Techniques Involved In The Development of The New Dataset For Anomaly Detection In Computer Networks

Computer networks data is overgrowing in nowadays world. More and more traffic is generated from the technology revolution (Cloud Computing, IoT, etc.) and the global situation of increased network traffic caused by the COVID19 pandemic. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. Machine learning is used in many different areas where it helps to predict future events and forecasts. Thus it would help detect anomalous data traf-fic in computer networks if machine learning algorithms are applied for a correctly created dataset. In this paper, we evaluate techniques that are involved in developing data to detect anomalies in computer networks with machine learning algorithms.