Frans Coenen

There is growing interest in Cyber Threat Intelligence (CTI) as a proactive defense strategy against cyberattacks. Indicators of Compromise (IoCs) are a key component of CTI, enabling organizations to effectively detect, mitigate, and respond to ongoing threats. Although many CTI feeds provide valuable insights into malicious activities by sharing IoCs, they often lack the necessary coverage to support timely and accurate defense against evolving cyberattacks. Moreover, most CTI feeds require organizations to invest in paid services for full access to critical data. This study introduces an open-source framework that leverages a large language model and a novel feature set to extract and rank CTI data based on the credibility of its source, with a particular focus on the X platform. The solution offers features not typically available in existing feeds, such as real-time detection, customization, open-source, and transparency in prioritizing IoCs. To this end, we fine-tune a BERT model to classify whether tweets contain IoC-related content and compare its performance with a suite of traditional machine learning classifiers that utilize various natural language processing techniques. The experimental results indicate that the fine-tuned model with an extended tokenizer and fully trainable layers achieved 99.36% accuracy, outperforming both existing solutions reported in the literature and the evaluated traditional machine learning models. Result represents 14.76% increase in F1-Score compared to the best-performing solution. Moreover, the study examined the collected IoCs from various aspects, uncovering that different credible IoCs were reported on X, some of which were not identified by some other CTI feeds.

In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a “bag” as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information.

With the advances in machine learning techniques and the potency of cloud computing there is an increasing adoption of third party cloud services for outsourcing training and prediction of machine learning models. Although the usage of cloud hosted machine learning services enables more efficient storage and computation on data, the privacy concerns and issues surrounding data sovereignty remain an important challenge. Privacy preserving machine learning provides a promising solution. In this paper, a privacy preserving neural network generation and utilisation framework is presented, the PPNNBP framework. PPNNBP allows model training and prediction to be securely delegated to a third party with minimal data owner participation once the input data have been encrypted without recourse to secret sharing or multiple party setting. This is achieved using a proposed fully homomorphic encryption scheme, the Modified Liu Scheme (MLS), that permits certain operations over cyphertexts and features order preservation. The PPNNBP framework using MLS addresses the challenge of the computational complexity of model learning using existing schemes; a complexity caused by the increasing size of cyphertexts (cyphertexts inflation) and the quantity of noise introduced into cyphertexts through the application of multiplication operations, as the learning progresses. Both the PPNNBP framework and MLS are fully described and analysed. The reported evaluation demonstrates that the PPNNBP framework achieves an accuracy that is comparable to that obtained using a “standard” framework, whilst at the same time operating in a secure manner with minimal data owner participation.

Although Deep Learning (DL) models have been introduced in various fields as effective prediction tools, they often do not care about uncertainty. This can be a barrier to their adoption in real-world applications. The current paper aims to apply and evaluate Monte Carlo (MC) dropout, a computationally efficient approach, to investigate the reliability of several skip connection-based Convolutional Neural Network (CNN) models while keeping their high accuracy. To do so, a high-dimensional regression problem is considered in the context of subterranean fluid flow modeling using 376,250 generated samples. The results demonstrate the effectiveness of MC dropout in terms of reliability with a Standard Deviation (SD) of 0.012–0.174, and of accuracy with a coefficient of determination (R²) of 0.7881–0.9584 and Mean Squared Error (MSE) of 0.0113–0.0508, respectively. The findings of this study may contribute to the distribution of pressure in the development of oil/gas fields.