Title
Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance.
Abstract
Code-driven systems have extent to more than half of the world’s populations in ambient data and connectivity, offering formerly unimagined opportunities and unexpected threats. Evolutions in Artificial Intelligence (AI) are seen increasing day by day especially in industrial builds. The unconventional technique of AI in cyber-attacks seems to be quite daunting. The idea of a machine growing its own knowledge through self-learning becomes sophisticated to attack things is a fretful problem to the cyber world. Most of the time, these AI enabled cyber-attacks are performed using advanced malwares which incorporates advanced evasion techniques to evade security perimeters. Traditional cyber security methods fail to cope with these attacks. In order to address these issues, robust traffic classification system using Principal Component Analysis (PCA) and Artificial Neural Network (ANN) is proposed for providing extreme surveillance. Further, these proposed method aims to expose various AI based cyber-attacks with their present-day impact, and their fortune in the future. Simulation is carried out using a self-developed autonomous agent which learns by itself. Experimental results confirm that the proposed schemes are efficient to classify the attack traffic with 99% of accuracy when compared to the state of the art methods.
Year
DOI
Venue
2019
10.1016/j.comcom.2019.08.003
Computer Communications
Keywords
Field
DocType
AI driven cyber-attacks,Malwares,Principle component analysis,Surveillance,Artificial neural network
Traffic classification,Autonomous agent,Computer science,Computer network,Artificial intelligence,Artificial neural network,Malware,Principal component analysis,Machine learning
Journal
Volume
ISSN
Citations 
147
0140-3664
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
D. Arivudainambi1548.50
Varun Kumar K.A230.73
S. Sibi Chakkaravarthy3133.70
P. Visu400.34