[4th revolution] new technology security education material] android security...james yoo
Interest in mobile security is increasing due to the development of the future environment. Through the diagnostic criteria of mobile security, we have made the diagnostic case for beginners to study and study. I hope it helps many people.
[4th revolution] new technology security education material] android security...james yoo
Interest in mobile security is increasing due to the development of the future environment. Through the diagnostic criteria of mobile security, we have made the diagnostic case for beginners to study and study. I hope it helps many people.
[Main Session] 보안을 고려한 애플리케이션 개발 공정 및 실무적 수행 방법 소개 Oracle Korea
애플리케이션 개발부터 배포 및 운영에 이르는 전 단계에서 보안을 강화하는 개념인 “소프트웨어 보안 보증”(Software Security Assurance)의 개념과 안전한 소프트웨어 개발 공정(S-SDLC), 그리고 실제 적용 방법을 간단하게 살펴보고자 합니다. 구체적으로 젠킨스와 Secure Coding 솔루션인 정적 보안 분석 툴을 연계하여 소스 코드의 취약점을 진단하는 과정을 살펴보고, 보안적으로 취약한 웹 애플리케이션을 Production 환경에서 보호하는 RASP 기술도 함께 시연할 예정입니다.
* 본 세션은 “입문자/초급자/중급자” 분들께 두루 적합한 세션입니다.
In the era of the 4th industrial revolution, the mobile era is approaching. Therefore, when open banking is activated, those who research security and IT need basic knowledge to conduct a mobile diagnosis. That's why we basically want to share an environment that can be diagnosed without a smartphone.
[2013 CodeEngn Conference 08] CherishCat - 각종 취약점과 대응방안 & 해킹, 보안 문제풀이GangSeok Lee
2013 CodeEngn Conference 08
보안사고는 사소한 취약점으로부터 시작되어 악용될 수 있다. Hard한 방법이나 별다른Hacking Tool을 사용하지 않은 간단한 발상으로 취약점을 찾아내어 보자. software부분과 Web Site부분에서 악용될 수 있는 여러 가지 취약점들을 실제 사례를 통해서 설명한다. 마지막으로 각종 해킹/보안 관련 문제들을 연습해볼 수 있는 War-Game사이트인 hack-me.org에 등록되어있는 문제들을 풀어본다. 국내외 해킹방어대회에서 다루는 문제들의 기반이 되는 기초적인 접근방법을 hack-me_Challenges를 통해서 입문자들도 알기 쉽게 각 문제유형들을 알아본다.
http://codeengn.com/conference/08
The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like LSTM, bi-LSTM, and convolutional neural networks. Finally, it presents results from evaluating different chatbot models on various metrics like validation score.
[Main Session] 보안을 고려한 애플리케이션 개발 공정 및 실무적 수행 방법 소개 Oracle Korea
애플리케이션 개발부터 배포 및 운영에 이르는 전 단계에서 보안을 강화하는 개념인 “소프트웨어 보안 보증”(Software Security Assurance)의 개념과 안전한 소프트웨어 개발 공정(S-SDLC), 그리고 실제 적용 방법을 간단하게 살펴보고자 합니다. 구체적으로 젠킨스와 Secure Coding 솔루션인 정적 보안 분석 툴을 연계하여 소스 코드의 취약점을 진단하는 과정을 살펴보고, 보안적으로 취약한 웹 애플리케이션을 Production 환경에서 보호하는 RASP 기술도 함께 시연할 예정입니다.
* 본 세션은 “입문자/초급자/중급자” 분들께 두루 적합한 세션입니다.
In the era of the 4th industrial revolution, the mobile era is approaching. Therefore, when open banking is activated, those who research security and IT need basic knowledge to conduct a mobile diagnosis. That's why we basically want to share an environment that can be diagnosed without a smartphone.
