  The exponential increase in the number of malicious threats on computer
networks and Internet services due to a large number of attacks makes the
network security at continuous risk. One of the most prevalent network attacks
that threaten networks is Denial of Service (DoS) flooding attack. DoS attacks
have recently become the most attractive type of attacks to attackers and these
have posed devastating threats to network services. So, there is a need for
effective approaches, which can efficiently detect any intrusion in the
network. This paper presents an efficient mechanism for network attacks
detection and types of attack classification using the Management Information
Base (MIB) database associated with the Simple Network Management Protocol
(SNMP) through machine learning techniques. This paper also investigates the
impact of SNMP-MIB data on network anomalies detection. Three classifiers,
namely, Random Forest, AdaboostM1 and MLP are used to build the detection
model. The use of different classifiers presents a comprehensive study on the
effectiveness of SNMP-MIB data in detecting different types of attack.
Empirical results show that the machine learning techniques were quite
successful in detecting and classifying the attacks with a high detection rate.
