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Subject: search.filters.subject.461104 Neural networks

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    Few-shot learning for malware detection : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Auckland, New Zealand
    (Massey University, 2024-01-29) Zhu, Jinting
    The amount of malware is growing as the electric equipment thrives, which is exacerbated by malware’s diversity and uniqueness. Under such circumstances, efficient detection by artificial intelligence (AI) has recently emerged and inspired researchers to pay attention to this field. At present, the designed AI model based on large-scale training can effectively detect known types of malicious attacks. However, malware differs from the natural images that other AI models access. In particular, zero-day attacks are scarce in number and frequently updated, and they generally are packaged with obfuscation techniques to avoid being detected. This thesis demonstrates some novel approaches from advanced artificial intelligence technology to overcome these challenges. Our first research investigates a few-shot learning model applied in malware detection with scarce data and utilizes the Siamese Neural Network (SNN) based on the metric space to detect malware. Our model addresses the optimization problem that tends to overfit in the few-shot training phase, in which feature embedding space is optimized with the objection function of binary cross-entropy loss to improve detection accuracy. We then explored the specificity between malware in the presence of obfuscation techniques affecting the malware signature and proposed a novel Task-Aware Meta-Learning-based Siamese Neural Network that generates task-specific weights based on the entropy value. With the weights that contribute to the different classes, this model efficiently captures the unique signatures of different malware families. Along with initial success in few-shot learning for malware detection, we take into account the characteristics of malicious signatures in entropy patterns. We first proposed a model that utilizes the entropy feature directly obtained from binary ransomware files to retain more fine-grained features associated with different ransomware signatures. Benefiting from the robust features, a pre-trained network (e.g., VGG-16) combined with SNN, boosts feature representation along the frequency of malware signature and achieved a competitive outcome compared with the traditional deep learning method applied in malware detection. Next, we propose a triage approach using a Task Memory based on the Meta-Transfer Learning framework, which quantifies the malware threat level in the few-shot learning mechanism to prioritize different classes, which can also alert some suspicious software to human decision-making methods. Finally, we propose a novel Siamese Neural Network (SNN) designed to replace the distance scores but use relation-aware embeddings which can output better similarity probabilities based on semantics across different malware samples. Along with the use of entropy images as inputs, our proposed model can obtain better structural information and subtle differences in malware signatures despite the noises introduced by different obfuscation techniques.
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    Viability of avocado yield quantification using machine vision and machine learning : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering at Massey University, Manawatū, New Zealand
    (Massey University, 2022) Macadam, Kyle
    The avocado is the green celebrity of the fruit world with NZ exports more than doubling since 2006. Quantitative crop estimation is a critical piece of information in a horticultural supply chain, as it heavily dictates planning of harvest timing, labour, and marketing. Like all fruit crops, the avocado industry has a need to estimate their harvest volume each season. And so arises the question many growers ask; how can they accurately estimate their harvest? However, factors like the close colour matching of the fruit with the canopy, and the large canopy of the production trees, create some challenges for crop estimation. This work provides an insight into the current methods that have been explored and what direction growers should move towards, to increase their ability to estimate crop load. Research solutions options were rated using a weighting matrix that considers accuracy, availability, cost, and relevance in order to justify technologies that are most suited to further development. A novel labelled avocado dataset was used to train a convolutional neural network called YOLOv5. The trained YOLOv5 model showed promising results, with a real time detection accuracy of 88%, and a model mAP of 86.9% at 0.6 IoU threshold when trained at an image size of 512x512 pixels. YOLOv5 predicted fruit per tree counts were within 5% compared to the harvest fruit per tree count.
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    A platform for practical homomorphic encryption in neural network classification : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Ph.D.) in Information Technology, Massey University
    (Massey University, 2021) Baryalai, Mehmood
    Convolutional neural networks (CNN) have become remarkably better in correctly identifying and classifying objects. By using CNN, numerous online services now exist that processes our data to provide meaningful insight and value-added services. Not all services are reliable and trustworthy due to which privacy concerns exist. To address the issue, the work presented in this research develops and optimise new techniques to use Homomorphic Encryption (HE) as a solution. Researchers have proposed solutions like the CryptoNets, Gazelle, and CryptoDL. However, homomorphic encryption is yet to see the limelight for real-world adoption, especially in neural networks. These proposed solutions are seen as a solution only for a particular CNN model and lack generality to be extended to a different CNN model. Moreover, the solutions for HE-CNN integration are seen as unprepared for adoption in a practical and real-world environment. Additionally, the complex integration of hybrid approaches limits their utilization with privacy-preserving based CNN models. For that reason, this research develops the mathematical and practical knowledge required to adopt HE within a CNN. This knowledge of performing encrypted classification for a CNN model is based on a careful selection of appropriate encryption parameters. Furthermore, this study succeeds in developing a dual-cloud system to mitigate many of the technical hurdles for evaluating an encrypted neural network without compromising privacy. Moreover, in the case of a single cloud, this study develops methods for overcoming technical issues in selecting encryption parameters for, and evaluating, a convolutional neural network. In the same context, the novel method of selecting and optimizing encryption parameters based on probability is given. The proposals and the knowledge from this research can aid and advance the strategies of HE-CNN integrations in an efficient and easy way.