New Approach for Intrusion Detection in Big Data as a Service in the Cloud
Dounya Kassimi, Okba Kazar, Omar Boussaid, Abdelhak Merizig
Journal of Digital Information Management, Vol. 16, No. 5, 2018.
DOI: https://doi.org/10.6025/jdim/2018/16/5/258-270
Link: http://dline.info/fpaper/jdim/v16i5/jdimv16i5_5.pdf
Abstract:
Nowadays, Big Data has reached every area
of our lives because it covers many tasks in different operation. This new technique forces the cloud computing to use it as a layer, for this reason cloud technology embraces it as Big Data as a service (BDAAS). After solving the problem of storing huge volumes of information
circulating on the Internet, remains to us how we can
protect and ensure that this information are stored without
loss or distortion. The aim of this paper is to study the
problem of safety in BDAAS, inparticularly we will cover
the problem of Intrusion detection system (IDS). In order
to solve the problems tackled in this paper, we have
proposed a Self-Learning Autonomous Intrusion Detection
system (SLA-IDS) which is based on the architecture
ofautonomic system to detect the anomaly data. In this
approach, to add the autonomy aspect to the proposed
system we have used mobile and situated agents. The
implementation of this model has been provided to
evaluate our system. The obtained findings show the effectiveness of our proposed model. We validate our proposition using Hadoop as Big Data Platform and CloudSim, machine learning Weka with java to create Model of detection.