Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns

Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self-Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post-processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two-dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41-years time series of 7 crime rate attributes in the states of the USA

GalSOM - Colour-Based Image Browsing and Retrieval with Tree-Structured Self-Organising Maps

This paper describes an image browsing and retrieval application called GalSOM. Bitmap images are described by their colour histograms and sorted using an improved variant of the tree-structured self-organising map (TS-SOM) algorithm. The advantages of using such a system are discussed in detail, and their application to the problem of image theft detection is proposed

Efficient Computer Forensic Analysis Using Machine Learning Approaches

In this digital era, the number of Cybercrimes is increasing that has resulted in increased number of pending cybercrimes cases such as artifacts as a malware, hacking and cyber fraud or e-harassment. In order to deal with these cases, digital forensics must include the concrete law enforcement in the court of law. In digital forensics, it is challenging task to detect reliable evidence because of worldwide use and advancements in digital communication technologies. Common approaches such as file signature analysis and the data carving can be done using the forensics tools, however, digital evidence examiners are keen to find the relevant data which helps in finding the truth behind the case. To reduce the examination time in the data examination or analysis process, this paper explores the role of unsupervised pattern recognition to identify the notable artefact. The Self-Organising Map (SOM) is used to automatically cluster notable artefacts. In this work, four cases are presented to demonstrate the use of SOM in examining the digital data saved in a CSV format. Multiple SOMs are created including Extension Mismatch SOM that represents the intentional changes done on the default extension of the file in order to hide it from the forensic examiner. Other types of SOM are created for the EXIF Metadata (i.e. MAC attributes). USB Device Attached (Device Make, Device Model, Device ID, Date/Time, Source File, Tags)

A Method for Detecting Abnormal Program Behavior on Embedded Devices

A potential threat to embedded systems is the execution of unknown or malicious software capable of triggering harmful system behavior, aimed at theft of sensitive data or causing damage to the system. Commercial off-the-shelf embedded devices, such as embedded medical equipment, are more vulnerable as these type of products cannot be amended conventionally or have limited resources to implement protection mechanisms. In this paper, we present a self-organizing map (SOM)-based approach to enhance embedded system security by detecting abnormal program behavior. The proposed method extracts features derived from processor's program counter and cycles per instruction, and then utilises the features to identify abnormal behavior using the SOM. Results achieved in our experiment show that the proposed method can identify unknown program behaviors not included in the training set with over 98.4% accuracy

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats

Using Fuzzy Logic Algorithms and Growing Hierarchical Self-Organizing Maps to Define Efficient Security Inspection Strategies in a Container Terminal

Maritime transport is one of the oldest methods of moving various types of goods, and it continues to have an important role in our modern society. More than 20 million containers are transported across the oceans daily. However, this form of transportation is constantly threatened by illegal operations, such as the smuggling of goods or people and merchandise theft. Port security departments must be prepared to face the different threats and challenges that accompany the use of innovative techniques and devices to achieve efficient inspection strategies. Two inspection strategies are presented in this study. The first strategy is based on fuzzy logic (FL), and the second strategy is based on the growing hierarchical self-organizing map (GHSOM) approach. The weight variation and security index (SI) of a container and the readings from certain technologies, such as radio-frequency identification (RFID) and X-ray scanning, are considered as the input data. To minimize the inspection time and considering the costs associated with the security inspections of containers, the results of both inspection strategies are compared and analyzed. The findings indicate there is potential for improving the effectiveness of security inspections by employing both techniques, and the specific relevance in the case of GHSOMs is discussed.Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad - “Estrategias de diseño microelectronico para IOT en escenarios hostiles” TEC2016-80396-C2-2-

Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes

Searching in CCTV : effects of organisation in the multiplex

Acknowledgements The author wishes to thank Dr Kenneth Scott-Brown for his comments on an earlier version of this manuscript. Data were collected with the assistance of three groups of third-year undergraduate psychology students. Funding This study is not associated with any external fundingPeer reviewedPublisher PD

A review on deep learning techniques for 3D sensed data classification

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches including; RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.Comment: 25 pages, 9 figures. Review pape