Intelligent False Alarm Research Articles

Measurement of optical fiber sensors for intrusion detection and warning systems fortified with intelligent false alarm suppression

Measurement of optical fiber sensors for intrusion detection and warning systems fortified with intelligent false alarm suppression

Adaptive machine learning‐based alarm reduction via edge computing for distributed intrusion detection systems

SummaryTo protect assets and resources from being hacked, intrusion detection systems are widely implemented in organizations around the world. However, false alarms are one challenging issue for such systems, which would significantly degrade the effectiveness of detection and greatly increase the burden of analysis. To solve this problem, building an intelligent false alarm filter using machine learning classifiers is considered as one promising solution, where an appropriate algorithm can be selected in an adaptive way in order to maintain the filtration accuracy. By means of cloud computing, the task of adaptive algorithm selection can be offloaded to the cloud, whereas it could cause communication delay and increase additional burden. In this work, motivated by the advent of edge computing, we propose a framework to improve the intelligent false alarm reduction for DIDS based on edge computing devices. Our framework can provide energy efficiency as the data can be processed at the edge for shorter response time. The evaluation results demonstrate that our framework can help reduce the workload for the central server and the delay as compared to the similar studies.

Enhancing False Alarm Reduction Using Voted Ensemble Selection in Intrusion Detection

Abstract Network intrusion detection systems (NIDSs) have become an indispensable component for the current network security infrastructure. However, a large number of alarms especially false alarms are a big problem for these systems which greatly lowers the effectiveness of NIDSs and causes heavier analysis workload. To address this problem, a lot of intelligent methods (e.g., machine learning algorithms) have been proposed to reduce the number of false alarms, but it is hard to determine which one is the best. We argue that the performance of different machine learning algorithms is very fluctuant with regard to distinct contexts (e.g., training data). In this paper, we propose an architecture of intelligent false alarm filter by employing a method of voted ensemble selection aiming to maintain the accuracy of false alarm reduction. In particular, there are four components in the architecture: data standardization, data storage, voted ensemble selection and alarm filtration. In the experiment, we condu...