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Malware Detection Using Network Traffic Analysis and Predicting Accuracy Using Deep Learning Algorithms

EasyChair Preprint no. 7115

7 pagesDate: November 29, 2021

Abstract

In this project, We will be analyzing malicious activity in our network like botnets, DDOS attack, SQL injection & erroneous packets in our Network traffic generated & analyze it using invaluable tools that allow for applied experimentation to find & calculate the working & performance of our networks, the infrastructure of our networks and the security preventive measures, by simulating and modelling the data packets and the payloads of those packets that would be generated by machines & devices on the network infrastructure like packets capturing & analysis using Wireshark. mainly for the secure & private applications, these networking tools shall be used to fluently simulate any kind of malicious or fraudulent activity on the network devices and testing the components that are designed & structured to mitigate ad detect the malicious activities, in a highly customizable and reliable way. The prediction and accuracy of performed results particularly depends on the reliability and performance of the used network traffic generator. So, here we will simulate & investigate the accuracy and performance of different network traffic tools which are most reviewed network traffic generators, namely Wireshark, Ostinato, Genesids and Cisco Trex. Most importantly, this analysis helps to examine & test the limitations and strengths of these networking tools, for any kind of bogus and malicious traffic. After the Analysis of this traffic, we will visualize the data and work with data sets trace files using to generate graphs using deep learning ANN algorithm to predict the accuracy of our analysis done for people to choose the best way of avoiding any kind of malicious activity in our network

Keyphrases: Attacks, Cyber Security, deep learning, network traffic analysis, Networking, Virtual Machines

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:7115,
  author = {Siddhant Khurana and Satwik Dash and Navneet Kumar Shiva and Rajat Vashist},
  title = {Malware Detection Using Network Traffic Analysis and Predicting Accuracy Using Deep Learning Algorithms},
  howpublished = {EasyChair Preprint no. 7115},

  year = {EasyChair, 2021}}
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