin Hu. "Conveying Automation Reliability and Automation Error Type An Empirical Study in the Cyber Domain." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 172–73. http://dx.doi.org/10.1177/1541931218621040.

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Background Emails have become an integral part of our daily life and work. Phishing emails are often disguised as trustworthy ones and attempt to obtain sensitive information for malicious reasons (Egelman, Cranor, Hong, 2008;). Anti-phishing tools have been designed to help users detect phishing emails or websites (Egelman, et al., 2008; Yang, Xiong, Chen, Proctor, & Li, 2017). However, like any other types of automation aids, these tools are not perfect. An anti-phishing system can make errors, such as labeling a legitimate email as phishing (i.e., a false alarm) or assuming a phishing email as legitimate (i.e., a miss). Human trust in automation has been widely studied as it affects how the human operator interacts with the automation system, which consequently influences the overall system performance (Dzindolet, Peterson, Pomranky, Pierce, & Beck, 2003; Lee & Moray, 1992; Muir, 1994; Sheridan & Parasuraman, 2006). With interacting with an automation system, the human operator should calibrate his or her trust level to trust a system that is capable but distrust a system that is incapable (i.e., trust calibration; Lee & Moray, 1994; Lee & See, 2004; McGuirl & Sarter, 2006). Among the various system capabilities, automation reliability is one of the most important factors that affect trust, and it is widely accepted that higher reliability levels lead to higher trust levels (Desai et al., 2013; Hoff & Bashir, 2015). How well these capabilities are conveyed to the operator is essential (Lee & See, 2004). There are two general ways of conveying the system capabilities: through an explicit description of the capabilities (i.e., description), or through experiencing the system (i.e., experience). These two ways of conveying information have been studied widely in human decision-making literature (Wulff, Mergenthaler-Canseco, & Hertwig, 2018). Yet, there has not been systematic investigation on these different methods of conveying information in the applied area of human-automation interaction (but see Chen, Mishler, Hu, Li, & Proctor, in press; Mishler et al., 2017). Furthermore, trust and reliance on automation is not only affected by the reliability of the automation, but also by the error types, false alarms and misses (Chancey, Bliss, Yamani, & Handley, 2017; Dixon & Wickens, 2006). False alarms and misses affect human performance in qualitatively different ways, with more serious damage being caused by false-alarmprone automation than by miss-prone automation (Dixon, Wickens, & Chang, 2004). In addition, false-alarm-prone automation reduces compliance (i.e., the operator’s reaction when the automation presents a warning); and miss-prone automation reduces reliance (i.e., the operator’s inaction when the automation remains silent; Chancey et al., 2017). Current Study The goal of the current study was to examine how the methods of conveying system reliability and automation error type affect human decision making and trust in automation. The automation system was a phishing-detection system, which provided recommendations to users as to whether an email was legitimate or phishing. The automation reliability was defined as the percentage of correct recommendations (60% vs. 90%). For each reliability level, there were a false-alarm condition, with all the automation errors being false alarms, and a miss condition, with all the errors being misses. The system reliability was conveyed through description (with an exact percentage described to the user) or experience (with immediate feedback to help the user learn; Barron, & Erev, 2003). A total of 510 participants were recruited and completed the experiment online through Amazon Mechanical Turk. The experimental task consisted of classifying 20 emails as phishing and legitimate, with a phishing-detection system providing recommendations. At the end of the experiment, participants rated their trust in this automated aid system. The measures included a performance measure (the decision accuracy made by the participants), as well as two trust measures (participants’ agreement rate with the phishing-detection system, and their self-reported trust in the system). Our results showed that higher system reliability and feedback increased accuracy significantly, but description or error type alone did not affect accuracy. In terms of the trust measures, false alarms led to lower agreement rates than did misses. With a less reliable system, though, the misses caused a problem of inappropriately higher agreement rates; this problem was reduced when feedback was provided for the unreliable system, indicating a trust-calibration role of feedback. Self-reported trust showed similar result patterns to agreement rates. Performance was improved with higher system reliability, feedback, and explicit description. Design implications of the results included that (1) both feedback and description of the system reliability should be presented in the interface of an automation aid whenever possible, provided that the aid is reliable, and (2) for systems that are unreliable, false alarms are more desirable than misses, if one has to choose between the two.

