text: With an increasing number of malicious attacks, the number of people and organizations falling prey to social engineering attacks is proliferating. Despite considerable research in mitigation systems, attackers continually improve their modus operandi by using sophisticated machine learning, natural language processing techniques with an intent to launch successful targeted attacks aimed at deceiving detection mechanisms as well as the victims. We propose a system for advanced email masquerading attacks using Natural Language Generation (NLG) techniques. Using legitimate as well as an influx of varying malicious content, the proposed deep learning system generates emails with malicious content, customized depending on the attacker's intent. The system leverages Recurrent Neural Networks (RNNs) for automated text generation. We also focus on the performance of the generated emails in defeating statistical detectors, and compare and analyze the emails using a proposed baseline.
  @cite_26: Phishing causes billions of dollars in damage every year and poses a serious threat to the Internet economy. Email is still the most commonly used medium to launch phishing attacks [1]. In this paper, we present a comprehensive natural language based scheme to detect phishing emails using features that are invariant and fundamentally characterize phishing. Our scheme utilizes all the information present in an email, namely, the header, the links and the text in the body. Although it is obvious that a phishing email is designed to elicit an action from the intended victim, none of the existing detection schemes use this fact to identify phishing emails. Our detection protocol is designed specifically to distinguish between “actionable” and “informational” emails. To this end, we incorporate natural language techniques in phishing detection. We also utilize contextual information, when available, to detect phishing: we study the problem of phishing detection within the contextual confines of the user’s email box and demonstrate that context plays an important role in detection. To the best of our knowledge, this is the first scheme that utilizes natural language techniques and contextual information to detect phishing. We show that our scheme outperforms existing phishing detection schemes. Finally, our protocol detects phishing at the email level rather than detecting masqueraded websites. This is crucial to prevent the victim from clicking any harmful links in the email. Our implementation called PhishNet-NLP, operates between a user’s mail transfer agent (MTA) and mail user agent (MUA) and processes each arriving email for phishing attacks even before reaching the inbox.
  @cite_3: 
  @cite_6: We focus on email-based attacks, a rich field with well-publicized consequences. We show how current Natural Language Generation (NLG) technology allows an attacker to generate masquerade attacks on scale, and study their effectiveness with a within-subjects study. We also gather insights on what parts of an email do users focus on and how users identify attacks in this realm, by planting signals and also by asking them for their reasoning. We find that: (i) 17 of participants could not identify any of the signals that were inserted in emails, and (ii) Participants were unable to perform better than random guessing on these attacks. The insights gathered and the tools and techniques employed could help defenders in: (i) implementing new, customized anti-phishing solutions for Internet users including training next-generation email filters that go beyond vanilla spam filters and capable of addressing masquerade, (ii) more effectively training and upgrading the skills of email users, and (iii) understanding the dynamics of this novel attack and its ability of tricking humans.
  @cite_24: Phishing is a form of identity theft that occurs when a malicious Web site impersonates a legitimate one in order to acquire sensitive information such as passwords, account details, or credit card numbers.Though there are several anti-phishing software and techniques for detecting potential phishing attempts in emails and detecting phishing contents on websites, phishers come up with new and hybrid techniques to circumvent the available software and techniques.
  @cite_5: 
  @cite_20: In a phishing attack, an unsuspecting victim is lured, typically via an email, to a web site designed to steal sensitive information such as bank credit card account numbers, login information for accounts, etc. Each year Internet users lose billions of dollars to this scourge. In this paper, we present a general semantic feature selection method for text problems based on the statistical t-test and WordNet, and we show its effectiveness on phishing email detection by designing classifiers that combine semantics and statistics in analyzing the text in the email. Our feature selection method is general and useful for other applications involving text-based analysis as well. Our email body-text-only classifier achieves more than 95 accuracy on detecting phishing emails with a false positive rate of 2.24 . Due to its use of semantics, our feature selection method is robust against adaptive attacks and avoids the problem of frequent retraining needed by machine learning classifiers.
  @cite_12: Phishing email has become a popular solution among attackers to steal all kinds of data from people and easily breach organizations' security. Hackers use multiple techniques and tricks to raise the chances of success of their attacks, like using information found on social networking websites to tailor their emails to the target's interests, or targeting employees of an organization who probably can't spot a phishing email or malicious websites and avoid sending emails to IT people or employees from Security department. In this paper we focus on analyzing the coherence of information contained in the different parts of the email: Header, Body, and URLs. After analyzing multiple phishing emails we discovered that there is always incoherence between these different parts. We created a comprehensive method which uses a set of rules that correlates the information collected from analyzing the header, body and URLs of the email and can even include the user in the detection process. We take into account that there is no such thing called perfection, so even if an email is classified as legitimate, our system will still send a warning to the user if the email is suspicious enough. This way even if a phishing email manages to escape our system, the user can still be protected.
summary: In this paper, we focus primarily on generation of fake emails specifically engineered for phishing and scamming victims. Additionally, we also look at some state-of-the-art phishing email detection systems. Researchers in @cite_24 extract a large number of text body, URL and HTML features from emails, which are then fed into supervised (SVMs, Neural Networks) as well as unsupervised (K-Means clustering) algorithms for the final verdict on the email nature. The system proposed in @cite_5 extracts 25 stylistic and structural features from emails, which are given to a supervised SVM for analysis of email nature. Newer techniques for phishing email detection based on textual content analysis have been proposed in @cite_26 @cite_20 @cite_12 @cite_3 . Masquerade attacks are generated by the system proposed in @cite_6 , which tunes the generated emails based on legitimate content and style of a famous personality. Moreover, this technique can be exploited by phishers for launching email masquerade attacks, therefore making such a system extremely dangerous.