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With the release of China's "Data Security Protection Law" in 2021, it means that data security is expected to form a new outlet in China.
The author is fortunate to join one of China's leading data security startups in 2021, engaged in cutting-edge research and implementation of data science combined with data security. In the process of exploration, I found that there are not many materials on the Internet specifically for data security, so I came up with the idea of arranging relevant materials and thinking, hoping to do my best to promote the development of the community.
Refuse to prostitute, welcome star!！
A person can go fast, only a group of people can go farther. The author has set up a big data security technology exchange group, with friends all over Silicon Valley, Singapore, Tencent, Ali, Zhejiang University, etc. Like-minded friends are welcome to contact me to join!
Last updated date is:2022/11
Using AI for Application Security Protection
- isc2022
- https://mp.weixin.qq.com/s/Ce8iXvAuNf2n3OFZSmFi1Q
- https://zhuanlan.zhihu.com/p/466955597?
- https://zhuanlan.zhihu.com/p/511095084
- https://mp.weixin.qq.com/s/Sme4gLnEHyxyhRSN2RUqCA
- https://www.zhihu.com/column/c_1471819989803700224
- Why Machine Learning Always Fails to Solve Cybersecurity Problems: Talk About Feature Space
- Why Machine Learning Always Fails to Solve Cybersecurity Problems: Fragile Systems Engineering
- Why Machine Learning Always Fails to Solve Cybersecurity Problems: Unreasonable Evaluation Metrics
- Why Machine Learning Always Fails to Solve Cybersecurity Problems: Machine Learning Is Not a Panacea
- Explainable Machine Learning for Solving Cybersecurity Problems
- https://mp.weixin.qq.com/s/-9xkAROp7_A6gDjTLxfUsg
- https://www.freebuf.com/articles/web/189981.html
- https://search.freebuf.com/search/?search=%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0#article
- Exploration of sensitive information leakage governance based on machine learning
- https://mp.weixin.qq.com/s/xGY1PxoH9Tlio2mWH7QLjw
- Du Yuejin: Basic Ideas of Data Security Governance
- Data Security Composite Governance and Practice White Paper
- Parameter tampering and traffic replay
- Common API Attacks
- "Unveiling Fake Accounts at the Time of Registration: An Unsupervised Approach"
- "DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks"
- Machine Learning for Malicious Traffic Detection Feature Engineering
- Machine Learning KNN Detects Malicious Traffic
- Webshell detection method combining reinforcement learning and CNN
- Security Risks of Automated Machine Learning
- Using open source intelligence to detect and explain malicious behavior
- BadNL: Semantic Preserving Improved NLP Model Backdoor Attack
- APTMalInsight: Identifying and Recognizing APT Malware Based on System Call Information and Ontology Knowledge Framework
- 《A Practical Approach to Constructing a Knowledge Graph for Cybersecurity》
- 《Developing an Ontology for Cyber Security Knowledge Graphs》
- 《Towards a Relation Extraction Framework for Cyber-Security Concepts》
- https://zhuanlan.zhihu.com/p/406415230
- https://zhuanlan.zhihu.com/p/69159780
- https://zhuanlan.zhihu.com/p/74274673
- https://zhuanlan.zhihu.com/p/75123819
- 《AI2: Training a big data machine to defend》
- 《Big Data Security Challenges: An Overview and Application of User Behavior Analytics》
- 《Adaptive Intrusion Detection System via Online Learning》
- 《A multi-model approach to the detection of web-based attacks》
- 《McPAD : A Multiple Classifier System for Accurate Payload-based Anomaly Detection》
- 《Using Generalization and Characterization Techniques in the Anomaly-based Detection of Web Attacks》
- 《Anomaly-Based Web Attack Detection: A Deep Learning Approach》
- 《A Big Data Analysis Framework for Model-Based Web User Behavior Analytics》
- 《Anomalous Payload-based Network Intrusion Detection》
- 《Data mining for security at Google》
- 《User and Entity Behavior Analytics for Enterprise Security》
- 《A Comprehensive Approach to Intrusion Detection Alert Correlation》