[2013 CodeEngn Conference 08] CherishCat - 각종 취약점과 대응방안 & 해킹, 보안 문제풀이GangSeok Lee
2013 CodeEngn Conference 08
보안사고는 사소한 취약점으로부터 시작되어 악용될 수 있다. Hard한 방법이나 별다른Hacking Tool을 사용하지 않은 간단한 발상으로 취약점을 찾아내어 보자. software부분과 Web Site부분에서 악용될 수 있는 여러 가지 취약점들을 실제 사례를 통해서 설명한다. 마지막으로 각종 해킹/보안 관련 문제들을 연습해볼 수 있는 War-Game사이트인 hack-me.org에 등록되어있는 문제들을 풀어본다. 국내외 해킹방어대회에서 다루는 문제들의 기반이 되는 기초적인 접근방법을 hack-me_Challenges를 통해서 입문자들도 알기 쉽게 각 문제유형들을 알아본다.
http://codeengn.com/conference/08
The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like LSTM, bi-LSTM, and convolutional neural networks. Finally, it presents results from evaluating different chatbot models on various metrics like validation score.
The document discusses challenges with using reinforcement learning for robotics. While simulations allow fast training of agents, there is often a "reality gap" when transferring learning to real robots. Other approaches like imitation learning and self-supervised learning can be safer alternatives that don't require trial-and-error. To better apply reinforcement learning, robots may need model-based approaches that learn forward models of the world, as well as techniques like active localization that allow robots to gather targeted information through interactive perception. Closing the reality gap will require finding ways to better match simulations to reality or allow robots to learn from real-world experiences.
[243] Deep Learning to help student’s Deep LearningNAVER D2
This document describes research on using deep learning to predict student performance in massive open online courses (MOOCs). It introduces GritNet, a model that takes raw student activity data as input and predicts outcomes like course graduation without feature engineering. GritNet outperforms baselines by more than 5% in predicting graduation. The document also describes how GritNet can be adapted in an unsupervised way to new courses using pseudo-labels, improving predictions in the first few weeks. Overall, GritNet is presented as the state-of-the-art for student prediction and can be transferred across courses without labels.
[234]Fast & Accurate Data Annotation Pipeline for AI applicationsNAVER D2
This document provides a summary of new datasets and papers related to computer vision tasks including object detection, image matting, person pose estimation, pedestrian detection, and person instance segmentation. A total of 8 papers and their associated datasets are listed with brief descriptions of the core contributions or techniques developed in each.
[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지NAVER D2
This document presents a formula for calculating the loss function J(θ) in machine learning models. The formula averages the negative log likelihood of the predicted probabilities being correct over all samples S, and includes a regularization term λ that penalizes predicted embeddings being dissimilar from actual embeddings. It also defines the cosine similarity term used in the regularization.
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기NAVER D2
The document discusses running a TensorFlow Serving (TFS) container using Docker. It shows commands to:
1. Pull the TFS Docker image from a repository
2. Define a script to configure and run the TFS container, specifying the model path, name, and port mapping
3. Run the script to start the TFS container exposing port 13377
The document discusses linear algebra concepts including:
- Representing a system of linear equations as a matrix equation Ax = b where A is a coefficient matrix, x is a vector of unknowns, and b is a vector of constants.
- Solving for the vector x that satisfies the matrix equation using linear algebra techniques such as row reduction.
- Examples of matrix equations and their component vectors are shown.
This document describes the steps to convert a TensorFlow model to a TensorRT engine for inference. It includes steps to parse the model, optimize it, generate a runtime engine, serialize and deserialize the engine, as well as perform inference using the engine. It also provides code snippets for a PReLU plugin implementation in C++.
The document discusses machine reading comprehension (MRC) techniques for question answering (QA) systems, comparing search-based and natural language processing (NLP)-based approaches. It covers key milestones in the development of extractive QA models using NLP, from early sentence-level models to current state-of-the-art techniques like cross-attention, self-attention, and transfer learning. It notes the speed and scalability benefits of combining search and reading methods for QA.
1. How can I hack your applic
ation?
Seokha Lee
Security Researcher of SEWORKS
Real Geek Hacker(wh1ant)
2. Agenda
Who are we?
Attack techniques
Game hacking
DEMO
Code obfuscation
Q&A
3. Real Geeks, SEWORKS
www.seworks.co
해커가 해커를 막는다.
지구상에 존재하는 모든 앱
앱 난독화, 앱 위변조 방지, 메모리 해킹 방지 etc.
Investor : Qualcomm, Softbank
Silicon Valley H.Q /Seoul R&D Center
wh1ant@seworks.co.kr