Visible to the public Detecting Phishing Attacks Using Natural Language Processing and Machine Learning

TitleDetecting Phishing Attacks Using Natural Language Processing and Machine Learning
Publication TypeConference Paper
Year of Publication2018
AuthorsPeng, Tianrui, Harris, Ian, Sawa, Yuki
Conference Name2018 IEEE 12th International Conference on Semantic Computing (ICSC)
Keywordsblacklisting, Computer crime, Electronic mail, Human Behavior, inappropriate statements detection, learning (artificial intelligence), machine learning, malicious intent detection, natural language processing, natural language text, phishing, phishing attack detection, phishing emails, pubcrawl, Resiliency, Scalability, security threats, Semantics, Social Engineering, text analysis, Uniform resource locators, unsolicited e-mail
AbstractPhishing attacks are one of the most common and least defended security threats today. We present an approach which uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks. Our approach is novel compared to previous work because it focuses on the natural language text contained in the attack, performing semantic analysis of the text to detect malicious intent. To demonstrate the effectiveness of our approach, we have evaluated it using a large benchmark set of phishing emails.
Citation Keypeng_detecting_2018