Analysis of Communication Pattern with Scammers in Enron Corpus
This paper is an exploratory analysis into fraud detection taking Enron email corpus as the case study. The paper posits conclusions like strict servitude and unquestionable faith among employees as breeding grounds for sham among higher executives. We also try to infer on the nature of communication between fraudulent employees and between non- fraudulent-fraudulent employees
Code (0)
등록된 구현이 없습니다.
Tasks
Fraud DetectionSimilar Papers 제목 키워드 기반
Spam Detection Using BERT
Emails and SMSs are the most popular tools in today communications, and as the increase of emails and SMSs users are increase, the number of spams is also increases. Spam is any kind of unwanted, unsolicited digital comm…
Spam detectionA pipeline and comparative study of 12 machine learning models for text classification
Text-based communication is highly favoured as a communication method, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into relaying…
BIG-bench Machine LearningClassificationtext-classificationText ClassificationWork Hard, Play Hard: Email Classification on the Avocado and Enron Corpora
In this paper, we present an empirical study of email classification into two main categories {``}Business{''} and {``}Personal{''}. We train on the Enron email corpus, and test on the Enron and Avocado email corpora. We…
ClassificationGeneral ClassificationScamming the Scammers: Using ChatGPT to Reply Mails for Wasting Time and Resources
The use of Artificial Intelligence (AI) to support cybersecurity operations is now a consolidated practice, e.g., to detect malicious code or configure traffic filtering policies. The recent surge of AI, generative techn…
CEREC: A Corpus for Entity Resolution in Email Conversations
We present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,448 email messages and 60,383 entity coref…
coreference-resolutionCoreference ResolutionEntity Resolution