336 research outputs found
A Novel Hybrid Spotted Hyena-Swarm Optimization (HS-FFO) Framework for Effective Feature Selection in IOT Based Cloud Security Data
Internet of Things (IoT) has gained its major insight in terms of its deployment and applications. Since IoT exhibits more heterogeneous characteristics in transmitting the real time application data, these data are vulnerable to many security threats. To safeguard the data, machine and deep learning based security systems has been proposed. But this system suffers the computational burden that impedes threat detection capability. Hence the feature selection plays an important role in designing the complexity aware IoT systems to defend the security attacks in the system. This paper propose the novel ensemble of spotted hyena with firefly algorithm to choose the best features and minimise the redundant data features that can boost the detection system's computational effectiveness. Firstly, an effective firefly optimized feature correlation method is developed. Then, in order to enhance the exploration and search path, operators of fireflies are combined with Spotted Hyena to assist the swarms in leaving the regionally best solutions. The experimentation has been carried out using the different IoT cloud security datasets such as NSL-KDD-99 , UNSW and CIDCC -001 datasets and contrasted with ten cutting-edge feature extraction techniques, like PSO (particle swarm optimization), BAT, Firefly, ACO(Ant Colony Optimization), Improved PSO, CAT, RAT, Spotted Hyena, SHO and BOC(Bee-Colony Optimization) algorithms. Results demonstrates the proposed hybrid model has achieved the better feature selection mechanism with less convergence time and aids better for intelligent threat detection system with the high performance of detection
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Analysis of physiological signals using machine learning methods
Technological advances in data collection enable scientists to suggest novel approaches, such as Machine Learning algorithms, to process and make sense of this information. However, during this process of collection, data loss and damage can occur for reasons such as faulty device sensors or miscommunication. In the context of time-series data such as multi-channel bio-signals, there is a possibility of losing a whole channel. In such cases, existing research suggests imputing the missing parts when the majority of data is available. One way of understanding and classifying complex signals is by using deep neural networks. The hyper-parameters of such models have been optimised using the process of back propagation. Over time, improvements have been suggested to enhance this algorithm. However, an essential drawback of the back propagation can be the sensitivity to noisy data. This thesis proposes two novel approaches to address the missing data challenge and back propagation drawbacks: First, suggesting a gradient-free model in order to discover the optimal hyper-parameters of a deep neural network. The complexity of deep networks and high-dimensional optimisation parameters presents challenges to find a suitable network structure and hyper-parameter configuration. This thesis proposes the use of a minimalist swarm optimiser, Dispersive Flies Optimisation(DFO), to enable the selected model to achieve better results in comparison with the traditional back propagation algorithm in certain conditions such as limited number of training samples. The DFO algorithm offers a robust search process for finding and determining the hyper-parameter configurations. Second, imputing whole missing bio-signals within a multi-channel sample. This approach comprises two experiments, namely the two-signal and five-signal imputation models. The first experiment attempts to implement and evaluate the performance of a model mapping bio-signals from A toB and vice versa. Conceptually, this is an extension to transfer learning using CycleGenerative Adversarial Networks (CycleGANs). The second experiment attempts to suggest a mechanism imputing missing signals in instances where multiple data channels are available for each sample. The capability to map to a target signal through multiple source domains achieves a more accurate estimate for the target domain. The results of the experiments performed indicate that in certain circumstances, such as having a limited number of samples, finding the optimal hyper-parameters of a neural network using gradient-free algorithms outperforms traditional gradient-based algorithms, leading to more accurate classification results. In addition, Generative Adversarial Networks could be used to impute the missing data channels in multi-channel bio-signals, and the generated data used for further analysis and classification tasks
Machine-learning-based condition assessment of gas turbine: a review
Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machinelearning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version
Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion
A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties
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