Update README.md

README.md

@@ -60,7 +60,7 @@ Specific Objectives :
 
 **Summary of Individual Components**
 
-IT17106702 – W.H Chanuka
+**IT17106702 – W.H Chanuka**
 
 •	Identify the IRC(Internet Relay Chat) traffic which will be extracted during a Mobile Botnet DDoS attack.
 •	Finding a reliable Machine Learning algorithm to train the model - Naïve Bayes
@@ -82,7 +82,7 @@ The dataset, which is used to train the Mobile Botnet attack detection model is
 Data Set used  : https://www.uvic.ca/engineering/ece/isot/datasets/
 
 
-IT17111034 - U.C.S. Bandara
+**IT17111034 - U.C.S. Bandara**
 
 •	Filter out the normal internet traffic and analyze the NTP Responses to detect whether it is an NTP Amplification attack or not.
 •	Finding a reliable Machine Learning algorithm to train the model -  Support Vector (SVM)
@@ -104,7 +104,7 @@ Dataset of the NTP Amplification attack is extracted from the Canadian Institute
 Data Set used : http://205.174.165.80/CICDataset/CICDDoS2019/Dataset/CSVs/
 
 
-IT17124904 - A.M.N. Eshan
+**IT17124904 - A.M.N. Eshan**
 
 •	Filter out the normal internet traffic and analyze the .pcap files whether it is a Slow Loris attack or not.
 •	Finding a reliable Machine Learning algorithm to train the model - Linear Regression
@@ -126,7 +126,7 @@ The dataset, which is used to train the Slowloris detection module, is published
 Data Set used  : http://205.174.165.80/CICDataset/ISCX-SlowDos-2016/Dataset/
 
 
-IT17114172 – A.U. Sudugala
+**IT17114172 – A.U. Sudugala**
 
 •	Filter out the normal internet traffic and analyze the .pcap files whether it is a Volumetric DDoS attack or not.
 •	Finding a reliable Machine Learning algorithm to train the model – Decision Tree
@@ -156,6 +156,10 @@ http://gitlab.sliit.lk/2020---092/2020-092/blob/master/Images/system_overview_di
 
 According to the above diagram, first malicious traffic signatures should be obtained from the raw data and then added to the database. In order do this, datasets which are related to the NTP Amplification attacks, Mobile Botnet attacks, Slowloris attacks and Volumetric attacks are used. Then, by using the feature selection, generation of the SDS will be done and afterwards the Machine Learning Algorithm is being trained. Then it is supplied to the system of traffic classification.
 
+**System Backend Diagram**
+
+As shown in the above figure admin dashboard is connected with the REST API and also it is connected to the client server. Once a request comes to the server it will be directed through the REST API. Therefore, it will detect whether it’s a malicious packet or not. If it is a malicious packet, the user will be alerted through the admin dashboard.
+
 **Other Necessary Instructions to run the code:**