Email Summarization to Assist Users in Phishing Identification

• URL: http://arxiv.org/abs/2203.13380v1
• Date: Thu, 24 Mar 2022 23:03:46 GMT
• Title: Email Summarization to Assist Users in Phishing Identification
• Authors: Amir Kashapov, Tingmin Wu, Alsharif Abuadbba, Carsten Rudolph
• Abstract summary: Cyber-phishing attacks are more precise, targeted, and tailored by training data to activate only in the presence of specific information or cues. This work leverages transformer-based machine learning to analyze prospective psychological triggers. We then amalgamate this information and present it to the user to allow them to (i) easily decide whether the email is "phishy" and (ii) self-learn advanced malicious patterns.
• Score: 1.433758865948252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cyber-phishing attacks recently became more precise, targeted, and tailored by training data to activate only in the presence of specific information or cues. They are adaptable to a much greater extent than traditional phishing detection. Hence, automated detection systems cannot always be 100% accurate, increasing the uncertainty around expected behavior when faced with a potential phishing email. On the other hand, human-centric defence approaches focus extensively on user training but face the difficulty of keeping users up to date with continuously emerging patterns. Therefore, advances in analyzing the content of an email in novel ways along with summarizing the most pertinent content to the recipients of emails is a prospective gateway to furthering how to combat these threats. Addressing this gap, this work leverages transformer-based machine learning to (i) analyze prospective psychological triggers, to (ii) detect possible malicious intent, and (iii) create representative summaries of emails. We then amalgamate this information and present it to the user to allow them to (i) easily decide whether the email is "phishy" and (ii) self-learn advanced malicious patterns.

Related papers

• Eyes on the Phish(er): Towards Understanding Users' Email Processing Pattern and Mental Models in Phishing Detection [0.4543820534430522]
  This study examines how workload affects susceptibility to phishing. We use eye-tracking technology to observe participants' reading patterns and interactions with phishing emails. Our results provide concrete evidence that attention to the email sender can reduce phishing susceptibility.
  arXiv  Detail & Related papers  (2024-09-12T02:57:49Z)
• Phishing Email Detection Using Inputs From Artificial Intelligence [5.172061216433]
  We present a dataset with annotated labels where these labels are created from the classes of signals that users are typically asked to identify in such training. With a comparative analysis of performance between human annotators and the models on these labels, we provide insights which can contribute to the improvement of the curricula for both machine and human training.
  arXiv  Detail & Related papers  (2024-05-21T04:37:23Z)
• Analysis and prevention of AI-based phishing email attacks [0.0]
  generative AI can be used to send each potential victim a different email. We use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The paper also describes the corpus of AI-generated phishing emails that is made open to the public.
  arXiv  Detail & Related papers  (2024-05-08T21:40:49Z)
• ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection [2.3999111269325266]
  This study introduces ChatSpamDetector, a system that uses large language models (LLMs) to detect phishing emails. By converting email data into a prompt suitable for LLM analysis, the system provides a highly accurate determination of whether an email is phishing or not. We conducted an evaluation using a comprehensive phishing email dataset and compared our system to several LLMs and baseline systems.
  arXiv  Detail & Related papers  (2024-02-28T06:28:15Z)
• Prompted Contextual Vectors for Spear-Phishing Detection [45.07804966535239]
  Spear-phishing attacks present a significant security challenge. We propose a detection approach based on a novel document vectorization method. Our method achieves a 91% F1 score in identifying LLM-generated spear-phishing emails.
  arXiv  Detail & Related papers  (2024-02-13T09:12:55Z)
• Untargeted Backdoor Attack against Object Detection [69.63097724439886]
  We design a poison-only backdoor attack in an untargeted manner, based on task characteristics. We show that, once the backdoor is embedded into the target model by our attack, it can trick the model to lose detection of any object stamped with our trigger patterns.
  arXiv  Detail & Related papers  (2022-11-02T17:05:45Z)
• Profiler: Profile-Based Model to Detect Phishing Emails [15.109679047753355]
  We propose a multidimensional risk assessment of emails to reduce the feasibility of an attacker adapting their email and avoiding detection. We develop a risk assessment framework that includes three models which analyse an email's (1) threat level, (2) cognitive manipulation, and (3) email type. Our Profiler can be used in conjunction with ML approaches, to reduce their misclassifications or as a labeller for large email data sets in the training stage.
  arXiv  Detail & Related papers  (2022-08-18T10:01:55Z)
• Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text [219.15486286590016]
  This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.
  arXiv  Detail & Related papers  (2021-10-10T17:19:37Z)
• Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes [81.85509264573948]
  In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier. In an information embedding attack, an attacker is the provider of a malicious third-party machine learning tool. In this work, we aim to design information embedding attacks that are verifiable and robust against popular post-processing methods.
  arXiv  Detail & Related papers  (2020-10-26T17:42:42Z)
• Backdoor Attack against Speaker Verification [86.43395230456339]
  We show that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data. We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification.
  arXiv  Detail & Related papers  (2020-10-22T11:10:08Z)
• Learning with Weak Supervision for Email Intent Detection [56.71599262462638]
  We propose to leverage user actions as a source of weak supervision to detect intents in emails. We develop an end-to-end robust deep neural network model for email intent identification.
  arXiv  Detail & Related papers  (2020-05-26T23:41:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.