  Cybersecurity is essential, and attacks are rapidly growing and getting more
challenging to detect. The traditional Firewall and Intrusion Detection system,
even though it is widely used and recommended but it fails to detect new
attacks, zero-day attacks, and traffic patterns that do not match with any
configured rules. Therefore, Machine Learning (ML) can be an efficient and
cost-reduced solution in cybersecurity.
  We used Netflow datasets to extract features after applying data analysis.
Then, a selection process has been applied to compare these features with one
another. Our experiments focus on how efficient machine learning algorithms can
detect Bot traffic, Malware traffic, and background traffic. We managed to get
0.903 precision value from a dataset that has 6.5% Bot flows, 1.57% Normal
flows, 0.18% Command&Control (C&C) flows, and 91.7% background flows, from
2,753,884 total flows. The results show low false-negative with few
false-positive detections.
