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Instructor:
Class Schedule: Tuesday and Friday 9:50-11:30am, Ryder Hall 155. Office Hours: Tuesday, 11:30am-12:30pm, WVH 348 Class description: “Big-data”
analytics has enabled a number of compute-intensive applications (such as
machine translation, speech recognition and precision medicine) with large
positive impact to our daily lives. Not surprisingly, “security
analytics”, the application of machine learning and data mining in the
field of cyber security, is effective as well in learning and predicting
attacker behavior, detecting malicious infrastructures and designing more
effective defensive techniques. This class will cover various practical
applications of machine learning techniques in network security, web
security, malware detection and usable authentication. Compared to other areas
benefiting from machine learning, security applications exhibit additional
challenges due to limited availability of attack datasets, difficulty of
validating new findings, high cost of false positives, and the risk of
potential adversarial tampering with the datasets and models. The course will
also discuss directions for addressing these challenges and include advanced
topics in the areas of adversarial machine learning and privacy-preserving
analytics. We will be reading and
discussing recent research papers from security and machine learning
conferences. A major component of the class is a research project conducted
in a small team of 1-2 students. A detailed project report suitable for a
workshop submission is expected at end of class. Pre-requisites: · Fundamental Networking · Introductory security preferable · Basic data mining preferable Grading
The
grade will be based on: - Class participation – 20% ·
Participation
in discussing the papers in class ·
Leading
the discussion for several papers - Paper summaries - 20% · Submit paper summaries before class ·
Detailed
comments on weaknesses, strengths and contributions - Research project - 60% ·
10
% project proposal - Due 10/04 ·
30%
final project report ·
20%
presentation in class Paper summaries
Reading
will be assigned for each lecture. The day before lecture (at midnight),
every student must submit a report for each assigned paper. The report should
contain a one-paragraph summary of the paper, description of three strong
points of the paper and three weak points of the paper, discussion on data
collection and machine learning methodology. Instructor will provide the
template for paper summaries. Please send the reports in Piazza. Project
-
Problem
addressed by the project -
Proposed
approach -
Milestones
(main steps and timeline) -
References:
additional literature survey that you intend to do -
Tools:
software, packages -
Data
sources: publicly available datasets for your research -
Deliverable
items: implementation, simulation results, graphs, visualizations, etc.
-
Motivation
of addressed problem -
Description
of public dataset used -
Proposed
solution/algorithm including technical details -
Comparison
with related work -
Experimental
results
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Anomaly Detection: A Survey. V. Chandola, A. Banerjee,
and V. Kumar Books: [ISL] An introduction to statistical
learning with applications in R. G. James, D. Witten, T. Hastie and R.
Tibshirani |
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[ESL]
The
elements of statistical learning. Data mining, Inference, and Prediction.
T. Hastie, R. Tibshirani and J. Friedman |
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