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The enormous growth in computer networks and in Internet usage in recent years, combined with the growth in the amount of data exchanged over networks, have shown an exponential increase in the amount of malicious and mysterious threats to computer networks. Among many security issues, network attack is a major one. For example, Denial of Service (DoS) flooding attacks have recently become attractive to attackers, and these have posed devastating threats to network services. Therefore, the intrusion detection and network anomalies become very critical tasks in the field of network security research area. Researchers suffer from the lack of real-life datasets. Most of the datasets in hand depend on simulated-based approaches, which cannot represent the exact and the nature of network intrusion and anomaly scenarios. Hence, generating realistic datasets is very important as it allows for accurate and appropriate evaluation of the detection techniques. To overcome such shortcoming of the existing datasets, in this paper, we identify the important requirements to generate effective dataset and we also identify important attack scenarios and the method of injecting them in such data. Our systematic approach involves the investigation of Simple Network Management Protocol (SNMP) for network anomaly detection. For that, we present a Management Information Base (MIB) based mechanism capturing realistic SNMP-MIB statistical data. Then we use this data from an SNMP agent by means of real-life experiments involving six types of DoS attacks and Brute Force attack. Our dataset consists of 4998 records, where each record consists of …