Abstract:
The security issue is essential and more challenging in Mobile Ad-Hoc Network
(MANET) due to its characteristics such as, node mobility, self-organizing capability and
dynamic topology. MANET is vulnerable to different types of attacks. One of possible attacks
is black hole attack. Black hole attack occurs when a malicious node joins the network with the
aim of intercepting data packets which are exchanged across the network and dropping them
which affects the performance of the network and its connectivity. This paper proposes a new
dataset (BDD dataset) for black hole intrusion detection systems which contributes to detect the
black hole nodes in MANET. The proposed dataset contains a set of essential features to build
an efficient learning model where these features are selected carefully using one of the feature
selection techniques which is information gain technique J48 decision tree, Na?ve Bayes (NB)
and Sequential Minimal Optimization (SMO) classifiers are learned using training data of BDD
dataset and the performance of these classifiers is evaluated using a learning machine tool
Weka 3.7.11. The obtained performance results indicate that using the proposed dataset features
succeeded in build an efficient learning model to train the previous classifiers to detect the
black hole attack.