Abstract Artificial Intelligence (AI) is transforming cybersecurity practices thanks to the amazing accuracy performance achieved with several AI-based malware detection systems. However, several recent studies have shown that AI decision models can be vulnerable to adversarial attacks. In malware detection scenarios, adversarial attacks are realistic manipulations of existing malware, which preserve the executable and malicious behaviour but evade the malware detection measures. In this study, we consider Windows Portable Executable (PE) malware, which is currently trending to prominent malware types, and we show that counterfactual explanations can be used to drive the generation of realistic adversarial Windows PE malware to evade AI-based detection. In particular, the proposed method OLIVANDER works in a black-box manner, which is the most restrictive attack option, as the evasion method interacts with the target decision system to evade by merely knowing the model input and output. The evaluation study explores the effectiveness of the proposed evasion method in terms of evasion ability, efficiency of computation, and attack transferability compared to two state-of-the-art evasion methods. In addition, the performed evaluation accounts for performances on commercial anti-malware systems.

Abstract Forest tree dieback inventory has a crucial role in improving forest management strategies. This inventory is traditionally performed by forests through laborious and time-consuming human assessment of individual trees. On the other hand, the large amount of Earth satellite data that are publicly available with the Copernicus program and can be processed through advanced deep learning techniques has recently been established as an alternative to field surveys for forest tree dieback tasks. However, to realize its full potential, deep learning requires a deep understanding of satellite data since the data collection and preparation steps are essential as the model development step. In this study, we explore the performance of a data-centric semantic segmentation approach to detect forest tree dieback events due to bark beetle infestation in satellite images. The proposed approach prepares a multisensor data set collected using both the SAR Sentinel-1 sensor and the optical Sentinel-2 sensor and uses this dataset to train a multisensor semantic segmentation model. The evaluation shows the effectiveness of the proposed approach in a real inventory case study that regards non-overlapping forest scenes from the Northeast of France acquired in October 2018. The selected scenes host bark beetle infestation hotspots of different sizes, which originate from the mass reproduction of the bark beetle in the 2018 infestation.

This study describes a new cyber-threat detection method, named PANACEA, that uses Ensemble Deep Learning coupled with Adversarial Training and XAI, to gain accuracy with neural models trained in cybersecurity problems.

Abstract Various applications in remote sensing demand automatic detection of changes in optical satellite images of the same scene acquired over time. This paper investigates how to leverage autoencoders in change vector analysis, in order to better delineate possible changes in a couple of co-registered, optical satellite images. Let us consider both a primary image and a secondary image acquired over time in the same scene. First an autoencoder artificial neural network is trained on the primary image. Then the reconstruction of both images is restored via the trained autoencoder so that the spectral angle distance can be computed pixelwise on the reconstructed data vectors. Finally, a threshold algorithm is used to automatically separate the foreground changed pixels from the unchanged background. The assessment of the proposed method is performed in three couples of benchmark hyperspectral images using different criteria, such as overall accuracy, missed alarms and false alarms. In addition, the method supplies promising results in the analysis of a couple of multispectral images of the burned area in the Majella National Park (Italy).