Software-Defined Networking (SDN) provides an outlook to detect network security issues effectually and problems that contribute to the emergence of programmable features. Networks have increased influences on modern life and play a vital role in research.

The Intrusion Detection System (IDS) was designed to recognize the outbreak of attacks and apprise the network administrator to provide network security. It is a hypothesized model to detect network traffic as an attack or routine.

The ML-based IDS for SDN provide several domain knowledge about anomaly-based, misuse-based, host-based and network-based IDS mechanism covering log-data analysis, flow rules matching, expert-system development, pattern matching, packet inspection, session data analysis and time-series-based subdomains.

Recently, ML approaches have been implemented in SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and overcome network security problems. NIDS developed quickly in response to increasing cyber-attacks and global contract risks to commercial enterprises and the government sector.

NIDS are designed to detect malicious activities, including viruses, worms and DDOS attacks. NIDS traces the position and time of intrusion according to its IP addresses and timestamps and detects all needed attacks in real-time. The critical success factors for NIDS are an abnormality in,

 •  Detection speed
 •  Accuracy
 •  Reliability

Therefore, ML is applied in SDN NIDS to improve its detection accuracy and low false alarm rate. It is a passive-based or inline detection technique for detecting the target network and host levels. The NIDS-based SDN shows some advantages in terms of,

 •  Security enforcement
 •  Virtual management
 •  Quality of service
 •  Low-cost usage
 •  Quicker in response
 •  No need to maintain sensor programming at host levels.