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This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions.

Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency. Plain Language Summary Winter storms can have devastating socioeconomic impacts across the United States. Climate models are used to understand and predict these winter storms and how they might change in the future, therefore helping to inform climate policy and emergency services. It is essential that we evaluate these climate models so that their performance and accuracy can be optimized. One way of evaluating climate models is to assess their ability in reproducing the large‐scale atmospheric conditions which occur during extreme events. Here we apply a cyclone tracking algorithm and self‐organizing maps, a machine‐learning approach, to automate the process of identifying key patterns associated with historical winter storm activity. We then use this approach to assess if CMIP6 climate models are able to reproduce the same patterns including the winter storm frequency and intensity. Our results show that, regardless of resolution, most CMIP6 models struggle to simulate the more extreme patterns and tend to favor weaker patterns that are not as conducive to cyclone formation. As a result, most models simulate too few winter storms, however, the error in storm intensity is more varied, with some models producing stronger storms and others producing weaker storms on average. Key Points Despite resolution, CMIP6 models struggle to reproduce amplified synoptic patterns over the US, with a robust bias toward zonal patterns Most CMIP6 models produce too few cyclones over the northeastern US, while model errors are more varied when considering cyclone intensity Increased resolution increases the number and intensity of simulated extratropical cyclones but does not improve model representation of synoptic patterns

In this paper, we have proposed a fusion of two architectures, self-organizing map and granular self-organizing map (SOM + GSOM), for solving the microblog summarization task where a set of relevant tweets are extracted from the available set of tweets. SOM is used to reduce the available set of tweets to a smaller subset, and GSOM is used for extracting relevant tweets. The fusion of SOM + SOM is also accomplished to illustrate the effectiveness of GSOM over SOM in the second architecture. Moreover, only SOM version is also utilized to illustrate the potentiality of fusion in our proposed approaches. As similarity/dissimilarity measures play major role in any summarization system; therefore, to measure the same between tweets, various measures like word mover distance, cosine distance and Euclidean distance are also explored. The results obtained are evaluated on four datasets related to disaster events using ROUGE measures. Experimental results demonstrate that our best-proposed approach (SOM + GSOM) has obtained 17 % and 5.9 % improvements in terms of ROUGE-2 and ROUGE-L scores, respectively, over the existing techniques. The results are also validated using statistical significance t -test.

The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.

In this study, the Self‐Organizing Maps in combination with K‐means clustering technique are used for classification of synoptic weather patterns inducing heavy rainfall exceeding 100 mm day−1 during the Baiu season (June–July) of 1979–2010 over northern Kyushu, southwestern Japan. It suggests that these local extreme rainfall events are attributed to four clustered patterns, which are primarily related to the Baiu front and the extratropical/tropical cyclone/depression activities and represented by the intrusion of warm and moist air accompanied by the low‐level jet or cyclonic circulation. The classification results are then implemented with the analogue method to predict the occurrence (yes/no) of local heavy rainfall days in June–July of 2011–2016 by using the prognostic synoptic fields from the operational Japan Meteorological Agency (JMA) Global Spectral Model (GSM). In general, the predictability of our approach evaluated by the Equitable Threat Score up to 7‐day lead times is significantly improved than that from the conventional method using only the predicted rainfall intensity from GSM. Although the false alarm ratio is still high, it is expected that the new method will provide a useful guidance, particularly for ranges longer than 2 days, for decision‐making and preparation by weather forecasters or end‐users engaging in disaster‐proofing and water management activities. The self‐organizing maps (SOM) is used for classification and forecast of synoptic weather patterns inducing heavy rainfall exceeding 100 mm day−1 during the Baiu season (June–July) over northern Kyushu, Japan. The predictability of SOM is significantly improved than that from the traditional method using only the predicted rainfall intensity from the medium‐range operational model (GSM). It is expected that the new method will provide useful guidance, particularly for ranges longer than 2 days, for decision‐making by weather forecasters and local end‐users.

The identification of anomalous activities is a challenging and crucially important task in sensor networks. This task is becoming increasingly complex with the increasing volume of data generated in real-world domains, and greatly benefits from the use of predictive models to identify anomalies in real time. A key use case for this task is the identification of misbehavior that may be caused by involuntary faults or deliberate actions. However, currently adopted anomaly detection methods are often affected by limitations such as the inability to analyze large-scale data, a reduced effectiveness when data presents multiple densities, a strong dependence on user-defined threshold configurations, and a lack of explainability in the extracted predictions. In this paper, we propose a distributed deep learning method that extends growing hierarchical self-organizing maps, originally designed for clustering tasks, to address anomaly detection tasks. The SOM-based modeling capabilities of the method enable the analysis of data with multiple densities, by exploiting multiple SOMs organized as a hierarchy. Our map-reduce implementation under Apache Spark allows the method to process and analyze large-scale sensor network data. An automatic threshold-tuning strategy reduces user efforts and increases the robustness of the method with respect to noisy instances. Moreover, an explainability component resorting to instance-based feature ranking emphasizes the most salient features influencing the decisions of the anomaly detection model, supporting users in their understanding of raised alerts. Experiments are conducted on five real-world sensor network datasets, including wind and photovoltaic energy production, vehicular traffic, and pedestrian flows. Our results show that the proposed method outperforms state-of-the-art anomaly detection competitors. Furthermore, a scalability analysis reveals that the method is able to scale linearly as the data volume presented increases, leveraging multiple worker nodes in a distributed computing setting. Qualitative analyses on the level of anomalous pollen in the air further emphasize the effectiveness of our proposed method, and its potential in determining the level of danger in raised alerts.

