2,418 research outputs found
Assessing the impact of affective feedback on end-user security awareness
A lack of awareness regarding online security behaviour can leave users and their devices vulnerable to compromise. This paper highlights potential areas where users may fall victim to online attacks, and reviews existing tools developed to raise users’ awareness of security behaviour. An ongoing research project is described, which provides a combined monitoring solution and affective feedback system, designed to provide affective feedback on automatic detection of risky security behaviour within a web browser. Results gained from the research conclude an affective feedback mechanism in a browser-based environment, can promote general awareness of online security
Reducing risky security behaviours:utilising affective feedback to educate users
Despite the number of tools created to help end-users reduce risky security behaviours, users are still falling victim to online attacks. This paper proposes a browser extension utilising affective feedback to provide warnings on detection of risky behaviour. The paper provides an overview of behaviour considered to be risky, explaining potential threats users may face online. Existing tools developed to reduce risky security behaviours in end-users have been compared, discussing the success rate of various methodologies. Ongoing research is described which attempts to educate users regarding the risks and consequences of poor security behaviour by providing the appropriate feedback on the automatic recognition of risky behaviour. The paper concludes that a solution utilising a browser extension is a suitable method of monitoring potentially risky security behaviour. Ultimately, future work seeks to implement an affective feedback mechanism within the browser extension with the aim of improving security awareness
Security awareness and affective feedback:categorical behaviour vs. reported behaviour
A lack of awareness surrounding secure online behaviour can lead to end-users, and their personal details becoming vulnerable to compromise. This paper describes an ongoing research project in the field of usable security, examining the relationship between end-user-security behaviour, and the use of affective feedback to educate end-users. Part of the aforementioned research project considers the link between categorical information users reveal about themselves online, and the information users believe, or report that they have revealed online. The experimental results confirm a disparity between information revealed, and what users think they have revealed, highlighting a deficit in security awareness. Results gained in relation to the affective feedback delivered are mixed, indicating limited short-term impact. Future work seeks to perform a long-term study, with the view that positive behavioural changes may be reflected in the results as end-users become more knowledgeable about security awareness
Emerging Phishing Trends and Effectiveness of the Anti-Phishing Landing Page
Each month, more attacks are launched with the aim of making web users
believe that they are communicating with a trusted entity which compels them to
share their personal, financial information. Phishing costs Internet users
billions of dollars every year. Researchers at Carnegie Mellon University (CMU)
created an anti-phishing landing page supported by Anti-Phishing Working Group
(APWG) with the aim to train users on how to prevent themselves from phishing
attacks. It is used by financial institutions, phish site take down vendors,
government organizations, and online merchants. When a potential victim clicks
on a phishing link that has been taken down, he / she is redirected to the
landing page. In this paper, we present the comparative analysis on two
datasets that we obtained from APWG's landing page log files; one, from
September 7, 2008 - November 11, 2009, and other from January 1, 2014 - April
30, 2014. We found that the landing page has been successful in training users
against phishing. Forty six percent users clicked lesser number of phishing
URLs from January 2014 to April 2014 which shows that training from the landing
page helped users not to fall for phishing attacks. Our analysis shows that
phishers have started to modify their techniques by creating more legitimate
looking URLs and buying large number of domains to increase their activity. We
observed that phishers are exploiting ICANN accredited registrars to launch
their attacks even after strict surveillance. We saw that phishers are trying
to exploit free subdomain registration services to carry out attacks. In this
paper, we also compared the phishing e-mails used by phishers to lure victims
in 2008 and 2014. We found that the phishing e-mails have changed considerably
over time. Phishers have adopted new techniques like sending promotional
e-mails and emotionally targeting users in clicking phishing URLs
Analyzing Social and Stylometric Features to Identify Spear phishing Emails
Spear phishing is a complex targeted attack in which, an attacker harvests
information about the victim prior to the attack. This information is then used
to create sophisticated, genuine-looking attack vectors, drawing the victim to
compromise confidential information. What makes spear phishing different, and
more powerful than normal phishing, is this contextual information about the
victim. Online social media services can be one such source for gathering vital
information about an individual. In this paper, we characterize and examine a
true positive dataset of spear phishing, spam, and normal phishing emails from
Symantec's enterprise email scanning service. We then present a model to detect
spear phishing emails sent to employees of 14 international organizations, by
using social features extracted from LinkedIn. Our dataset consists of 4,742
targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack
emails sent to 5,912 non victims; and publicly available information from their
LinkedIn profiles. We applied various machine learning algorithms to this
labeled data, and achieved an overall maximum accuracy of 97.76% in identifying
spear phishing emails. We used a combination of social features from LinkedIn
profiles, and stylometric features extracted from email subjects, bodies, and
attachments. However, we achieved a slightly better accuracy of 98.28% without
the social features. Our analysis revealed that social features extracted from
LinkedIn do not help in identifying spear phishing emails. To the best of our
knowledge, this is one of the first attempts to make use of a combination of
stylometric features extracted from emails, and social features extracted from
an online social network to detect targeted spear phishing emails.Comment: Detection of spear phishing using social media feature
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