Daniele Lain

A common form of phishing training in organizations is the use of simulated phishing emails to test employees' susceptibility to phishing attacks, and the immediate delivery of training material to those who fail the test. This widespread practice is dubbed embedded training; however, its effectiveness in decreasing the likelihood of employees falling for phishing again in the future is questioned by the contradictory findings of several recent field studies. We investigate embedded phishing training in three aspects. First, we observe that the practice incorporates different components---knowledge gains from its content, nudges and reminders from the test itself, and the deterrent effect of potential consequences---our goal is to study which ones are more effective, if any. Second, we explore two potential improvements to training, namely its timing and the use of incentives. Third, we analyze employees' reception and perception of the practice. For this, we conducted a large-scale mixed-methods (quantitative and qualitative) study on the employees of a partner company. Our study contributes several novel findings on the training practice: in particular, its effectiveness comes from its nudging effect, i.e., the periodic reminder of the threat rather than from its content, which is rarely consumed by employees due to lack of time and perceived usefulness. Further, delaying training to ease time pressure is as effective as currently established practices, while rewards do not improve secure behavior. Finally, some of our results support previous findings with increased ecological validity, e.g., that phishing is an attention problem, rather than a knowledge one, even for the most susceptible employees, and thus enforcing training does not help.

In this paper, we present findings from a large-scale and long-term phishing experiment that we conducted in collaboration with a partner company. Our experiment ran for 15 months during which time more than 14,000 study participants (employees of the company) received different simulated phishing emails in their normal working context. We also deployed a reporting button to the company's email client which allowed the participants to report suspicious emails they received. We measured click rates for phishing emails, dangerous actions such as submitting credentials, and reported suspicious emails. The results of our experiment provide three types of contributions. First, some of our findings support previous literature with improved ecological validity. One example of such results is good effectiveness of warnings on emails. Second, some of our results contradict prior literature and common industry practices. Surprisingly, we find that embedded training during simulated phishing exercises, as commonly deployed in the industry today, does not make employees more resilient to phishing, but instead it can have unexpected side effects that can make employees even more susceptible to phishing. And third, we report new findings. In particular, we are the first to demonstrate that using the employees as a collective phishing detection mechanism is practical in large organizations. Our results show that such crowd-sourcing allows fast detection of new phishing campaigns, the operational load for the organization is acceptable, and the employees remain active over long periods of time.

Acoustic emanations of computer keyboards represent a serious privacy issue. As demonstrated in prior work, physical properties of keystroke sounds might reveal what a user is typing. However, previous attacks assumed relatively strong adversary models that are not very practical in many real-world settings. Such strong models assume: (i) adversary's physical proximity to the victim, (ii) precise profiling of the victim's typing style and keyboard, and/or (iii) significant amount of victim's typed information (and its corresponding sounds) available to the adversary. This paper presents and explores a new keyboard acoustic eavesdropping attack that involves Voice-over-IP (VoIP), called Skype & Type (S&T), while avoiding prior strong adversary assumptions. This work is motivated by the simple observation that people often engage in secondary activities (including typing) while participating in VoIP calls. As expected, VoIP software acquires and faithfully transmits all sounds, including emanations of pressed keystrokes, which can include passwords and other sensitive information. We show that one very popular VoIP software (Skype) conveys enough audio information to reconstruct the victim's input -- keystrokes typed on the remote keyboard. Our results demonstrate that, given some knowledge on the victim's typing style and keyboard model, the attacker attains top-5 accuracy of 91.7% in guessing a random key pressed by the victim. Furthermore, we demonstrate that S&T is robust to various VoIP issues (e.g., Internet bandwidth fluctuations and presence of voice over keystrokes), thus confirming feasibility of this attack. Finally, it applies to other popular VoIP software, such as Google Hangouts.

The Command and Control (C&C) channel of modern botnets is migrating from traditional centralized solutions (such as the ones based on Internet Relay Chat and Hyper Text Transfer Protocol), towards new decentralized approaches. As an example, in order to conceal their traffic and avoid blacklisting mechanisms, recent C&C channels use peer-to-peer networks or abuse popular Online Social Networks (OSNs). A key reason for this paradigm shift is that current detection systems become quite effective in detecting centralized C&C. In this paper we propose ELISA (Elusive Social Army), a botnet that conceals C&C information using OSNs accounts of unaware users. In particular, ELISA exploits in a opportunistic way the messages that users exchange through the OSN. Furthermore, we provide our prototype implementation of ELISA. We show that several popular social networks can be maliciously exploited to run this type of botnet, and we discuss why current traffic analysis systems cannot detect ELISA. Finally, we run a thorough set of experiments that confirm the feasibility of our proposal. We have no evidence of any real-world botnets that use our technique to create C&C channels. However, we believe that finding out in advance potential new types of botnets will help to prevent possible future malevolent applications.