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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.

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.

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.

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.

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

Maximizing network lifetime is the main goal of designing a wireless sensor network. Clustering and routing can effectively balance network energy consumption and prolong network lifetime. This paper presents a novel cluster-based routing protocol called EECRAIFA. In order to select the optimal cluster heads, Self-Organizing Map neural network is used to perform preliminary clustering on the network nodes, and then the relative reasonable level of the cluster, the cluster head energy, the average distance within the cluster and other factors are introduced into the firefly algorithm (FA) to optimize the network clustering. In addition, the concept of decision domain is introduced into the FA to further disperse cluster heads and form reasonable clusters. In the inter-cluster routing stage, the inter-cluster routing is established by an improved ant colony optimization (ACO). Considering factors such as the angle, distance and energy of the node, the heuristic function is improved to make the selection of the next hop more targeted. In addition, the coefficient of variation in statistics is introduced into the process of updating pheromones, and the path is optimized by combining energy and distance. In order to further improve the network throughput, a polling control mechanism based on busy/idle nodes is introduced during the intra-cluster communication phase. The simulation experiment results prove that under different application scenarios, EECRAIFA can effectively balance the network energy consumption, extend the network lifetime, and improve network throughput.

The present work introduces a hybrid integration of the self-organizing map and the hidden Markov model (HMM) for anomaly detection in 802.11 wireless networks. The self-organizing hidden Markov model map (SOHMMM) deals with the spatial connections of HMMs, along with the inherent temporal dependencies of data sequences. In essence, an HMM is associated with each neuron of the SOHMMM lattice. In this paper, the SOHMMM algorithm is employed for anomaly detection in 802.11 wireless access point usage data. Furthermore, we extend the SOHMMM online gradient descent unsupervised learning algorithm for multivariate Gaussian emissions. The experimental analysis uses two types of data: synthetic data to investigate the accuracy and convergence of the SOHMMM algorithm and wireless simulation data to verify the significance and efficiency of the algorithm in anomaly detection. The sensitivity and specificity of the SOHMMM algorithm in anomaly detection are compared to two other approaches, namely HMM initialized with universal background model (HMM-UBM) and SOHMMM with zero neighborhood (Z-SOHMMM). The results from the wireless simulation experiments show that SOHMMM outperformed the aforementioned approaches in all the presented anomalous scenarios.

Paleochannels are crucial reservoir types in continental oil and gas fields. The P oil layer in the Sanzhao Depression of the Songliao Basin is a typical example of river-controlled delta-phase deposition. The underwater diversion channel is highly developed and serves as the primary unit of the oil and gas reservoir. The stacking of the river channel creates a complex relationship. Identifying river channels has always been a challenging aspect of predicting sweet spot reservoirs due to the thinness of the sand, fault cutting, seismic acquisition and processing noise, low-resolution constraints, and other factors. This paper proposes a method for identifying river channels by combining principal component analysis (PCA) and self-organised mapping network (SOM) with seismic attribute clustering. The aim is to address the issue of difficult identification of paleochannels. The seismic attribute set, which contains multiple types of attributes, is first dimensionality reduced using principal component analysis. Then, a few principal components that can characterise the attribute set are extracted and used as inputs to the self-organising map (SOM). This reduces the dimensionality of the training sample set and simplifies the network structure. The self-organising neural network structure is designed based on the input sample features. Training is then carried out, and the network weights are updated. Attribute clustering is performed to obtain the river identification results. The effectiveness of this method for paleochannel recognition is verified by the clear morphology of the recognised paleochannels in the Songliao Basin, their conformity to geological understanding, and their matching with drilling information.

Summer (June–August) heat waves in North China are found to be either primarily dry or moist, based on surface meteorological observations. This study characterizes synoptic circulation patterns (i.e., 500 hPa geopotential height) using the self-organizing map (SOM) method and investigates the influence of synoptic circulation patterns on these two types of heat waves. Results show that regional dry and moist heat waves are associated with different circulation patterns, which significantly modulate the advection of water vapor within the low-level atmosphere, and soil moisture and evaporation conditions at the surface. Regional dry heat waves are associated with times when a continental high pressure ridge is situated to the northwest of North China, and when the northern edge of the western North Pacific subtropical high (WNPSH) is south of 30° N. Regional moist heat waves are associated with a northward shift of the WNPSH. Long term variations of dry and moist heat wave occurrences correlated significantly with the occurrences of their associated circulation patterns at 0.38 (p = 0.02) and 0.71 (p = 0.00), respectively. On sub-seasonal time scales, the dominant heat wave type transforms from dry in June to moist in late July, which is in accordance with summer north–south WNPSH shifts. In addition, training the SOM with absolute geopotential height results in representative circulation patterns that are closely related to surface heat wave conditions in North China rather than the anomaly field, which mixes different circulation regimes.

The impacts of global warming/climate change on extreme rainfall events during the Baiu period in Japan and their dependency on weather patterns (WPs) were examined using the self-organizing map (SOM) method. To investigate the differences in climate change impacts on daily rainfall among the WPs, a SOM was applied to surface atmospheric circulation data from the Database for Policy Decision Making for Future Climate Change (d4PDF) to determine the dominant heavy rainfall WPs. The obtained SOM shows that different WPs are associated with regional variations in extreme rainfall events. Projected future changes in the occurrence of heavy rainfall displayed a non-uniform spatial distribution. To understand the spatial heterogeneous rainfall changes, the sensitivity of heavy rainfall WPs to climate forcing was evaluated. Results of the SOM analysis suggest that this regional variation in the future changes in extreme rainfall events could be attributed to sensitivity differences between WPs to the climate changes. These sensitivity differences can be attributed to the non-uniform spatial changes in the large-scale climatological background state in East Asia via modulations in the moist air intrusion into Japan.