A Multi-Class Intrusion Detection System Based on Continual Learning

With the proliferation of smart devices, network security has become crucial to protect systems and data. In order to identify and categorise different network threats, this study introduces a flow-based Network Intrusion Detection System (NIDS) based on continual learning with a CNN backbone. Using the LYCOS-IDS2017 dataset, the study explores several continuous learning techniques for identifying threats including denial-of-service and SQL injection. Unlike previous approaches, this work treats intrusion detection as a multi-class classification problem, rather than anomaly detection. The findings show how continuously learning models may identify network intrusions with high recall rates and accuracy while generating few false alarms. This study contributes to the development of an adaptive NIDS that can handle attack classification simultaneously with detection, and that can be trained online without periodic offline training. Additionally, utilising the improved version of the dataset adds value to the research on LYCOS-IDS2017 by presenting results for untested models

Exploring the Bacterial Communities of the Kaiafas Thermal Spring Anigrides Nymphes in Greece Prior to Rehabilitation Actions

Anigrides Nymphes of Lake Kaiafas is a thermal spring that is well known for its therapeutical properties, as the hot water (32–34 °C) is rich in sulfur compounds and minerals. Nowadays, efforts are made from the Hellenic Republic to modernize the existing facilities and infrastructure networks of the area. To study the complex ecosystem of the thermal spring, we collected water from four sampling points (Lake, and Caves 1, 2, and 3). Filtration method was used for microbial enumeration. In parallel, total bacterial DNA was extracted and subjected to next-generation sequencing (NGS). A total of 166 different bacterial families were detected. Differences in families, genera, and species abundances were detected between the different sampling points. Specifically, Comamonadaceae was the most common family detected in Lake and Cave 3. Similarly, in Caves 1 and 2, Rhodobacteraceae was detected at a higher percentage compared to the rest of the families. Moreover, the detection of sequences assigned to waterborne or opportunistic pathogens, i.e., Enterobacteriaceae, Legionellaceae, Coxiellaceae, and Clostridiaceae, as well as Enterococcus and Vibrio, is of great importance. Although the presence of pathogens was not examined by quantitative PCR, the detection of their sequences strengthens the need of the planned rehabilitation actions of this natural environment in order to allow human swimming