Sheriff. DDoS detection system презентация

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MISSION Today it is difficult to imagine a successful organization

MISSION

Today it is difficult to imagine a successful organization that does

not have a web sites. Moreover most companies directly depend on their performance. But not every service guarantees trouble-free operation when faced with a DDoS attack. The goal of the project in brief is to preserve the autonomy of the resource, by providing protection and minimizing the threat from DDoS attacks.
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NOT ON MY SHIFT

NOT ON MY SHIFT

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01 DDoS Overview DoS and DDoS, its classifications and how

01

DDoS Overview

DoS and DDoS, its classifications and how to defend

Grafana monitoring

System

of monitoring and visualization of traffic

03

02

04

ML Algorithm

Self learning algorithm which analyzes traffic

Alerts and notification

Customization alerts for anomalies

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DDoS overview 01

DDoS overview

01

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DoS and DDoS DOS attack an external attack on an

DoS and DDoS

DOS attack an external attack on an information system

that leads to a denial of service. As a rule, the goal is to prevent users from gaining access to such a system, or to make it very difficult for them to obtain such access

DDoS using not only single computer to fill the complete transfer speed of the server, so a disseminated assault from different machines is more often than not used-a DDoS assault. Tainted machines from which demands are sent are too called zombies.

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Structure of DDoS attacker zombie / agent handler handler zombie / agent victim

Structure of DDoS

attacker

zombie / agent

handler

handler

zombie / agent

victim

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DDOS AND PREVENTION You should build DDoS protection at all

DDOS AND PREVENTION

You should build DDoS protection at all levels. You

can pass traffic through the cleaning network, organize site protection at the transport and network levels

All devices connected to the Internet can potentially become part of an attacker’s infrastructure and be used in DDoS attacks.

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ML algorithm 02

ML algorithm

02

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ML algorithm 95% Accuracy A major theme of forecasting at

ML algorithm

95%

Accuracy

A major theme of forecasting at scale is that analysts

with a variety of backgrounds must make more forecasts than they can do manually forecasting time series data based on an preservative model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
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GRAFANA MONITORING 03

GRAFANA MONITORING

03

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TRAFFIC MONITORING We visualized traffic and predicted traffic to control

TRAFFIC MONITORING

We visualized traffic and predicted traffic to control any anomalies

and if your traffic exceed the alert line you will get the push

Data and predict

alert

prediction

real traffic

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CONFIGURE ALERTS 04

CONFIGURE ALERTS

04

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ALERT! Grafana has different notification variations. From a primitive message

ALERT!

Grafana has different notification variations. From a primitive message to a

detailed description and reason for the notification, notifications can also be configured for a group of users, including messengers, using their API
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