TY - JOUR
T1 - An artificial deep neural network for the binary classification of network traffic
AU - Abdullah, Shubair A.
AU - Al-Ashoor, Ahmed
N1 - Publisher Copyright:
© 2013 The Science and Information (SAI) Organization.
PY - 2020
Y1 - 2020
N2 - Classifying network packets is crucial in intrusion detection. As intrusion detection systems are the primary defense of the infrastructure of networks, they need to adapt to the exponential increase in threats. Despite the fact that many machine learning techniques have been devised by researchers, this research area is still far from finding perfect systems with high malicious packet detection accuracy. Deep learning is a subset of machine learning and aims to mimic the workings of the human brain in processing data for use in decision-making. It has already shown excellent capabilities in dealing with many real-world problems such as facial recognition and intelligent transportation systems. This paper develops an artificial deep neural network to detect malicious packets in network traffic. The artificial deep neural network is built carefully and gradually to confirm the optimum number of input and output neurons and the learning mechanism inside hidden layers. The performance is analyzed by carrying out several experiments on real-world open source traffic datasets using well-known classification metrics. The experiments have shown promising results for real-world application in the binary classification of network traffic.
AB - Classifying network packets is crucial in intrusion detection. As intrusion detection systems are the primary defense of the infrastructure of networks, they need to adapt to the exponential increase in threats. Despite the fact that many machine learning techniques have been devised by researchers, this research area is still far from finding perfect systems with high malicious packet detection accuracy. Deep learning is a subset of machine learning and aims to mimic the workings of the human brain in processing data for use in decision-making. It has already shown excellent capabilities in dealing with many real-world problems such as facial recognition and intelligent transportation systems. This paper develops an artificial deep neural network to detect malicious packets in network traffic. The artificial deep neural network is built carefully and gradually to confirm the optimum number of input and output neurons and the learning mechanism inside hidden layers. The performance is analyzed by carrying out several experiments on real-world open source traffic datasets using well-known classification metrics. The experiments have shown promising results for real-world application in the binary classification of network traffic.
KW - ANN
KW - Binary classification
KW - Deep learning
KW - Malicious traffic classification
KW - Packet classification
UR - http://www.scopus.com/inward/record.url?scp=85080115086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85080115086&partnerID=8YFLogxK
U2 - 10.14569/ijacsa.2020.0110150
DO - 10.14569/ijacsa.2020.0110150
M3 - Article
AN - SCOPUS:85080115086
SN - 2158-107X
VL - 11
SP - 402
EP - 408
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 1
ER -