This paper seeks to understand how zero- day vulnerabilities relate to traded markets. People in trade and development are reluctant to talk about zero-day vulnerabilities. Thanks to years of research, in addition to interviews, The majority of thepublic documentation about Mr. Cesar Cerrudo s 0-day vulnerabilities are examinedby him, and he talks to experts in many computer security domains about them. In this research, we gave a summary of the current malware detection technologies and suggest a fresh zero-day malware detection and prevention model that is capable of efficiently separating malicious from benign zero-day samples. We also discussed various methods used to detect malicious files and present the results obtained from these methods.

Android is the most popular smartphone operating system with a market share of 68.6\% in Apr 2023. Hence, Android is a more tempting target for cybercriminals. This research aims at contributing to the ongoing efforts to enhance the security of Android applications and protect users from the ever-increasing sophistication of malware attacks. Zero-day attacks pose a significant challenge to traditional signature-based malware detection systems, as they exploit vulnerabilities that are unknown to all. In this context, static analysis can be an encouraging approach for detecting malware in Android applications, leveraging machine learning (ML) and deep learning (DL)-based models. In this research, we have used single feature and combination of features extracted from the static properties of mobile apps as input(s) to the ML and DL based models, enabling it to learn and differentiate between normal and malicious behavior. We have evaluated the performance of those models based on a diverse dataset (DREBIN) comprising of real-world Android applications features, including both benign and zero-day malware samples. We have achieved F1 Score 96\% from the multi-view model (DL Model) in case of Zero-day malware scenario. So, this research can be helpful for mitigating the risk of unknown malware.

The most serious risk to network security can arise from a zero-day attack. Zero-day attacks are challenging to identify as they exhibit unseen behavior. Intrusion detection systems (IDS) have gained considerable attention as an effective tool for detecting such attacks. IDS are deployed in network systems to monitor the network and to detect any potential threats. Recently, a lot of Machine learning (ML) and Deep Learning (DL) techniques have been employed in Intrusion Detection Systems, and it has been found that these techniques can detect zero-day attacks efficiently. This paper provides an overview of the background, importance, and different types of ML and DL techniques adopted for detecting zero-day attacks. Then it conducts a comprehensive review of recent ML and DL techniques for detecting zero-day attacks and discusses the associated issues. Further, we analyze the results and highlight the research challenges and future scope for improving the ML and DL approaches for zero-day attack detection.

In this paper, we propose a novel approach for detecting zero-day attacks on networked autonomous systems (AS). The proposed approach combines CNN and LSTM algorithms to offer efficient and precise detection of zero-day attacks. We evaluated the proposed approach’s performance against various ML models using a real-world dataset. The experimental results demonstrate the effectiveness of the proposed approach in detecting zero-day attacks in networked AS, achieving better accuracy and detection probability than other ML models.

This paper reports on work in progress on incorporating a possibility of zero-day attacks into security risk metrics. System security is modelled by Attack Graph (AG), where attack paths may include a combination of known and zero-day exploits. While set of feasible zero-day exploits and composition of each attack path are known, only estimates of likelihoods of known exploits are available. We propose addressing uncertain likelihoods of zero-day exploits within framework of robust risk metrics. Assuming some base likelihoods of zero-day exploits, robust risk metrics assume worst-case Probabilistic or Bayesian AG scenario allowing for a controlled deviation of actual likelihoods of zero-day exploits from their base values. The corresponding worst-case scenario is defined with respect to the system losses due to a zero-day attack. These robust risk metrics interpolate between the corresponding probabilistic or Bayesian AG model on the one hand and purely antagonistic game-theoretic model on the other hand. Popular k-zero day security metric is a particular case of the proposed metric.

