Life-long Learner - Personal Notes About Everything.

Study Makes Me Happy!

This is my personal study notes, may be computer science, mathematics, and even diary, everything. I hope to motivate myself through this project and its about passionate living and lifelong learning.

Items marked with (-) are still works in progress.

Items marked with (el) means external links.

Machine Learning Foundations (NTU)

1. Lecture 1: The Learning Problem
2. Lecture 2: Learning to Answer Yes/No
3. Lecture 3: Types of Learning
4. Lecture 4: Feasibility of Learning
5. Homework 1

Web Application Security

1. XSS for beginners

Programming

1. (-) Python Challenge Writeup

SEED Labs

1. Set-UID Program Vulnerability Lab
2. Format-String Vulnerability Lab
3. Shellshock Attack Lab
4. Buffer Overflow Vulnerability Lab

Reverse Engineering for Beginners

0. Auto script for learner
1. Noise in stack

Misc

1. (-) Infosec Favorites
2. LaTex in GitHub

Use Linux

1. Arch Linux/manjaro

Linux Kernel Development 3rd

1. Chapter 1: Introduction to the Linux Kernel
2. Chapter 2: Getting Started with the Kernel
3. Chapter 3: Process Management
4. Chapter 4: Process Scheduling
5. Chapter 5: System Calls
6. Chapter 6: Kernel Data Structures
7. Chapter 7: Interrupts and Interrupt Handlers
8. Chapter 8: Bottom Halves and Deferring Work
9. Chapter 9: An Introduction to Kernel Synchronization
10. Chapter 10: Kernel Synchronization Methods
11. Chapter 11: Timers and Time Management
12. Chapter 12: Memory Management
13. Chapter 13: The Virtual Filesystem
14. Chapter 14: The Block I/O Layer
15. Chapter 15: The Process Address Space
16. Chapter 16: The Page Cache and Page Writeback
17. (-) Chapter 17: Devices and Modules

Paper Review

0. (el) How to Read an Engineering Research Paper
1. All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask)
2. MAIL: Malware Analysis Intermediate Language: a step towards automating and optimizing malware detection
3. DroidAnalytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware
4. DroidNative: Semantic-Based Detection of Android Native Code Malware
5. MaMaDroid: Detecting Android malware by building Markov chains of behavorial models
6. Android Malware Classification by Applying Online Machine Learning
7. A Static Android Malware Detection Based on Actual Used Permissions Combination and API Calls
8. DynaLog: An automated dynamic analysis framework for characterizing Android applications
9. EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning

Data Communications and Networking 5th

1. Chapter 1: Introduction
2. Chapter 2: Network Models
3. Chapter 3: Introduction to Physical Layer
4. Chapter 4: Digital Transmission
5. Chapter 5: Analog Transmission
6. Chapter 6: Bandwidth Utilization: Multiplexing and Spectrum Spreading
7. Chapter 7: Transmission Media
8. Chapter 8: Switching
9. Chapter 9: Introduction to Data-Link Layer
10. Chapter 10: Error Detection and Correction
11. Chapter 11: Data Link Control (DLC)
12. Chapter 12: Media Access Control (MAC)
13. Chapter 13: Wired LANs: Ethernet
14. Chapter 14: Other Wired Network
15. Chapter 15: Wireless LANs
16. (-) Chapter 16: Other Wireless Network

Diary

1. 2016.1.18

LICENSE

MIT LICENSE.
COPYRIGHT (C) 2017 firmy

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Started on 2017/04/27 by Firmy Yang