  Despite the great developments in information technology, particularly the
Internet, computer networks, global information exchange, and its positive
impact in all areas of daily life, it has also contributed to the development
of penetration and intrusion which forms a high risk to the security of
information organizations, government agencies, and causes large economic
losses. There are many techniques designed for protection such as firewall and
intrusion detection systems (IDS). IDS is a set of software and/or hardware
techniques used to detect hacker's activities in computer systems. Two types of
anomalies are used in IDS to detect intrusive activities different from normal
user behavior. Misuse relies on the knowledge base that contains all known
attack techniques and intrusion is discovered through research in this
knowledge base. Artificial intelligence techniques have been introduced to
improve the performance of these systems. The importance of IDS is to identify
unauthorized access attempting to compromise confidentiality, integrity or
availability of the computer network. This paper investigates the Intrusion
Detection (ID) problem using three machine learning algorithms namely, BayesNet
algorithm, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). The
algorithms are applied on a real, Management Information Based (MIB) dataset
that is collected from real life environment. To enhance the detection process
accuracy, a set of feature selection approaches is used; Infogain (IG), ReleifF
(RF), and Genetic Search (GS). Our experiments show that the three feature
selection methods have enhanced the classification performance. GS with
bayesNet, MLP and SVM give high accuracy rates, more specifically the BayesNet
with the GS accuracy rate is 99.9%.