This paper reports on work in progress on security metrics combining risks of known and zero-day attacks. We assume that system security is modelled by Attack Graph (AG), where attack paths may include a combination of known and zeroday exploits and impact of successful attacks is quantified by system loss function. While set of feasible zero-day exploits and composition of each attack path are known, only estimates of likelihoods of known exploits are available. After averaging the system loss function over likelihoods of known exploits, we propose addressing uncertain likelihoods of zero-day exploits within framework of robust risk metrics. Assuming some prior likelihoods of zero-day exploits, robust risk metrics are identified with the worst-case Bayesian AG scenario subject to a controlled deviation of actual likelihoods of zero-day exploits from their priors. The corresponding worst-case scenario is defined with respect to the system losses due to a zero-day attack. We argue that the proposed risk metric quantifies potential benefits of system configuration diversification, such as Moving Target Defense, for mitigation of the system/attacker information asymmetry.

Digital Twin can be developed to represent a certain soil carbon emissions ecosystem that takes into account various parameters such as the type of soil, vegetation, climate, human interaction, and many more. With the help of sensors and satellite imagery, real-time data can be collected and fed into the digital model to simulate and predict soil carbon emissions. However, the lack of interpretable prediction results and transparent decision-making results makes Digital Twin unreliable, which could damage the management process. Therefore, we proposed an explainable artificial intelligence (XAI) empowered Digital Twin for better managing soil carbon emissions through AI-enabled proximal sensing. We validated our XAIoT-DT components by analyzing real-world soil carbon content datasets. The preliminary results demonstrate that our framework is a reliable tool for managing soil carbon emissions with relatively high prediction results at a low cost.

Alzheimer s disease (AD) is a disorder that has an impact on the functioning of the brain cells which begins gradually and worsens over time. The early detection of the disease is very crucial as it will increase the chances of benefiting from treatment. There is a possibility for delayed diagnosis of the disease. To overcome this delay, in this work an approach has been proposed using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to use active Magnetic Resonance Imaging (MRI) scanned reports of Alzheimer s patients to classify the stages of AD along with Explainable Artificial Intelligence (XAI) known as Gradient Class Activation Map (Grad-CAM) to highlight the regions of the brain where the disease is detected.

Explainable Artificial Intelligence (XAI) seeks to enhance transparency and trust in AI systems. Evaluating the quality of XAI explanation methods remains challenging due to limitations in existing metrics. To address these issues, we propose a novel metric called Explanation Significance Assessment (ESA) and its extension, the Weighted Explanation Significance Assessment (WESA). These metrics offer a comprehensive evaluation of XAI explanations, considering spatial precision, focus overlap, and relevance accuracy. In this paper, we demonstrate the applicability of ESA and WESA on medical data. These metrics quantify the understandability and reliability of XAI explanations, assisting practitioners in interpreting AI-based decisions and promoting informed choices in critical domains like healthcare. Moreover, ESA and WESA can play a crucial role in AI certification, ensuring both accuracy and explainability. By evaluating the performance of XAI methods and underlying AI models, these metrics contribute to trustworthy AI systems. Incorporating ESA and WESA in AI certification efforts advances the field of XAI and bridges the gap between accuracy and interpretability. In summary, ESA and WESA provide comprehensive metrics to evaluate XAI explanations, benefiting research, critical domains, and AI certification, thereby enabling trustworthy and interpretable AI systems.

The rapid advancement in Deep Learning (DL) proposes viable solutions to various real-world problems. However, deploying DL-based models in some applications is hindered by their black-box nature and the inability to explain them. This has pushed Explainable Artificial Intelligence (XAI) research toward DL-based models, aiming to increase the trust by reducing their opacity. Although many XAI algorithms were proposed earlier, they lack the ability to explain certain tasks, i.e. image captioning (IC). This is caused by the IC task nature, e.g. the presence of multiple objects from the same category in the captioned image. In this paper we propose and investigate an XAI approach for this particular task. Additionally, we provide a method to evaluate XAI algorithms performance in the domain1.