- 《Trafc Anomaly Detection Using K-Means Clustering》
- 《Calculation of the Behavior Utility of a Network System: Conception and Principle》
- 《Spectrogram: A Mixture-of-Markov-Chains Model for Anomaly Detection in Web Traffic》
- 《用户画像相关技术》
- Web attack classification and detection model based on machine learning
- https://blog.cloudflare.com/api-abuse-detection/
- Using Machine Learning to Detect Malicious HTTP Outbound Traffic
- ExecScent: Mining for New C&C Domains in Live Networks with Adaptive Control Protocol Templates
- MADE: Security Analytics for Enterprise Threat Detection
- Machine Learning Practices in Internet Giants
- Application of Machine Learning in Intrusion Detection - Training Intrusion Detection Discriminant Model Based on ADFA-LD Training Set
- datacon competition direction three - attack source and attacker analysis writeup
- [Machine learning-based malware encryption traffic detection research sharing](https://blog.riskivy.com/%e5%9f%ba%e4%ba%8e%e6%9c%ba%e5%99%a8% e5%ad%a6%e4%b9%a0%e7%9a%84%e6%81%b6%e6%84%8f%e8%bd%af%e4%bb%b6%e5%8a%a0%e5% af%86%e6%b5%81%e9%87%8f%e6%a3%80%e6%b5%8b/?from=groupmessage&isappinstalled=0)
- anomaly-detection-through-reinforcement-learning
- URLNet: Learning URL Representations via Deep Learning for Malicious URL Detection
- My AI Security Detection Study Notes (1)
- "Compromised or Attacker-Owned: A Large Scale Classification and Study of Hosting Domains of Malicious URLs"
- Predicting DDoS attacks based on KDDCUP 99 dataset
- Research on DDoS Attack Detection Technology Based on Spectral Analysis and Statistical Machine Learning
- Research on Distributed Denial of Service Attack Detection Method Based on Machine Learning
- DDoS Attacks Using Hidden Markov Models and Cooperative ReinforcementLearning*
- [Win the 0-Day Racing Game Against Botnet on Cloud](https://i.blackhat.com/asia-20/Friday/asia-20-Xu-Win-The-0-Day-Racing-Game-Against -Botnet-In-Public-Cloud.pdf)
- datacon 2020 Botnet Detection
- LSTM identifies malicious HTTP requests
- Mini deployment of machine learning model based on URL anomaly detection
- My AI Security Detection Study Notes (1)
- Web attack classification and detection model based on machine learning
- Machine Learning Based Attack Detection System
- WAF Construction and Operation and AI Application Practice
- Advantages of Machine Learning in Web Security Detection
- [APT detection based on machine learning](https://mp.weixin.qq.com/s?__biz=MzU5MTM5MTQ2MA==&mid=2247484139&idx=1&sn=0da63a49f341eccc0bb48c954d8ebbb4&chksm=fe2efd60c95974767521fe6a6b7257a1d05e5482fc7ddeda281bdf0f0deb20add82d1a82d8ec&mpshare=1&scene=1&srcid=&pass_ticket=bjnNiDKomd79pQvRonW%2BXsTe6JrO%2FFs6oII12dZaLBPuQOtNK6Rzh9WSJ %2B%2F89ZUA#rd)
- RSAC 2019 | Machine Learning Algorithm Analysis Engine Helps Security Threat Reasoning Analysis
- Solving the last mile between machine learning and security operations
- RSAC 2019 | Using NLP Machine Learning for Automated Compliance Risk Management
- Shumei Risk Control
- Aliyun Artificial Intelligence waf
- Du Zhongwei: Identification and Traceability of Shell Black Ash Products
- How to build a good intelligent risk control tool system?
- Automated Iteration of Intelligent Risk Control Model
- Fourth Paradigm Intelligent Risk Control Middle Platform Architecture Design and Application
- 58 City Risk Control Platform Evolution
- Risk Control Modeling Process: Take the JD Group Perception Project as an Example
- Huya Risk Control
- Betta Fish
1、Samples of Security Related Dats
2、DARPA Intrusion Detection Data Sets
5、Data Capture from National Security Agency
6、The ADFA Intrusion Detection Data Sets
9、Multi-Source Cyber-Security Events
10、Malware Training Sets: A machine learning dataset for everyone
-
Vulnbank_dataset. A competition project of the KDD competition, the main purpose is to use machine learning methods to build an intrusion detector. The intrusion behaviors mainly include: DDOS, password brute force cracking, buffer overflow, scanning and other attack behaviors.