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Sarno, Dawn M., Joanna E. Lewis, Corey J. Bohil, and Mark B. Neider. "Which Phish Is on the Hook? Phishing Vulnerability for Older Versus Younger Adults." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 5 (June 25, 2019): 704–17. http://dx.doi.org/10.1177/0018720819855570.

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ObjectiveTo determine if there are age-related differences in phishing vulnerability and if those differences exist under various task conditions (e.g., framing and time pressure).BackgroundPrevious research suggests that older adults may be a vulnerable population to phishing attacks. Most research exploring age differences has used limiting designs, including retrospective self-report measures and restricted email sets.MethodThe present studies explored how older and younger adults classify a diverse sample of 100 legitimate and phishing emails. In Experiment 1, participants rated the emails as either spam or not spam. Experiment 2 explored how framing would alter the results when participants rated emails as safe or not safe. In Experiment 3, participants performed the same task as Experiment 1, but were put under time pressure.ResultsNo age differences were observed in overall classification accuracy across the three experiments, rather all participants exhibited poor performance (20%–30% errors). Older adults took significantly longer to make classifications and were more liberal in classifying emails as spam or not safe. Time pressure seemed to remove this bias but did not influence overall accuracy.ConclusionOlder adults appear to be more cautious when classifying emails. However, being extra careful may come at the cost of classification speed and does not seem to improve accuracy.ApplicationAge demographics should be considered in the implementation of a cyber-training methodology. Younger adults may be less vigilant against cyber threats than initially predicted; older adults might be less prone to deception when given unlimited time to respond.

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Chou, Frank Kun-Yueh, Abbott Po-Shun Chen, and Vincent Cheng-Lung Lo. "Mindless Response or Mindful Interpretation: Examining the Effect of Message Influence on Phishing Susceptibility." Sustainability 13, no. 4 (February 4, 2021): 1651. http://dx.doi.org/10.3390/su13041651.

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Influence-based deceptive messages constantly play a critical role in email phishing attacks. However, the literature lacks adequate understanding about how phishing messages with attractive and coercive influence result in the receivers’ adverse consequences. We therefore take the perspective of mindless response and mindful interpretation to address this issue by examining comprehensive relationships among message influence, cognitive processing, and phishing susceptibility. To accomplish this, a survey approach was adopted after a simulated phishing attack was conducted in campuses. Our empirical evidence shows that both message influence and cognitive processing can lead to people being phished, and a combination of different influences can also trigger cognitive processing. This research makes contributions to the literature of information security, persuading influence, and cognitive psychology.

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Laksono, Eko, Achmad Basuki, and Fitra Bachtiar. "Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 2 (April 20, 2020): 377–83. http://dx.doi.org/10.29207/resti.v4i2.1845.

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There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

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Mishler, Scott, Cody Jeffcoat, and Jing Chen. "Effects of Anthropomorphic Phishing Detection Aids, Transparency Information, and Feedback on User Trust, Performance, and Aid Retention." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 183. http://dx.doi.org/10.1177/1071181319631351.

