Recent technological innovations along with the vast amount of available data worldwide have led to the rise of cyberattacks against network systems. Intrusion Detection Systems (IDS) play a crucial role as a defense mechanism in networks against adversarial attackers. Machine Learning methods provide various cybersecurity tools. However, these methods require plenty of data to be trained efficiently, which may be hard to collect or to use due to privacy reasons. One of the most notable Machine Learning tools is the Generative Adversarial Network (GAN), and it has great potential for tabular data synthesis. In this work, we start by briefly presenting the most popular GAN architectures, VanillaGAN, WGAN, and WGAN-GP. Focusing on tabular data generation, CTGAN, CopulaGAN, and TableGAN models are used for the creation of synthetic IDS data. Specifically, the models are trained and evaluated on an NSL-KDD dataset, considering the limitations and requirements that this procedure needs. Finally, based on certain quantitative and qualitative methods, we argue and evaluate the most prominent GANs for tabular network data synthesis.

The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an ∼8% F1 score improvement compared to an LSTM (long short-term memory) model.

Federated learning (FL) is an emerging machine learning technique where machine learning models are trained in a decentralized manner. The main advantage of this approach is the data privacy it provides because the data are not processed in a centralized device. Moreover, the local client models are aggregated on a server, resulting in a global model that has accumulated knowledge from all the different clients. This approach, however, is vulnerable to attacks because clients can be malicious or malicious actors may interfere within the network. In the first case, these types of attacks may refer to data or model poisoning attacks where the data or model parameters, respectively, may be altered. In this paper, we investigate the data poisoning attacks and, more specifically, the label-flipping case within a federated learning system. For an image classification task, we introduce two variants of data poisoning attacks, namely model degradation and targeted label attacks. These attacks are based on synthetic images generated by a generative adversarial network (GAN). This network is trained jointly by the malicious clients using a concatenated malicious dataset. Due to dataset sample limitations, the architecture and learning procedure of the GAN are adjusted accordingly. Through the experiments, we demonstrate that these types of attacks are effective in achieving their task and managing to fool common federated defenses (stealth). We also propose a mechanism to mitigate these attacks based on clean label training on the server side. In more detail, we see that the model degradation attack causes an accuracy degradation of up to 25%, while common defenses can only alleviate this for a percentage of ∼5%. Similarly, the targeted label attack results in a misclassification of 56% compared to 2.5% when no attack takes place. Moreover, our proposed defense mechanism is able to mitigate these attacks.

Neural Style Transfer (NST) is a popular technique of computer vision where the content of an image is blended with the style of another, which results in a fused image with certain properties of both original images. This approach has practical applications in various domains and has garnered significant attention in both industry and academia. An interesting application of this technique is segmented style transfer where a segmentation algorithm is used to locate objects within an image and then the style transfer method is performed locally, producing images with different styles for different objects. This approach opens up possibilities for creating visually striking compositions by seamlessly blending various artistic styles onto specific objects within an image, allowing for a new level of creative expression. This paper proposes a novel method that combines Segment Anything Model (SAM), a state-of-the-art vision transformer-based image segmentation model developed by Facebook, with style transfer. Our approach includes performing localized style transfer in selected segmentation regions of an image using classical style transfer algorithms. To ensure smooth transitions between the stylized and non-stylized border we also develop our loss function with a border smoothing technique. Experimental results demonstrate the robustness and effectiveness of the proposed methodology, including the ability to infuse multiple artistic styles into different objects within an image. The contributions of this work include integrating SAM with style transfer, proposing a novel loss function, evaluating the segmented style transfer in multiple content regions, comparing with state-of-the-art approaches, and experimenting with multiple style images for diverse stylization. Our primary focus centers on creating a model that serves as a digital painter across a wide range of image genres and artistic styles.

Safety management is a priority to guarantee human-centered manufacturing processes in the context of Industry 5.0, which aims to realize a safe human–machine environment based on knowledge-driven approaches. The traditional approaches for safety management in the industrial environment include staff training, regular inspections, warning signs, etc. Despite the fact that proactive measures and procedures have exceptional importance in the prevention of safety hazards, human–machine–environment coupling requires more sophisticated approaches able to provide automated, reliable, real-time, cost-effective, and adaptive hazard identification in complex manufacturing processes. In this context, the use of virtual reality (VR) can be exploited not only as a means of human training but also as part of the methodology to generate synthetic datasets for training AI models. In this paper, we propose a flexible and adjustable detection system that aims to enhance safety management in Industry 5.0 manufacturing through real-time monitoring and identification of hazards. The first stage of the system contains the synthetic data generation methodology, aiming to create a synthetic dataset via VR, while the second one concerns the training of AI object detectors for real-time inference. The methodology is evaluated by comparing the performance of models trained on both real-world data from a publicly available dataset and our generated synthetic data. Additionally, through a series of experiments, the optimal ratio of synthetic and real-world images is determined for training the object detector. It has been observed that even with a small amount of real-world data, training a robust AI model is achievable. Finally, we use the proposed methodology to generate a synthetic dataset of four classes as well as to train an AI algorithm for real-time detection.

This paper addresses the challenge of olive tree segmentation using drone imagery, which is crucial for precision agriculture applications. We tackle the data scarcity issue by augmenting existing detection datasets. Additionally, lightweight model variations of state-of-the-art models like YOLOv8n, RepViT-SAM, and EdgeSAM are combined into two proposed pipelines to meet computational constraints while maintaining segmentation accuracy. Our multifaceted approach successfully achieves an equilibrium among model size, inference time, and accuracy, thereby facilitating efficient olive tree segmentation in precision agriculture scenarios with constrained datasets. Following comprehensive evaluations, YOLOv8n appears to surpass the other models in terms of inference time and accuracy, albeit necessitating a more intricate fine-tuning procedure. Conversely, SAM-based pipelines provide a significantly more streamlined fine-tuning process, compatible with existing detection datasets for olive trees. However, this convenience incurs the disadvantages of a more elaborate inference architecture that relies on dual models, consequently yielding lower performance metrics and prolonged inference durations.