The results of the Deep Learning (DL) are indisputable in different fields and in particular that of the medical diagnosis. The black box nature of this tool has left the doctors very cautious with regard to its estimates. The eXplainable Artificial Intelligence (XAI) recently seemed to lift this challenge by providing explanations to the DL estimates. Several works are published in the literature offering explanatory methods. We are interested in this survey to present an overview on the application of XAI in Deep Learning-based Magnetic Resonance Imaging (MRI) image analysis for Brain Tumor (BT) diagnosis. In this survey, we divide these XAI methods into four groups, the group of the intrinsic methods and three groups of post-hoc methods which are the activation based, the gradientr based and the perturbation based XAI methods. These XAI tools improved the confidence on the DL based brain tumor diagnosis.

This paper delves into the nascent paradigm of Explainable AI (XAI) and its pivotal role in enhancing the acceptability of growing AI systems that are shaping the Digital Management 5.0 era. XAI holds significant promise, promoting compliance with legal and ethical standards and offering transparent decision-making tools. The imperative of interpretable AI systems to counter the black box effect and adhere to data protection laws like GDPR is highlighted. This paper aims to achieve a dual objective. Firstly, it provides an indepth understanding of the emerging XAI paradigm, helping practitioners and academics project their future research trajectories. Secondly, it proposes a new taxonomy of XAI models with potential applications that could facilitate AI acceptability. Although the academic literature reflects a crucial lack of exploration into the full potential of XAI, existing models remain mainly theoretical and lack practical applications. By bridging the gap between abstract models and the pragmatic implementation of XAI in management, this paper breaks new ground by launching the scientific foundations of XAI in the upcoming era of Digital Management 5.0.

In the past two years, technology has undergone significant changes that have had a major impact on healthcare systems. Artificial intelligence (AI) is a key component of this change, and it can assist doctors with various healthcare systems and intelligent health systems. AI is crucial in diagnosing common diseases, developing new medications, and analyzing patient information from electronic health records. However, one of the main issues with adopting AI in healthcare is the lack of transparency, as doctors must interpret the output of the AI. Explainable AI (XAI) is extremely important for the healthcare sector and comes into play in this regard. With XAI, doctors, patients, and other stakeholders can more easily examine a decision s reliability by knowing its reasoning due to XAI s interpretable explanations. Deep learning is used in this study to discuss explainable artificial intelligence (XAI) in medical image analysis. The primary goal of this paper is to provide a generic six-category XAI architecture for classifying DL-based medical image analysis and interpretability methods.The interpretability method/XAI approach for medical image analysis is often categorized based on the explanation and technical method. In XAI approaches, the explanation method is further sub-categorized into three types: text-based, visualbased, and examples-based. In interpretability technical method, it was divided into nine categories. Finally, the paper discusses the advantages, disadvantages, and limitations of each neural network-based interpretability method for medical imaging analysis.

Explainable AI (XAI) techniques are used for understanding the internals of the AI algorithms and how they produce a particular result. Several software packages are available implementing XAI techniques however, their use requires a deep knowledge of the AI algorithms and their output is not intuitive for non-experts. In this paper we present a framework, (XAI4PublicPolicy), that provides customizable and reusable dashboards for XAI ready to be used both for data scientists and general users with no code. The models, and data sets are selected dragging and dropping from repositories While dashboards are generated selecting the type of charts. The framework can work with structured data and images in different formats. This XAI framework was developed and is being used in the context of the AI4PublicPolicy European project for explaining the decisions made by machine learning models applied to the implementation of public policies.

This work proposes an interpretable Deep Learning framework utilizing Vision Transformers (ViT) for the classification of remote sensing images into land use and land cover (LULC) classes. It uses the Shapley Additive Explanations (SHAP) values to achieve two-stage explanations: 1) bandwise feature importance per class, showing which band assists the prediction of each class and 2) spatial-wise feature understanding, explaining which embedded patches per band affected the network’s performance. Experimental results on the EuroSAT dataset demonstrate the ViT’s accurate classification with an overall accuracy 96.86 \%, offering improved results when compared to popular CNN models. Heatmaps in each one of the dataset’s existing classes highlight the effectiveness of the proposed framework in the band explanation and the feature importance.

