[2] Understanding Spam and Phishing in Networked Communications

Spam

Unwanted messages sent via email or social media.

Anti-Spam Industry

Multi-billion dollar sector combating spam messages.

Spam Detection Challenge

Balancing precision and recall in identifying spam.

Precision

Avoiding false positives in spam detection.

Bayesian Filtering

Analyzes words to classify messages as spam.

Random Forests

Machine learning model for spam detection.

Support Vector Machines (SVM)

Algorithm used to classify spam messages.

Neural Networks

Advanced model for processing spam data.

Canadian Anti-Spam Legislation (CASL)

Regulates commercial electronic messages in Canada.

Commercial Electronic Message (CEM)

Message encouraging participation in commercial activities.

Express Consent

Explicit permission required to send CEMs.

Implied Consent

Assumed permission based on prior interactions.

Phishing

Cybercrime to acquire private information via deception.

Vishing

Voice phishing conducted through phone calls.

Smishing

Phishing via deceptive text messages.

Phishing Email Signs

Unidentified sender, urgency, suspicious links.

Identity Theft

Stealing personal information for fraudulent use.

Telegraph

Early communication technology using electrical signals.

Mass Communication

Dissemination of information to large audiences.

Big Data

Large datasets analyzed for insights and trends.

Surveillance Risks

Potential dangers associated with monitoring technologies.

Spam-based Advertising

Profitable business model leveraging spam messages.

Spam Detection Models

Algorithms processing metadata, content, and context.