Artificial Intelligence based Intrusion Detection System – A Detailed Survey

Abstract

The Internet and communications have rapidly expanded, leading to a significant rise in data generation and heterogeneity. Intrusion detection systems play a crucial role in ensuring the security and integrity of computer systems. These systems have been developed by researchers, academicians, and practitioners to effectively detect and mitigate network attacks. Intrusion detection systems are designed to analyze network traffic and compare it with a baseline of normal behavior, allowing them to identify any deviations or inconsistencies that may indicate an intrusion. Furthermore, the cooperative and distributed architecture of intrusion detection systems enables them to effectively detect attacks and protect the network from unauthorized access. Additionally, to enhance the performance of intrusion detection systems, techniques such as resampling the dataset and utilizing classifier ensemble are used to improve the classification accuracy. Moreover, intrusion detection systems have been integrated with intrusion response systems to ensure a timely and effective response to detected attacks. AI-based Intrusion Detection Systems have emerged as a crucial tool in ensuring network security and combating cyber threats. These systems utilize artificial intelligence algorithms to analyze network traffic, identify patterns of malicious activity, and detect potential cyber-attacks. They have proven to be highly effective in improving the detection accuracy, reducing false alarms, and even detecting previously unknown types of attacks. In summary, the development of accurate and efficient intrusion detection systems is crucial for ensuring network security. In today’s rapidly changing world, the significance of accurate intrusion detection systems cannot be overstated.

Keywords

• machine learning
• deep learning
• feature selection
• feature extraction
• deep neural network