CS 259D Data Mining for Cyber Security Notes Introduction

The notes are the supplement to papers and handouts of CS 259D. Unless otherwise stated, all images and tables are cited from the original papers and slides.

English: GitHub GitBook 中文：Github GitBook

Lectures

Introduction

  • Overview of information security, current security landscape, the case for security data mining

Botnets

  • Botnet topologies, botnet detection using NetFlow analysis
    • Lecture 2
    • BotMiner: Clustering Analysis of Network Traffic for Protocol- and Structure- Independent Botnet Detection
    • BotFinder: Finding Bots in Network Traffic Without Deep Packet Inspection
  • Botnet detection using DNS analysis
    • Lecture 3
    • EXPOSURE: Finding Malicious Domains Using Passive DNS Analysis (2011)

Insider Threats

  • Introduction to insider threats, masquerader detection strategies
    • Lecture 3
    • One-class Training for Masquerade Detection (2003)

Behavioral Biometrics

  • Active authentication using behavioral and cognitive biometrics
    • Lecture 4
    • An examination of user behavior for re-authentication (M. Pusara's PhD thesis,2007)
  • Mouse dynamics analysis for active authentication
    • Lecture 5
    • Continuous authentication for mouse dynamics: A pattern-growth approach (Shen C, Cai Z, Guan X. 2012)
    • Lecture 7
    • An Efficient User Verification System via Mouse Movements, 2011
  • Touch and swipe pattern analysis for mobile active authentication
    • Lecture 7
    • Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication, 2013

Web Security

  • Web threat detection via web server log analysis
    • Lecture 8
    • A multi-model approach to the detection of web-based attacks, 2005
  • Alert aggregation for web security
    • Lecture 12
    • Using Generalization and Characterization Techniques in the Anomaly-based Detection of Web Attacks, Robertson et al., 2006

Phishing Detection

  • Phishing email detection, phishing website detection
    • Lecture 16
    • Learning to Detect Phishing Emails, Fette et al, 2007
    • Cantina: A content-based approach to detecting phishing websites, Zhang et al, 2007

Intrusion Detection Systems (IDSs)

  • Overview of multi-classifier systems (MCS), advantages of MCS in security analytics
    • Lecture 10
    • Adaptive Intrusion Detection System via Online Learning, 2012
  • Building attack scenarios from individual alerts correlation
    • Lecture 20
    • A Comprehensive Approach to Intrusion Detection Alert Correlation, Valeur et al, 2004

Deep Packet Inspection

  • Packet payload modeling for network intrusion detection
    • Lecture 12
    • PAYL: Anomalous payload-based network intrusion detection, Wang-Stolfo 2004
  • One-class multi-classifier systems, one-class MCS for packet payload modeling and network intrusion detection
    • Lecture 15
    • McPAD : A Multiple Classifier System for Accurate Payload-based Anomaly Detection, Perdisci et al, 2009

Polymorphism

  • Polymorphic blending attacks, infeasibility of modeling polymorphic attacks
    • Lecture 14
    • Polymorphic Blending Attacks, Fogla et al, 2006
    • On the Infeasibility of Modeling Polymorphic Shellcode, Song et al, 2007

Machine Learning for Security

  • Challenges in applying machine learning (ML) to security, guidelines for applying ML to security
    • Lecture 13
    • Outside the closed world: On using machine learning for network intrusion detection, Sommer-Paxson, 2010
    • Challenging the Anomaly Detection Paradigm: A Provocative Discussion, Gates-Taylor, 2007
    • The Base-Rate Fallacy and Its Implications for the Difficulty of Intrusion Detection, Axelsson, 1999

Adversarial Machine-Learning

  • Security of machine learning
    • Lecture 10
    • The security of machine learning, 2010

Security at Industry

  • Security at Wells Fargo
    • Lecture 6
    • Guest speaker Avi Avivi, VP Enterprise Information Security Architecture at Wells Fargo
  • Security at Union Bank
    • Lecture 9
    • Guest speaker Gary Lorenz, Chief Information Security Officer (CISO) and Managing Director at MUFG Union Bank
  • Security Data Mining at Google
    • Lecture 11
    • Guest speaker Massimiliano Poletto, head of Google Security Monitoring Tools group
  • Industry Perspectives
    • Q&A with guest speaker Michael Fey, EVP and CTO of Intel Security Group (aka McAfee)

Homework

Homework1

  • Question 1. Botnets
  • Question 2. Anomaly Detection via "Eigenface" of Command History
  • Question 3. Continuous Authentication via Biometric Behavior

Homework2

  • Question 1. Quizzes
  • Question 2. Touch Biometrics
  • Question 3. Merits of Entropy in Attack Detection/Diagnostics

Homework3

  • Analyse e-mail packets

Homework4

  • Quizzes

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