Unraveling Network Attacks with Machine Learning and Explainable AI
Deepak Kumar K, Saravana Gokul G, Anandhi Sivaramalingam, Senthil Pandi S · 2024
In today's interconnected world, network security is becoming increasingly critical, driving a significant amount of research and study. Intrusion detection systems (IDSs) are an essential component of network security, and machine learning algorithms can be used to detect and stop network assaults, improving network security. However, while machine learning models are extremely useful in recognizing and preventing attacks, they do not provide a thorough explanation of why an attack is classified as such or why it is not. To address this limitation, we have leveraged the robust NSL-KDD dataset and utilized the supervised learning algorithm random forests to train a model to detect various networking attacks. One of the key features of the random forest algorithm is that it can handle large datasets with continuous variables, making it ideal for network security applications. To further aid users in comprehending the model easily, we have implemented Explainable Artificial Intelligence (XAI). XAI helps users of varying levels understand and interpret data more easily. The suggested methodology employs XAI to achieve its goal of improving the understanding of machine learning models, which are often viewed as “black boxes.” By integrating XAI into our model, we can provide users with a clear understanding of how the model works and how it arrived at its conclusions. This transparency not only improves user trust but also enables them to identify any biases or errors in the model's algorithm, ensuring fairness and transparency. Overall, the proposed methodology represents a significant step forward in the field of network security. By combining the power of machine learning algorithms with the interpretability of XAI, we can provide a robust and transparent approach to intrusion detection, improving network security in the process. With the ever-increasing importance of network security, this methodology will undoubtedly play a critical role in protecting systems and data in the years to come.