- https://github.com/LiaoWenzhe
- https://github.com/yzhao062/pyod
- https://github.com/yzhao062/anomaly-detection-resources
- Machine Learning in Cybersecurity Collection
- The Ultimate Security Data Science and Machine Learning Guide
- Machine Learning for Cyber Security
- 404 Master's finishing
- Awesome-AI-Security
- awesome-ml-for-cybersecurity
- The Definitive Security Data Science and Machine Learning Guide
- https://github.com/0xMJ/AI-Security-Learning
- Dark Cloud
- Propose good ideas and directions
- Liu Zhiyuan: Where do good research ideas come from
- MIT Artificial Intelligence Lab: How to do research
- ReadPaper paper reading platform
- arxiv
- google scholar
- Baidu rasp security detection tool
- Ali Security Emergency Response Center
- Tencent Security Emergency Response Center
- Baidu Security Emergency Response Center
- freebuf
- 先知社区
- BlackHat / BlackHat Asia
- owasp
- botconf
- DEF-CON
- S&P
- CCS
- ICDFC
- USENIX Security
- PETS
- Wisec
- CODASPY
- ICSE
- NDSS
- Computer & Security
- TDSC
- RSAC
- Omniscience Technology
- salt
- NSFOCUS
- Anheng Information
- Flash information
- Qi Anxin
- DataCon
- DataFountain
- "Introduction to Machine Learning for Web Security"
- "Deep Learning in Web Security"
- "Reinforcement Learning and Gan of Web Security"
- https://blog.csdn.net/Liao_Wenzhe/
- http://iami.xyz
- https://www.cdxy.me/
- Alibaba Cloud Security
- https://www.blackhat.com/docs/asia-17/materials/asia-17-Dong-Beyond-The-Blacklists-Detecting-Malicious-URL-Through-Machine-Learning.pdf
- https://i.blackhat.com/briefings/asia/2018/asia-18-Simakov-Marina-Breaking-The-Attack-Graph.pdf
- https://i.blackhat.com/asia-19/Fri-March-29/bh-asia-Pham-Automated-REST-API-Endpoint.pdf
- https://i.blackhat.com/asia-20/Friday/asia-20-Hao-Attacking-And-Defending-Machine-Learning-Applications-Of-Public-Cloud.pdf
- https://i.blackhat.com/eu-19/Wednesday/eu-19-Kettle-HTTP-Desync-Attacks-Request-Smuggling-Reborn.pdf
- https://www.blackhat.com/docs/us-17/wednesday/us-17-Gil-Web-Cache-Deception-Attack.pdf
- https://www.blackhat.com/docs/us-17/wednesday/us-17-Burnett-Ichthyology-Phishing-As-A-Science-wp.pdf
- https://i.blackhat.com/us-18/Thu-August-9/us-18-Kettle-Practical-Web-Cache-Poisoning-Redefining-Unexploitable.pdf
- https://i.blackhat.com/us-18/Thu-August-9/us-18-Kettle-Practical-Web-Cache-Poisoning-Redefining-Unexploitable.pdf
- https://i.blackhat.com/USA-19/Wednesday/us-19-Valenta-Monsters-In-The-Middleboxes-Building-Tools-For-Detecting-HTTPS-Interception.pdf
- https://i.blackhat.com/USA-20/Wednesday/us-20-Kettle-Web-Cache-Entanglement-Novel-Pathways-To-Poisoning.pdf
- https://www.163.com/dy/article/GPJBLI020511CJ6O.html
- https://i.blackhat.com/USA-20/Wednesday/us-20-Klein-HTTP-Request-Smuggling-In-2020-New-Variants-New-Defenses-And-New-Challenges.pdf
- https://i.blackhat.com/EU-21/Wednesday/EU-21-Thatcher-Practical-HTTP-Header-Smuggling.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Gavrichenkov-Breaking-HTTPS-With-BGP-Hijacking-wp.pdf
- https://www.blackhat.com/docs/us-16/materials/us-16-Sivakorn-HTTP-Cookie-Hijacking-In-The-Wild-Security-And-Privacy-Implications-wp.pdf
- https://towardsdatascience.com/deep-learning-for-specific-information-extraction-from-unstructured-texts-12c5b9dceada
- https://portswigger.net/research/cracking-the-lens-targeting-https-hidden-attack-surface
- https://www.botconf.eu/category/keynote/
- https://www.botconf.eu/2016/getting-your-hands-dirty-how-to-analyze-the-behavior-of-malware-traffic-and-web-connections/
- https://www.botconf.eu/2015/dga-clustering-and-analysis-mastering-modern-evolving-threats/
- https://www.blackhat.com/us-16/briefings/schedule/#account-jumping-post-infection-persistency--lateral-movement-in-aws-4309
- https://www.blackhat.com/us-16/briefings/schedule/#http-cookie-hijacking-in-the-wild-security-and-privacy-implications-3467
