Abstract
With the advancement in technology and upsurge in network devices, more and more devices are getting connected to the network leading to more data and information on the network which emphasizes the security of the network to be of paramount importance. Malicious traffic must be detected in networks and machine learning or more precisely deep learning (DL), which is an upcoming approach, should be used for better detection. In this paper, Detection of attacks through a classification of traffic into normal and attack data is done using 1D-CNN, a special variant of convolutional neural network (CNN). For this, the CICIDS2017 dataset consisting of 14 attack types spread across 8 different files, is considered for evaluating model performance and various indicators like recall, precision, F1-score have been utilized. Separate 1D-CNN based DL models were built on individual sub-datasets as well as on combined datasets. Also, an evaluation of the model is done by comparing it with an artificial neural network (ANN) model. Experimental results have demonstrated that the proposed model has performed better and shown great capability in detecting network attacks as the majority of the class labels had achieved excellent scores in each of the evaluation indicators used.
Highlights
The Internet has become a major aspect in today’s society with people using the services of WWW for most of their dayto-day activities
In [26], authors utilized the 1D-convolutional neural network (CNN) based model for intrusion detection further evaluated using the NSL-KDD dataset. They compared the performance of their proposed model with different Machine learning (ML)/deep learning (DL) techniques like J48, naïve Bayes (NB), random forest (RF), MLP, and RNN
As we deal with different files the architecture of these separate models is uniform/identical albeit with minor changes. It consists of an input layer sequentially connected to 2 or 3 CNN layers intermixed by dropout and followed by flatten layer which further connects to a fully connected (FC) or dense layer and output layer
Summary
The Internet has become a major aspect in today’s society with people using the services of WWW for most of their dayto-day activities. The architecture of 1D-CNN and 2D CNN remains the same with the main difference between the two is the use of 1d array or tensor in the former and 2d matrix or tensor in the latter This means both input data and the kernel used for convolution are in 1d array form and the kernel moves over input in 1d direction. These minor but strategic changes led to certain advantages of 1D-CNN over 2D-CNN like 1) Reduced computational complexity due to 1D tensor over 2D tensor, 2) Well suited for low-cost applications but can be used for complex problems [8].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
