A MapReduce-based Deep Belief Network for Intrusion Detection
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Pulchowk Campus
Abstract
The network security is being a challenging task in this modern era of IT with
increasing number of hacking tools, complexity of networks and network security
threats. The efficient and reliable intrusion detection system is an essential need in
recent growing digital world. Deep Belief Networks (DBNs) with Restricted
Boltzmann Machines (RBMs) as the building block have recently attracted wide
attention to the field of Network Intrusion Detection System (NIDS) due to their great
performance. DBN consists of two phase of training: first is pre-training of stack of
RBMs followed by fine-tuning using backpropagation However, the sequential
implementation of DBN is computationally very time consuming to process large
amount of data sets. So, this thesis research is focused on implementing a
MapReduce-based Deep Belief Network (MRDBN) for distributed computation for
efficient NIDS using Hadoop ecosystem. The performance of system has been
evaluated using two different datasets: UNSW-NB15 and NSL-KDD. First, the dataset
is preprocessed to convert all attributes into numerical values and normalized it. Then, the distributed DBN based on MapReduce framework is implemented and evaluated
in preprocessed datasets. The scalability test of system showed that training time for
10 nodes cluster sized MRDBN for intrusion detection is 2.2 times faster than 4 nodes
cluster sized MRDBN. The overall accuracy of the MRDBN intrusion detection
system for multiclass classification is 82.61% with 3.9% false alarm rate for UNSW-
NB15 dataset. The system is found to be more precise than existing Artificial Neural
Network (ANN) and Support Vector Machine (SVM) in the field of intrusion
detection.
Description
The network security is being a challenging task in this modern era of IT with
increasing number of hacking tools, complexity of networks and network security
threats.