Abstract: The worldwide reach and density of information have also raised the risk of integrity and confidentiality. Security lapses are become far too common. Thus, the enhancement of network security is emphasized these days. Network protection lets in unintentional interference of some kind and helps to prevent it. It is made up of network intrusion detection software that follows the network. To trace traffic within the network from source to destination apps, NIDS is strategically placed within the network. The device would efficiently filter all incoming and outgoing traffic, but this would lead to congestion, which would slow down the system's speed as a whole. Lastly, these techniques incorporate machine learning algorithms that provide dependable performance and flexibility for the gadget. Machine learning techniques can be used to identify patterns in the auditing data that indicate the difference between malicious and normal activity. This is because intrusion activities leave evidence behind.

Keywords: Intrusion Detection, Machine learning, Deep learning, HTML, Phishing Technique.

| DOI: 10.17148/IARJSET.2024.11625