- https://www.blackhat.com/docs/us-17/wednesday/us-17-Kettle-Cracking-The-Lens-Exploiting-HTTPs-Hidden-Attack-Surface.pdf
- https://www.blackhat.com/docs/us-17/wednesday/us-17-Kettle-Cracking-The-Lens-Exploiting-HTTPs-Hidden-Attack-Surface-wp.pdf
- https://www.blackhat.com/docs/us-17/thursday/us-17-Prandl-PEIMA-Harnessing-Power-Laws-To-Detect-Malicious-Activities-From-Denial-Of-Service-To-Intrusion-Detection-Traffic-Analysis-And-Beyond.pdf
- https://www.blackhat.com/docs/us-17/thursday/us-17-Prandl-PEIMA-Harnessing-Power-Laws-To-Detect-Malicious-Activities-From-Denial-Of-Service-To-Intrusion-Detection-Traffic-Analysis-And-Beyond-wp.pdf
- https://www.blackhat.com/docs/us-17/thursday/us-17-Hypponen-The-Epocholypse-2038-Whats-In-Store-For-The-Next-20-Years.pdf
- https://www.blackhat.com/docs/us-16/materials/us-16-Amiga-Account-Jumping-Post-Infection-Persistency-And-Lateral-Movement-In-AWS.pdf
- https://www.blackhat.com/docs/us-16/materials/us-16-Sivakorn-HTTP-Cookie-Hijacking-In-The-Wild-Security-And-Privacy-Implications.pdf
- https://www.blackhat.com/docs/us-16/materials/us-16-Gelernter-Timing-Attacks-Have-Never-Been-So-Practical-Advanced-Cross-Site-Search-Attacks.pdf
- https://www.blackhat.com/docs/us-16/materials/us-16-Ermishkin-Viral-Video-Exploiting-Ssrf-In-Video-Converters.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Gavrichenkov-Breaking-HTTPS-With-BGP-Hijacking.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Zadeh-From-False-Positives-To-Actionable-Analysis-Behavioral-Intrusion-Detection-Machine-Learning-And-The-SOC.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Zadeh-From-False-Positives-To-Actionable-Analysis-Behavioral-Intrusion-Detection-Machine-Learning-And-The-SOC-wp.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Morgan-Web-Timing-Attacks-Made-Practical.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Morgan-Web-Timing-Attacks-Made-Practical-wp.pdf
- https://www.blackhat.com/docs/us-15/materials/us-15-Saxe-Why-Security-Data-Science-Matters-And-How-Its-Different.pdf
- https://www.blackhat.com/docs/us-14/materials/us-14-Pinto-Secure-Because-Math-A-Deep-Dive-On-Machine-Learning-Based-Monitoring-WP.pdf
- https://www.blackhat.com/docs/us-14/materials/us-14-Pinto-Secure-Because-Math-A-Deep-Dive-On-Machine-Learning-Based-Monitoring.pdf
- https://media.blackhat.com/us-13/US-13-Pinto-Defending-Networks-with-Incomplete-Information-A-Machine-Learning-Approach-Slides.pdf
- https://paper.bobylive.com/Meeting_Papers/BlackHat/USA-2013/US-13-Pinto-Defending-Networks-with-Incomplete-Information-A-Machine-Learning-Approach-Slides.pdf
- https://paper.bobylive.com/Meeting_Papers/BlackHat/USA-2013/US-13-Peck-Abusing-Web-APIs-Through-Scripted-Android-Applications-WP.pdf
- https://www.youtube.com/watch?v=RGqCZO3cgY8
- https://www.youtube.com/watch?v=JUY4DQZ02o4
- https://www.youtube.com/watch?v=D6MG2uBIfUI
- https://paper.bobylive.com/Meeting_Papers/BlackHat/USA-2011/BH_US_11_Balduzzi_HPP_Slides.pdf
- https://www.blackhat.com/html/bh-us-11/bh-us-11-archives.html
- https://www.blackhat.com/docs/eu-14/materials/eu-14-Hafif-Reflected-File-Download-A-New-Web-Attack-Vector.pdf
- https://www.madlab.it/slides/BHEU2011/whitepaper-bhEU2011.pdf
- https://infocon.org/cons/Black%20Hat/Black%20Hat%20Europe/Black%20Hat%20Europe%202011/Presentations/Raul_Siles/BlackHat_EU_2011_Siles_SAP_Session-WP.pdf
- https://www.blackhat.com/presentations/bh-europe-09/Zanero_Criscione/BlackHat-Europe-2009-Zanero-Criscione-Masibty-Web-App-Firewall-slides.pdf
- https://infocon.org/cons/Black%20Hat/Black%20Hat%20USA/Black%20Hat%20USA%202007/presentations/Bolzoni_and_Zambon/Whitepaper/bh-usa-07-bolzoni_and_zambon-WP.pdf
- https://www.blackhat.com/html/bh-media-archives/bh-archives-2007.html#eu_07