@@ -54,22 +54,22 @@ To achieve the main object, another specific objective is to identify Volumetric
**Summary of Individual Components**
**Identifying Mobile Botnet DDoS attacks**
**Identifying Mobile Botnet DDoS attacks - IT17106702**
This component is to identify the IRC(Internet Relay Chat) traffic beside normal traffic and figure out whether it is a Mobile Botnet attack or not. If there are any suspicious traffic, a notification should be given to the user. By identifying Mobile Botnet DDoS attack, the detection system can ensure the availability of a system for the legitimate users without any interruption. And also reduce financial and other losses of the industries and governments worldwide.
Data Set used : https://www.uvic.ca/engineering/ece/isot/datasets/
The component is implementing to filter out the normal internet traffic and identify the NTP Responses and identify whether it is a NTP Amplification attack or not. After identifying the Network traffic if there is any suspicious traffic a Notification should be given.
Data Set used : http://205.174.165.80/CICDataset/CICDDoS2019/Dataset/CSVs/
**Identifying Slow Loris attacks**
**Identifying Slow Loris attacks - IT17124904**
This component is to identify the .pcap files and figure out whether it is a Slow Loris attack or not.is there any suspicious traffic, partial HTTP requests, a notification should be given to the user. Is that suspicious traffic is Slow Loris attack, system detect the attack type and ensure the availability of the systems for the legitimate users without any interruption.
Data Set used : http://205.174.165.80/CICDataset/ISCX-SlowDos-2016/Dataset/
**Identifying Volumetric attacks**
**Identifying Volumetric attacks - IT17114172**
Volumetric Distributed Denial of Service attack is one of the severe malicious attack which can be seen on Internet and it is responsible for more than half of all kinds of those attacks. This components are to detect volumetric attacks using machine learning and to make a safe environment for the users without DDoS disruption.
Data Set used : Extracted DDoS Flows from CSE-CIC-IDS2018- AWS, CICIDS2017, CIC DoS dataset(2016)