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Phishing email attacks are a prevalent threat to internet users. Users often ignore or otherwise disregard automated aids, even when the aids’ reliability is high. The current study sought to fill a gap in the literature by examining the effects of anthropomorphism, feedback, and transparency information on user trust and performance within the domain of phishing email detection. Based upon previous studies in anthropomorphic automated systems, this study incorporated three levels of anthropomorphism (AI, human, text), two levels of aid gender (male, female), transparency information (present, absent), and feedback (present, absent). The 465 participants were recruited online through Amazon Mechanical Turk (MTurk) and performed the study on Qualtrics. Phishing was explained and instructions told the participants to judge whether the following emails are legitimate or phishing in three separate blocks of five emails. The first block was without any automated aid as a baseline of participants’ performance. The second block showed participants their respective aid and had them complete five more emails with the aid. The final block allowed participants to choose if they wanted to keep the aid or classify the emails alone. Afterwards, participants were asked how much they trusted the aid to help detect phishing threats using a trust in automation scale based on Jian, Bisantz, and Drury's (2000) study. Our results revealed improved performance on the phishing detection task for participants with an aid over participants without an aid. In addition, feedback was shown to be helpful for improving judgement accuracy as well as increase trust. Transparency also improved judgement accuracy for the human aid but was less helpful for the AI aid. This study compliments past research indicating improvements in performance with automated aids (Chen et al., 2018; Röttger, Bali, & Manzey, 2009; Wiegmann, Rich, & Zhang, 2001). Performance in blocks 2 and 3 was better than block 1. A significant positive correlation between trust and performance reinforces that high trust in a highly reliable aid begets good performance. Subsequently, if participants did not retain the aid for block 3, their performance was worse than those who retained the aid. Designers of automated aid systems should prioritize users interacting with and using the aid so that performance stays high. Feedback also helped improve judgement accuracy. By allowing participants to understand the reliability of the aid, they could learn to trust it more and rely on the suggestions of the aid. Feedback information should be offered to users if possible because it helps improve trust and performance, which is the goal of any automated aid system. Human aids with transparency information helped improve performance compared to human aids without transparency information. But this effect was not found for AI aids and nearly reversed. Transparency was expected to improve trust and performance (Hoff & Bashir, 2015), but it showed no differences in trust and only improved performance for human aids. This new finding demonstrates that there could be differences in the perception of human and AI aids, although further experiments would need to be conducted to further examine these findings.

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Abdelhamid, Mohamed. "The Role of Health Concerns in Phishing Susceptibility: Survey Design Study." Journal of Medical Internet Research 22, no. 5 (May 4, 2020): e18394. http://dx.doi.org/10.2196/18394.

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Background Phishing is a cybercrime in which the attackers usually impersonate a trusted source. The attackers usually send an email that contains a link that allows them to steal the receiver’s personal information. In the United States, phishing is the number one cybercrime by victim count according to the Federal Bureau of Investigation’s 2019 internet crime report. Several studies investigated ways to increase awareness and improve employees’ resistance to phishing attacks. However, in 2019, successful phishing attacks continued to rise at a high rate Objective The objective of this study was to investigate the influence of personality-based antecedents on phishing susceptibility in a health care context. Methods Survey data were collected from participants through Amazon Mechanical Turk to test a proposed conceptual model using structural equation modeling. Results A total of 200 participants took part. Health concerns, disposition to trust, and risk-taking propensity yielded higher phishing susceptibility. This highlights the important of personality-based factors in phishing attacks. In addition, females had a higher phishing susceptibility than male participants Conclusions While previous studies used health concerns as a motivator for contexts such as sharing personal health records with providers, this study shed light on the danger of higher health concerns in enabling the number one cybercrime.

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Grzebielec, Szymon. "Analysis of the vulnerability of IT system users to a phishing attack." Journal of Computer Sciences Institute 15 (June 27, 2020): 164–67. http://dx.doi.org/10.35784/jcsi.2049.

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This article presents an analysis of users' vulnerability to phishing attacks. The study was carried out using a self-prepared attack. A phishing attack was carried out on a group of 100 people. The subjects were divided into two groups of 50 people. The first group was attacked from a private, trusted account. The second group was attacked from a foreign email address. The attacked people were asked to complete the survey, its results and conclusions are presented in this article.

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Journal articles on the topic 'Email phishing'

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