When analyzing high-dimensional data with many elements, a data visualization that maps the data onto a low-dimensional space is often performed. By visualizing the data, humans can intuitively understand the structure of the data in the high-dimensional space. The self-organizing map (SOM) is one such data visualization method. We propose a spherical tree-structured SOM (S-TS-SOM), which speeds up the search for winner nodes and eliminates the unevenness of learning due to the position of the winner nodes by placing the nodes on a sphere and applying the tree search method. In this paper, we confirm that the S-TS-SOM can achieve the same results as a normal spherical SOM while reducing the learning time. In addition, we confirm the granularity of clustering on the tree structure of the S-TS-SOM.

Riverine flooding in the New England region of the United States of America is devastating, arises from multiple processes during any season, and lacks ties to common climate indices. Here the connection between large‐scale atmospheric patterns and surface conditions prior to and during the occurrence of riverine peak flow events in the heavily‐populated, flood‐vulnerable region of New England is explored. Understanding the mechanisms governing peak‐flows improves the near‐ and long‐term forecasts of hydroclimatic extremes as well as provides supplemental process‐level knowledge for regional water resource planning and emergency response. Through the application of self‐organizing maps, several distinct meteorological and hydrological patterns associated with river discharge events in New England are identified. Using case‐studies of major floods in July and December of 2023, we demonstrate that this methodology provides a mechanistic foundation for understanding the drivers of New England floods and how they might change in a future climate.

Groundwater is a dynamic component of the global water cycle with important social, economic, ecological, and Earth system functions. We present a new global classification and mapping of groundwater systems, which we call groundwaterscapes, that represent predominant configurations of large‐scale groundwater system functions. We identify and map 15 groundwaterscapes which offer a new lens to conceptualize, study, model, and manage groundwater. Groundwaterscapes are derived using a novel application of sequenced self‐organizing maps that capture patterns in groundwater system functions at the grid cell level (∼10 km), including groundwater‐dependent ecosystem type and density, storage capacity, irrigation, safe drinking water access, and national governance. All large aquifer systems of the world are characterized by multiple groundwaterscapes, highlighting the pitfalls of treating these groundwater bodies as lumped systems in global assessments. We evaluate the distribution of Global Groundwater Monitoring Network wells across groundwaterscapes and find that industrial agricultural regions are disproportionately monitored, while several groundwaterscapes have next to no monitoring wells. This disparity undermines the ability to understand system dynamics across the full range of settings that characterize groundwater systems globally. We argue that groundwaterscapes offer a conceptual and spatial tool to guide model development, hypothesis testing, and future data collection initiatives to better understand groundwater's embeddedness within social‐ecological systems at the global scale. Key Points Groundwaterscapes are presented as landscape units representing configurations of groundwater's social‐ecological and Earth system functions A two‐stage self‐organizing map clustering method is implemented to derive 15 groundwaterscapes at the global scale All large aquifer systems of the world contain multiple groundwaterscapes

The main features of daily extreme precipitation and circulation types in southern South America (SSA) were evaluated and compared in both multiple observational datasets (rain gauges, CHIRPS, CPC and MSWEP) and simulations from four regional climate models (RCMs) driven by ERA-Interim during 1980–2010. The inter-comparison of extreme events, characterised in terms of their intensity, frequency and spatial coverage, varied across SSA showing large differences among observational datasets and RCMs and reflecting the current observational uncertainty when evaluating precipitation extremes at a daily scale. The spread between observational datasets was smaller than for the RCMs. Most of the RCMs successfully captured the spatial pattern of extreme precipitation across SSA, although RCA4 (REMO) usually underestimated (overestimated) precipitation intensities, particularly the maximum amounts in southeastern South America (SESA), where the extremes are remarkable. The synoptic circulation was described by a classification of circulation types (CTs) using Self-Organizing Maps (SOM). Specific CTs were found to significantly enhance the occurrence of extreme precipitation events in sectorized areas of SESA. The RCMs adequately reproduced the SOM node frequencies, although they tended to simplify the predominant CTs into a more reduced number of configurations. They appropriately represented the extreme precipitation frequencies conditioned by each CT, exhibiting some limitations in the location and intensity of the resulting precipitation systems. These sorts of evaluations contribute to a better understanding of the physical mechanisms responsible for extreme precipitation and of their future projections in a climate change scenario.