8,663 research outputs found

    A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection

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    The broadening dependency and reliance that modern societies have on essential services provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just at the economic level but also in terms of physical damage and even loss of human life. Complementing traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are in place and compliant with standards and internal policies. Forensics assist the investigation of past security incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of tackling the requirements imposed by massively distributed and complex Industrial Automation and Control Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic template for a converged platform. These results are intended to guide future research on forensics and compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio

    Graduate Catalog of Studies, 2023-2024

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    Fractal feature selection model for enhancing high-dimensional biological problems

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    The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Efficient network management and security in 5G enabled internet of things using deep learning algorithms

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    The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model

    Deep Learning Algorithms in Industry 5.0: A Comprehensive Experimental Study

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    This extensive experimental research provides strong empirical proof of the revolutionary power of deep learning algorithms when integrated into Industry 5.0. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and Transformers are a few examples of deep learning algorithms that have shown remarkable accuracy rates of 92.3%, 88.7%, and 95.1%, respectively. Furthermore, the processing durations, which vary between 15 and 25 milliseconds, confirm their ability to make decisions in real time. The abundance of various data accessible in Industry 5.0 is highlighted by data collection sources such as picture databases (300 GB), text corpora (150 GB), equipment records (250 GB), and IoT sensor data (500 GB). The significant energy savings, shown by 20% reductions across a range of machine types, highlight the financial and ecological advantages of deep learning integration. Moreover, the noteworthy improvements in production quality, exhibiting up to 50% reductions in defect rates, highlight the potential of deep learning in quality assurance. These results provide tangible proof of the critical roles deep learning algorithms play in streamlining production lines, increasing energy economy, and boosting product quality in the ever-changing Industry 5.0 environment

    CNN-LSTM framework to automatically detect anomalies in farmland using aerial images from UAVs

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    Using aerial inspection techniques in farmlands can yield vital data instrumental in mitigating various impediments to optimizing farming practices. Farmland anomalies (standing water and clusters of weeds) can impede farming practices, leading to the improper utilization of farmland and the disruption of agricultural development. Utilizing Unmanned Aerial Vehicles (UAVs) for remote sensing is a highly effective method for obtaining extensive imagery of farmland. Visual data analytics in the context of automatic pattern recognition from collected data is valuable for advancing Deep Learning (DL) -assisted farming models. This approach shows significant potential in enhancing agricultural productivity by effectively capturing crop patterns and identifying anomalies in farmland. Furthermore, it offers prospective solutions to address the inherent barriers farmers encounter. This study introduces a novel framework, namely the hybrid Convolutional Neural Networks and Long Short-Term Memory (HCNN-LSTM), which aims to detect anomalies in farmland using images obtained from UAVs automatically. The system employs a Convolutional Neural Network (CNN) for deep feature extraction, while Long Short-Term Memory (LSTM) is utilized for the detection task, leveraging the extracted features. By integrating these two Deep Learning (DL) architectures, the system attains an extensive knowledge of farm conditions, facilitating the timely identification of irregularities such as the presence of water, clusters of weeds, nutrient deficit, and crop disease. The proposed methodology is trained and evaluated using the Agriculture-Vision challenge database. The results obtained from the experiment demonstrate that the proposed system has achieved a high level of accuracy, with a value of 99.7%, confirming the effectiveness of the proposed approach

    Online semi-supervised learning in non-stationary environments

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    Existing Data Stream Mining (DSM) algorithms assume the availability of labelled and balanced data, immediately or after some delay, to extract worthwhile knowledge from the continuous and rapid data streams. However, in many real-world applications such as Robotics, Weather Monitoring, Fraud Detection Systems, Cyber Security, and Computer Network Traffic Flow, an enormous amount of high-speed data is generated by Internet of Things sensors and real-time data on the Internet. Manual labelling of these data streams is not practical due to time consumption and the need for domain expertise. Another challenge is learning under Non-Stationary Environments (NSEs), which occurs due to changes in the data distributions in a set of input variables and/or class labels. The problem of Extreme Verification Latency (EVL) under NSEs is referred to as Initially Labelled Non-Stationary Environment (ILNSE). This is a challenging task because the learning algorithms have no access to the true class labels directly when the concept evolves. Several approaches exist that deal with NSE and EVL in isolation. However, few algorithms address both issues simultaneously. This research directly responds to ILNSE’s challenge in proposing two novel algorithms “Predictor for Streaming Data with Scarce Labels” (PSDSL) and Heterogeneous Dynamic Weighted Majority (HDWM) classifier. PSDSL is an Online Semi-Supervised Learning (OSSL) method for real-time DSM and is closely related to label scarcity issues in online machine learning. The key capabilities of PSDSL include learning from a small amount of labelled data in an incremental or online manner and being available to predict at any time. To achieve this, PSDSL utilises both labelled and unlabelled data to train the prediction models, meaning it continuously learns from incoming data and updates the model as new labelled or unlabelled data becomes available over time. Furthermore, it can predict under NSE conditions under the scarcity of class labels. PSDSL is built on top of the HDWM classifier, which preserves the diversity of the classifiers. PSDSL and HDWM can intelligently switch and adapt to the conditions. The PSDSL adapts to learning states between self-learning, micro-clustering and CGC, whichever approach is beneficial, based on the characteristics of the data stream. HDWM makes use of “seed” learners of different types in an ensemble to maintain its diversity. The ensembles are simply the combination of predictive models grouped to improve the predictive performance of a single classifier. PSDSL is empirically evaluated against COMPOSE, LEVELIW, SCARGC and MClassification on benchmarks, NSE datasets as well as Massive Online Analysis (MOA) data streams and real-world datasets. The results showed that PSDSL performed significantly better than existing approaches on most real-time data streams including randomised data instances. PSDSL performed significantly better than ‘Static’ i.e. the classifier is not updated after it is trained with the first examples in the data streams. When applied to MOA-generated data streams, PSDSL ranked highest (1.5) and thus performed significantly better than SCARGC, while SCARGC performed the same as the Static. PSDSL achieved better average prediction accuracies in a short time than SCARGC. The HDWM algorithm is evaluated on artificial and real-world data streams against existing well-known approaches such as the heterogeneous WMA and the homogeneous Dynamic DWM algorithm. The results showed that HDWM performed significantly better than WMA and DWM. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM. In both drift and real-world streams, significance tests and post hoc comparisons found significant differences between algorithms, HDWM performed significantly better than DWM and WMA when applied to MOA data streams and 4 real-world datasets Electric, Spam, Sensor and Forest cover. The seeding mechanism and dynamic inclusion of new base learners in the HDWM algorithms benefit from the use of both forgetting and retaining the models. The algorithm also provides the independence of selecting the optimal base classifier in its ensemble depending on the problem. A new approach, Envelope-Clustering is introduced to resolve the cluster overlap conflicts during the cluster labelling process. In this process, PSDSL transforms the centroids’ information of micro-clusters into micro-instances and generates new clusters called Envelopes. The nearest envelope clusters assist the conflicted micro-clusters and successfully guide the cluster labelling process after the concept drifts in the absence of true class labels. PSDSL has been evaluated on real-world problem ‘keystroke dynamics’, and the results show that PSDSL achieved higher prediction accuracy (85.3%) and SCARGC (81.6%), while the Static (49.0%) significantly degrades the performance due to changes in the users typing pattern. Furthermore, the predictive accuracies of SCARGC are found highly fluctuated between (41.1% to 81.6%) based on different values of parameter ‘k’ (number of clusters), while PSDSL automatically determine the best values for this parameter
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