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[네트워크 실습] Intrusion Detection and Honeypot Analysis🌐 네트워크 2022. 10. 11. 19:58
Objective 1) learn how to analyze the log of a honeypot and determine information about the attacks that occurred. 2) learn the behavior of attackers and the measures that should be taken to make system safe. Task The file "attack.log" contains logs recorded during the time of the attack. Use Wireshark to analyze it. 1. What is the IP of the attacker? How about its physical location (country) ? ..
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[네트워크실습] DHCP serv configuration카테고리 없음 2022. 9. 26. 17:08
network configuration : Ethernet and DHCP 실습 1. Build a Local Area Network (LAN) and analyze network traffic. 2. After analyzing the basic network properties, connect a router to the network. (In this first phase, it will only be used as a DHCP server) 3. Configure a DHCP server with the IPRange provided. 4 . Connect machines to each other and connect two networks with different subnets to each ..
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[Abbreviations] 네트워크 용어🌐 네트워크 2022. 9. 26. 16:11
ARP Address Resolution Protocol DHCP Dynamic Host Configuration Protocol HTTP Hypertext Transfer Protocol ICMP Internet Control Message Protocol IP Internet Protocol RJ Registered Jack TCP Transmission Control Protocol URL Uniform Resource Locator UTP Unshielded Twisted Pair
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List of books to learn time series analysis데이터 분석/시계열 분석 2022. 9. 22. 21:50
Recommended • Shumway, R. H. and Stoffer, D. S. (2006). Time Series Analysis and its Applications: with R examples. Springer. Introductory books • Brockwell, P.J. and Davis, R.A. (2002). Introduction to Time Series and Forecasting (2nd edition). Springer. • Chatfield, C. (2004): The Analysis of Time Series: An Introduction (6th Edition). Chapman and Hall. • Diggle, P.J. (1990). Time Series. A Bi..
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[colab] 구글 코랩 - 파일 업로드🌏 개발환경 2022. 9. 15. 21:38
https://medium.com/@hanSolow/importing-datasets-in-google-colab-c816fc654f97 Importing Datasets in Google Colab Have you ever had trouble with setting up Jupyter Notebook on your computer, managing different versions of python and anaconda? While… medium.com
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[자연어처리/NLP] 단어 임베딩: word2vec머신러닝 || 딥러닝 2022. 9. 8. 08:44
원핫인코딩의 단점: -단어의 존재여부만 표현할뿐 단어사이의 맥락은 표현하지 못한다. -문장내의 단어가 많아질수록 벡터의 차원이 증가된다. 해결책 : 단어를 다차원공간에 벡터화 하자 ! => aka 단어 임베딩 word2vec -단어 임베딩 알고리즘, 딥러닝을 이용한 비지도학습 -단어의 앞뒤 관계를 보고 근접도를 계산하여 벡터 형태로 정량화하는 기법 (200~1000차원 정도에서 벡터로 표현) - e.g. woman+king-man = queen - "같은 맥락에 있는 단어는 서로 가까운 의미를 가지고있다"라는 전제 (각 단어의 앞뒤 관계가 비슷한 단어들은 비슷한 공간에 몰려있게된다) -단어가 주어졌을때 근처에 등장하는 다른 단어는 비슷한 의미의 단어일 가능성이 높다 word2vec 의 두가지 모델 -CB..