Understanding the temperature dependence of acoustic and photoacoustic (PA) properties is important for the characterization of materials and measurements in various applications. Ultrasound methods have been developed to estimate these properties, but they require careful consideration of multiple variables and steps to obtain reliable results. This study aimed to develop an automated system for simultaneous characterization of acoustic and PA properties of materials. The system was designed to minimize operator errors, ensuring robust temperature control and reproducibility for acoustic measurements. This was made possible through the integration of a commercially available PA imaging system with a custom-built platform specifically tailored for ultrasound-based acoustic characterization. This platform consisted of both hardware and software modules. The system was evaluated with NaCl solutions at different concentrations and a gelatin/agar cubic phantom prepared with uniformly distributed magnetic nanoparticles serving as optical absorbers. Results obtained from the NaCl solution samples exhibited a high Lin s concordance coefficient (above 0.9) with previously reported studies. In the ultrasound/PA experiment, temperature dependences of the speed of sound and PA intensity revealed a strong Pearson s correlation coefficient (0.99), with both measurements exhibiting a monotonic increase as anticipated for water-based materials. These findings demonstrate the accuracy and stability of the developed system for acoustic property measurements.

In this work, we investigated the design of low loss and wideband shear horizontal surface acoustic wave (SH-SAW) acoustic delay lines (ADLs) on a sapphire-based thin-film lithium niobate on insulator (LNOI) platform. The SH-SAW propagates in a Y-cut LN/SiO2 double-layer thin film atop the sapphire substrate, where the significant acoustic impedance mismatch between the thin film and the substrate confines the acoustic energy at the surface, thus minimizing the propagation loss. The single-phase unidirectional transducers (SPUDT) used in this work is implemented with gold (Au) to maximize the electromechanical coupling as well as the directionality. The proposed ADLs based on YX-LN/SiO2/Sapphire centered at 830 MHz showed a minimum insertion loss (IL) of 3 dB, a wide fractional bandwidth (FBW) of 4.19\%, and a low propagation loss (PL) of 2.51 dB/mm, which yields an effective quality factor (QPL) exceeds 2,700. These results demonstrate the competitive performance of the proposed devices compared to state-of-the-art thin film LN ADLs, offering extremely low propagation loss for RF signal processing.

This paper presents the design of a MEMS resonator with capacitive transduction as an acoustic sensor, intended for cantilever-enhanced photoacoustic spectroscopy. The sensor employs area-variable capacitive detection by surrounding the silicon resonator with dense comb teeth. To reduce gas damping effects on the resonator motion, the anchor height is increased to 260 µm. This approach successfully resolves the capacitance detection sensitivity and motion damping trade-off commonly seen in acoustic detection. Experimental results exhibit a maximum sensitivity of 3749 mV/Pa at the resonant frequency of 1870 Hz with a 15 V bias voltage. The equivalent noise has a peak value of 7.9 µPa/Hz1/2 and the noise sources are analyzed.

This work presents a modified AlN/Sapphire layered SAW structure localized partial removal of AlN thin film and sapphire, respectively. The SAW propagation and resonance characteristics of the proposed structure with periodic grooves and voids are analyzed using finite element method (FEM). Compared with conventional AlN-based SAW, the proposed structure with optimization configuration and parameters effectively improves the K2 while maintaining a high V, meanwhile eliminates spurious modes. It is demonstrated that the Sezawa mode on the proposed SAW resonator structure offers operating frequencies above 5GHz, K2 values above 6.5\%, and an excellent impedance ratio of 98dB, which makes it a potential candidate for advanced 5G applications.

In this work, the shear horizontal surface acoustic wave (SH-SAW) resonators were demonstrated on 15° YXLiNbO3/SiO2/sapphire (LiNbO3-on-sapphire, LNOS) substrate. Compared to the reported gigahertz SAW resonators based on piezoelectric heterogeneous substrates, the fabricated resonator in this work exhibits a state-of-the-art electromechanical coupling coefficient (k2) of 42.2\%, a maximum Bode-Q (Qmax) of 1457 and an excellent figure of merit (k2×Qmax) of 615. Besides, several methods for suppressing transverse modes were implemented and compared. Tilted interdigital-transducers combined with the apodization technique can suppress the transverse modes more thoroughly while maintaining decent Q values. Overall, SAW devices based on the LNOS substrate have great potential for RF filters with low insertion loss, steep skirts, and wide bandwidth.

