Optimized deep neural network based vulnerability detection enabled secured testing for cloud SaaS
Rohith Vallabhaneni, Sanjaikanth E. Vadakkethil Somanathan Pillai, Srinivas A. Vaddadi, Santosh Reddy Addula, Bhuvanesh Ananthan
Abstract
Based on the information technology service model, an on-demand services towards user becomes cost effective, which is provided with cloud computing. The network attack is detected with research community that pays huge interest. The novel proposed framework is intended with the combination of mitigation and detection of attack. While enormous traffic is obtainable, extract the relevant fields decide with Software-as-a-service (SaaS) provider. According to the network vulnerability and mitigation procedure, perform deep learning-based attack detection model. The golf optimization algorithm (GOA) done the selection of features followed by deep neural network (DNN) detect the attacks from the selected features. The correntropy variational features validates the level of risk and performs vulnerability assessment. Perform the process of bait-oriented mitigation during the phase of attack mitigation. The proposed approach demonstrates 0.97kbps throughput with 0.2% packet loss ratio than traditional methods.
Keywords
Attack mitigation; Deep neural network; Features; Golf optimization algorithm; Vulnerability detection
DOI:
http://doi.org/10.11591/ijeecs.v36.i3.pp1950-1959
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).
IJEECS visitor statistics