Social engineering and the dangers of phishing

Social Engineering and phishing technique are subjects that have been evolving as the years pass, mainly through email, which is one of the most used communication tools in the world. Phishing emails are usually related to Social Engineering and may be proposed through links and / or attachments in this type of email, both of which are malicious propagation, and may be hacked into personal / confidential information or even complete control of the computer / email without the users noticing. Several studies have already been carried out showing that there have been more and more attacks of this type and increasingly impacting the population. The research described in this article aims to review prevention methods for this type of computer crime. The research included an exploratory study with a qualitative methodology, through interviews with professionals in the area of Computer Security and later a study with a quantitative methodology, through an online questionnaire.info:eu-repo/semantics/acceptedVersio

Detecting semantic social engineering attacks with the weakest link: Implementation and empirical evaluation of a human-as-a-security-sensor framework

The notion that the human user is the weakest link in information security has been strongly, and, we argue, rightly contested in recent years. Here, we take a step further showing that the human user can in fact be the strongest link for detecting attacks that involve deception, such as application masquerading, spearphishing, WiFi evil twin and other types of semantic social engineering. Towards this direction, we have developed a human-as-a-security-sensor framework and a practical implementation in the form of Cogni-Sense, a Microsoft Windows prototype application, designed to allow and encourage users to actively detect and report semantic social engineering attacks against them. Experimental evaluation with 26 users of different profiles running Cogni-Sense on their personal computers for a period of 45 days has shown that human sensors can consistently outperform technical security systems. Making use of a machine learning based approach, we also show that the reliability of each report, and consequently the performance of each human sensor, can be predicted in a meaningful and practical manner. In an organisation that employs a human-as-a-security-sensor implementation, such as Cogni-Sense, an attack is considered to have been detected if at least one user has reported it. In our evaluation, a small organisation consisting only of the 26 participants of the experiment would have exhibited a missed detection rate below 10%, down from 81% if only technical security systems had been used. The results strongly point towards the need to actively involve the user not only in prevention through cyber hygiene and user-centric security design, but also in active cyber threat detection and reporting