gamja99 Goto Github PK

2020.spring.ai4everyone

2020학년도 1학기 인공지능의 이해 (융합연계전공)

ai-tech-interview

👩‍💻👨‍💻 AI 엔지니어 기술 면접 스터디

aip-custom-tracking-portal-samples

Store AIP Custom Tracking Portal Samples.

algorithm-test

anomaly-detection-in-logs

Using Unsupervised methods to identify anomalies in user behaviour through IP Profiling

anomaly_detection_test

for Anomaly Detection Test

anomaly_email_detection

Abnormal Email Data Analysis Project

atap

Code for Applied Text Analysis with Python

atlassian-python-api

Atlassian Python REST API wrapper

awesome-anomaly-detection

A curated list of awesome anomaly detection resources

awesome-automl-papers

A curated list of automated machine learning papers, articles, tutorials, slides and projects

awesome-fraud-detection-papers

A curated list of data mining papers about fraud detection.

cicids-2017-intrution-detection-

Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and accurate performance evolutions. Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable. Some of these datasets suffer from the lack of traffic diversity and volumes, some do not cover the variety of known attacks, while others anonymize packet payload data, which cannot reflect the current trends. Some are also lacking feature set and metadata. CICIDS2017 dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using CICFlowMeter with labeled flows based on the time stamp, source, and destination IPs, source and destination ports, protocols and attack (CSV files). Also available is the extracted features definition. Generating realistic background traffic was our top priority in building this dataset. We have used our proposed B-Profile system (Sharafaldin, et al. 2016) to profile the abstract behavior of human interactions and generates naturalistic benign background traffic. For this dataset, we built the abstract behaviour of 25 users based on the HTTP, HTTPS, FTP, SSH, and email protocols. The data capturing period started at 9 a.m., Monday, July 3, 2017 and ended at 5 p.m. on Friday July 7, 2017, for a total of 5 days. Monday is the normal day and only includes the benign traffic. The implemented attacks include Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet and DDoS. They have been executed both morning and afternoon on Tuesday, Wednesday, Thursday and Friday.

cjons-hackaton

clx

A collection of RAPIDS examples for security analysts, data scientists, and engineers to quickly get started applying RAPIDS and GPU acceleration to real-world cybersecurity use cases.

codeengn

write-up

cyberthreathunting

A collection of resources for Threat Hunters

cycle_gan_project

data-wrangling-with-python

Simplify your ETL processes with these hands-on data sanitation tips, tricks, and best practices

data_study

Zookeeper, Flume, Kafka, Sorm, Esper, HBase, Hadoop, Redis, Hue, Hive, Spark, Zeppelin, Mahout, TF

dcso

Supplementary material for KDD 2018 workshop "DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles"

detectlm

Detecting Lateral Movement with Machine Learning

detext

Detection of malicious data exfiltration over DNS using Machine Learning techniques

easyocr

Ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai

examples

Home for Elasticsearch examples available to everyone. It's a great way to get started.

food_detection-ocr

gamja99.github.io

A minimal, responsive, and powerful Jekyll theme for presenting professional writing.

hai

HIL-based Augmented ICS (HAI) Security Dataset

hands-on-time-series-analylsis-python

Source Code for 'Hands-on Time Series Analysis with Python' by B V Vishwas and Ashish Patel

harmful-text-detection_da