Search Results for « Anomaly detection »

Intrusion detection systems (IDS) play a critical role in network security by monitoring systems and network traffic to detect anomalies and attacks. This study explores the different types of IDS, including host-based and network-based, along with their deployment scenarios. A key focus is on incorporating anomaly detection techniques within IDS to identify novel and unknown threats that evade signature-based methods. Statistical approaches like outlier detection and machine learning techniques like neural networks are discussed for building effective anomaly detection models. Data collection and preprocessing techniques, including feature engineering, are examined. Both unsupervised techniques like clustering and density estimation and supervised methods like classification are covered. Evaluation datasets and performance metrics for assessing anomaly detection models are highlighted. Challenges like curse of dimensionality and concept drift are outlined. Emerging trends include integrating deep learning and explainable AI into anomaly detection. Overall, this comprehensive study examines the role of anomaly detection within IDS, delves into various techniques and algorithms, surveys evaluation practices, discusses limitations and challenges, and provides insights into future research directions to advance network security through improved anomaly detection capabilities.

In this paper, we presented an approach to deploying a student–teacher feature pyramid model (STFPM) for anomaly detection metal-textile gas filters used in automotive exhaust gas filtering at GKD-Gebr. Kufferath AG. As the customer requires 100% quality of the delivered parts, an optical inspection process of every produced filter is required. This is very demanding for the human inspection worker as she has to inspect many 100 parts in an 8 hours shift. On the other hand, a fully vision-based system is not able to achieve the required classification rates either. Therefore, we propose a one-class anomaly detection process for the gas filters where human and AI work together in achieving the 100% pass rate. The STFPM model deals with the large amount of clearly true positive cases and automatedly classified them as PASS. Only cases of doubt where an anomaly has been detected are inspected by the human inspector. This way, the work load of the inspection worker is reduced, and, on the other hand, the hard to meet case of no mis-classification of the AI system can be avoided. We show the network architecture and the integration into the quality inspection process of the company GKD.