MailTrout:a machine learning browser extension for detecting phishing emails

The onset of the COVID-19 pandemic has given rise to an increase in cyberattacks and cybercrime, particularly with respect to phishing attempts. Cybercrime associated with phishing emails can significantly impact victims, who may be subjected to monetary loss and identity theft. Existing anti-phishing tools do not always catch all phishing emails, leaving the user to decide the legitimacy of an email. The ability of machine learning technology to identify reoccurring patterns yet cope with overall changes complements the nature of anti-phishing techniques, as phishing attacks may vary in wording but often follow similar patterns. This paper presents a browser extension called MailTrout, which incorporates machine learning within a usable security tool to assist users in detecting phishing emails. MailTrout demonstrated high levels of accuracy when detecting phishing emails and high levels of usability for end-users

Digital fingerprinting for identifying malicious collusive groups on Twitter

Propagation of malicious code on online social networks (OSN) is often a coordinated effort by collusive groups of malicious actors hiding behind multiple online identities (or digital personas). Increased interaction in OSN have made them reliable for the efficient orchestration of cyber-attacks such as phishing click bait and drive-by downloads. URL shortening enables obfuscation of such links to malicious websites and massive interaction with such embedded malicious links in OSN guarantees maximum reach. These malicious links lure users to malicious endpoints where attackers can exploit system vulnerabilities. Identifying the organised groups colluding to spread malware is non-trivial owing to the fluidity and anonymity of criminal digital personas on OSN. This paper proposes a methodology for identifying such organised groups of criminal actors working together to spread malicious links on OSN. Our approach focuses on understanding malicious users as ‘digital criminal personas’ and characteristics of their online existence. We first identify those users engaged in propagating malicious links on OSN platforms, and further develop a methodology to create a digital fingerprint for each malicious OSN account/digital persona. We create similarity clusters of malicious actors based on these unique digital fingerprints to establish ‘collusive’ behaviour. We evaluate the ability of a cluster-based approach on OSN digital fingerprinting to identify collusive behaviour in OSN by estimating within-cluster similarity measures and testing it on a ground truth dataset of five known colluding groups on Twitter. Our results show that our digital fingerprints can identify 90% of cyber-personas engaged in collusive behaviour 75% of collusion in a given sample set