This paper investigates acoustic cross-coupling and remote excitation in an array of PMUTs (piezoelectric micromachined ultrasound transducers). Though undesired cross-talk can impact on PMUT array performance, the same can be also employed for remote excitation. The device array under study comprises of 7 PMUTs with constant pitch which is designed and characterized at the fundamental and higher order modes. The insights are employed to demonstrate a remote frequency filter and dual-channel excitation employing acoustic coupling.

The availability of Piezoelectric-On-Insulator (POI) substrates, made of a thin single crystal LiTaO3 film atop a silicon substrate, has promoted the development of innovative Surface and Bulk Acoustic Wave (SAW and BAW) devices. However, these substrates are so far only commercially available in 100 and 150 mm diameter. In this work, we successfully demonstrate acoustic devices based on 200 mm POI substrates. First, we fabricate SAW resonators displaying an electromechanical coupling coefficient of 8.8\% at a resonance frequency of 1.6 GHz. Then, we implement Film Bulk Acoustic Resonators (FBAR), integrating buried electrodes and an acoustic isolation structure, which exhibits a single resonance at 2.8 GHz, with an electromechanical coupling coefficient of 8.8\% and a quality factor close to 190. Eventually, we show a Solidly Mounted Resonator (SMR) based on a dielectric (AlN/SiO2) Bragg mirror, which exhibits performances close to AlN-based resonators, i.e. a coupling coefficient of 6.1\% and a quality factor of 405 at 4 GHz. For the later, a Temperature Coefficient of Frequency (TCF) of -14 and -22 ppm/°C at resonance and antiresonance are obtained respectively. Such TCF values are among the lowest ever reported for LiNbO3 and LiTaO3 BAW resonators. These results offer promising perspectives towards the development of 200 mm SAW and BAW filters based on POI substrates.

In this paper, a 30° YX-Lithium Niobate (LN) 0-th shear horizontal (SH0) plate acoustic wave (PAW) resonator is proposed. The SH0 mode characteristics the superiority of interdigital transducer (IDT) in the frequency definition over most other plate modes. Using finite element analysis method, the rotation angle of LN and the thickness of each layer were optimized for large effective coupling coefficient (k2eff) and high acoustic velocity. The rotation angle and the thickness of LN membrane are optimized as 30° and 0.2, respectively. To improve the temperature stability of proposed PAW resonators, a SiO2 film are added and the thickness is designed as 0.2. The measurement results derived a k2eff of 25.1\%, a Bode-Qmax of 604, and a Figure of merit (FoM) of 151, which is higher than the reported similar-type PAW resonators. The measured first-order temperature coefficients of frequency at resonant frequency (TCFfs) and anti-resonant frequency (TCFfp) are -38ppm/°C and -26ppm/°C, suggesting the temperature stability improvement in comparison with only LN membrane-based resonators.

This paper presents a new method to suppress spurious modes in lithium niobate thin-film acoustic devices by twisting the piezoelectric coupling properties of the spurious modes. The excellent piezoelectric properties of lithium niobate (LiNbO3) advance performance but lead to significant spurious modes accompanied by the targeted main mode. To harvest the benefits and avoid the spurious modes, this work investigates solidly mounted LiNbO3 thin films with different substrates to twist the zero-coupling orientations of spurious modes to be close to the maximum-coupling orientation of the targeted main mode. The fabricated devices, based on the solidly mounted LiNbO3sapphire structure and surface guided acoustic wave, show an operating frequency of 2.4 GHz with a large electromechanical coupling of 22\% and a spurious-free response in the wide frequency range. This work could overcome a significant bottleneck in commercializing LiNbO3 thin-film acoustic devices.