Main Article Content
The need for cheaper and faster delivery in the electronics industry has increased as a result of information technology advancements. The quick development of technology not only makes life simpler but also raises several security concerns. The number of attacks conducted online has increased as the Internet has developed through time. One of the supporting layers that can be used for information security is the intrusion detection system (IDS). IDS offers a clean atmosphere for conducting business and steers clear of shady network activity. The security on the user's end of web transactions is the most difficult task in the construction of an e-commerce system. This study examined intrusion detection security techniques. Continuous monitoring of intrusion detection is required for further technological adaptation, and as a result, presents a comparative comparison of adaptive artificial intelligence-based intrusion detection algorithms. This work shows how reinforcement learning (RL) and regression learning-based intrusion detection systems (IDS) can be used to solve extremely difficult issues, such as choosing input features and taking limited resources into account.