[D2 COMMUNITY] Open Container Seoul Meetup - Kubernetes를 이용한 서비스 구축과 openshiftNAVER D2
Junho Lee is a Solutions Architect who has worked at Rockplace Inc. since 2014. The document compares Kubernetes (k8s), OpenShift, and Google Kubernetes Engine (GKE). k8s is an open-source container cluster manager originally designed by Google. OpenShift is Red Hat's container application platform based on k8s. GKE provides k8s clusters on Google Cloud Platform. Both OpenShift and GKE add services on top of k8s like app stores, logging, monitoring and technical support. The document outlines the key components, architectures and capabilities of each platform.
[D2 COMMUNITY] Open Container Seoul Meetup - Running a container platform in ...NAVER D2
This document discusses containers and related technologies like Docker, Kubernetes, and Openshift. It provides an overview of the container approach taken by GS Shop including their experience running non-microservice applications on containers in production. Some areas they are currently working on include containerized stateful services, multi-tenant container infrastructure, and container infrastructure provisioning automation.
This document discusses deep learning and its applications. It provides an overview of deep learning, including how it is used for tasks like speech recognition, machine translation, and image classification. It then discusses deep learning applications at NAVER, including using convolutional neural networks for image classification and recurrent neural networks for language modeling. The document also covers important aspects of deep learning like new algorithms, large datasets, and specialized hardware.
OWASP Top 10 2013이 발표되었습니다. 이번 업데이트는 2010년 Top 10에 비해 일반적이면서도 중요한 취약점 분류
기준을 확대 적용하였으며, 얼마나 많이 퍼져있는가를 기준으로 순위를 재조정하였습니다. 또한 2010년 Top 10의
‘A6:보안 설정 오류’의 세부적인 설명의 모호함을 해소하고자, 위협 분류 가운데 컴포넌트 보안을 새로 포함하였습니다.
OWASP Top 10 2013은 애플리케이션 보안을 전문으로 하는 7개 기업의 8개 데이터세트를 토대로 하였습니다. 이
데이터들은 수백 개 기업, 수천 개의 애플리케이션에 걸친 500,000개 이상의 취약점들을 포함하고 있습니다. Top 10 각 항목들은 이 가운데 가장 많이 퍼져있는 데이터를 기준으로, 취약점 공격 가능성, 탐지 가능성, 그리고 영향 평가 등을 함께 고려하여 선정되었습니다.
OWASP Top 10을 선정하는 가장 큰 이유는 가장 중요한 웹 애플리케이션의 보안 취약점 개발자, 설계자, 아키텍트, 운영자, 혹은 기관들에게 주요 웹 애플리케이션 보안 취약점으로 인한 영향을 알리기 위해서입니다. Top 10은 위험도가 큰 문제들에 대해 대응할 수 있는 기본적인 기술을 제공하며, 또한 이를 근거로 향후 방향을 제시하고 있습니다.
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.
6. • HTTPS (보안웹서버) 중에서
• 취약한버전의openssl을 사용하는 경우
• 특정정보가노출될수있는 취약점
무슨취약점이길래?
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7. Ransomware의 진화 : 관리상의 취약점 악용, DB데이터 암호화
1만2천대에서 2만7천대 정도 피해 받았을 것으로. 0.1 BTC ~1 BTC 요구
27017번 포트 접근을 차단하거나, 서버에 접근을 제한하기 위해 로컬 IP 주소 바인딩을설정
MongoDB hacked..
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11. • SHE (Security Hole Examiner)
• LOOKER
• ILVA (Integrated Log Analysis Tool)
History: SHE & .. (1997)
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12. KANE(KAIST Anti-Network Epidemic Framework)
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계층적 구조를 갖는 대규모 네트워크에서의 협력적인 침입 탐지 및 대응 프레임워크
(WISC 2001에서 발표)
인간 의학의 전염병 대응 체계(방역)과 유사하게 사이버 침해를 다루자는 아이디어
History : KANE(2001)
Cooperative and Autonomous Methodologies
KAIST domain specific
Prevention : Vulnerability Scanning and Patching
Detection : Real time Intrusion Detection Recover
y : Restore before-the-attack system state Investi
gation : TraceAttacker
Isolation : Isolate Attacked system to investigate and prevent from re-intrusion
Similar to medical epidemic control process
Network-based Anomaly Detection
Detect anomalous or malicious network packets
Focus on unknown or modified attacks
Investigation of protocol or program specification Applicatio
n IDS for Web server in INTRANET Environments Attack C
ategorization on detection attacks
13. • SAD (Session Anomaly Detection)
• Web Session Anomaly Detection
• Computing the degree of anomaly compared to established usage PatternsUsing
Web Sessions from raw Web Log data
• SAD Viewer
• Visualization for Web Usage Pattern
• Visualization for Anomaly and Misuse Detection in Web
• Visually flag suspicious sessions using yellow or red flags
• Real Time Monitoring of Web Session
History : SAD(2003)
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23. Bug Hunter, 프리랜서(pen-tester)
보안 업체 : 보안 컨설턴트, 보안솔루션개발자
일반 기업 : 보안실무자
공무원 : 행정자치부, 미래부,국정원
학계 : 대학, 대학원생,교수
연구자 : KISA, 금융보안원, 국가보안기술연구소, ETRI등등
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무궁무진해요
42. Penetration Test(pen-test)
-is an attack on a computer system with the
intention of finding security weaknesses, pot
entially gaining access to it, its functionality a
nd data
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Promising Security Area
43. Determining the feasibility of a particular set of attack vectors
Identifying higher-risk vulnerabilities that result from a combination of
lower-risk vulnerabilities exploited in a particular sequence
Identifying vulnerabilities that may be difficult or impossible to detect with
automated network or application vulnerability scanning software
Assessing the magnitude of potential business and operational impacts of
successful attacks
Testing the ability of network defenders to successfully detect and
respond to the attacks
Providing evidence to support increased investments in security personnel
and technology
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Penetration Testing