Publications by authors named "Christian D Jensen"
Article Synopsis
- Noise alerts in intrusion detection systems can overwhelm analysts and disrupt operations, often triggered by routine software tasks or user activities.
- This paper introduces a method to minimize these noise alerts by identifying frequent alerts based on their occurrence rates, suggesting that simpler algorithms can effectively address the problem.
- The proposed Apriori-based approach demonstrated a significant reduction in noise alerts, filtering over 40% of alerts with a minimum occurrence threshold of 70%, and over 90% for certain alert categories based on real customer data from a Danish NDR solution.
The amount of data generated in today's world has a fair share of personal information about individuals that helps data owners and data processors in providing them with personalized services. Different legal and regulatory obligations apply to all data owners collecting personal information, specifying they use it only for the agreed-upon purposes and in a transparent way to preserve privacy. However, it is difficult to achieve this in large-scale and distributed infrastructures as data is continuously changing its form, such as through aggregation with other sources or the generation of new transformed resources, resulting often in the loss or misinterpretation of the .
View Article and Find Full Text PDF