1. Challenges
1.1. Intelligent-based phisher
1.1.1. Typosquatting
1.1.2. Attacking anti-phishing algorithms
1.1.3. Shorten lifespan of phishing websites
1.2. Accuracy Analysis
1.2.1. False Positivity & Missed Detection
1.2.2. Zero-Hour Detection
1.2.3. Language Dependent in Machine Learning Models
2. Consequences of Phishing
2.1. Identity Fraud
2.1.1. Personal Information Exposure
2.1.2. Contagious scams through Social Networks
2.2. Financial Loss
3. Practices
3.1. Feature-based methodology
3.1.1. Content-based
3.1.1.1. CANTINA
3.1.1.2. Random Forest Modeling
3.1.1.3. Support Vector Machine(SVM)
3.1.2. Lexical-based
3.1.2.1. Natural Language Processing & Word Vector
3.2. Neuron-Network-based methodology
3.2.1. Convolutional Neuron Network
3.2.2. Recurrent Neuron Network
3.3. Traditional Methodology
3.3.1. Blacklist/Whitelist database
3.3.2. Heuristic-based detection
4. Medium
4.1. Web
4.1.1. Webpage
4.1.2. Uniform Resource Locator(URL)
4.1.3. Email
4.2. Social Network
4.2.1. Social Media
4.2.1.1. Twitter
4.2.2. Instant Messager
4.2.2.1. Facebook Messenger