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This study delves into the effectiveness of two time integration techniques, namely the adaptive implicit–explicit (imp–exp) and explicit–explicit (exp–exp) methods, which stand as efficient formulations for tackling intricate systems characterized by multiple time scales. The imp–exp technique combines implicit and explicit procedures by employing implicit formulations for faster components and explicit calculations for slower ones, achieving high accuracy and computational efficiency. Conversely, the exp–exp method, a variation of explicit methods with sub-cycling, excels in handling locally stiff systems by employing smaller sub-steps to resolve rapid changes while maintaining stability. For both these approaches, numerical damping may be activated by adaptive time integration parameters, allowing numerical dissipation to be locally applied, if necessary, as a function of the considered discrete model and its computed responses, enabling a highly effective numerical dissipative algorithm. Furthermore, both these techniques stand as very simple and straightforward formulations as they rely solely on single-step displacement–velocity relations, describing truly self-starting procedures, and they stand as entirely automated methodologies, requiring no effort nor expertise from the user. This work provides comparative studies of the adaptive imp–exp and exp–exp approaches to assess their accuracy and efficiency across a wide range of scenarios, with emphasis on geophysical applications characterized by multiscale problems, aiming to establish under which circumstances one approach should be preferred over the other. [ABSTRACT FROM AUTHOR]

This study delves into the effectiveness of two time integration techniques, namely the adaptive implicit–explicit (imp–exp) and explicit–explicit (exp–exp) methods, which stand as efficient formulations for tackling intricate systems characterized by multiple time scales. The imp–exp technique combines implicit and explicit procedures by employing implicit formulations for faster components and explicit calculations for slower ones, achieving high accuracy and computational efficiency. Conversely, the exp–exp method, a variation of explicit methods with sub-cycling, excels in handling locally stiff systems by employing smaller sub-steps to resolve rapid changes while maintaining stability. For both these approaches, numerical damping may be activated by adaptive time integration parameters, allowing numerical dissipation to be locally applied, if necessary, as a function of the considered discrete model and its computed responses, enabling a highly effective numerical dissipative algorithm. Furthermore, both these techniques stand as very simple and straightforward formulations as they rely solely on single-step displacement–velocity relations, describing truly self-starting procedures, and they stand as entirely automated methodologies, requiring no effort nor expertise from the user. This work provides comparative studies of the adaptive imp–exp and exp–exp approaches to assess their accuracy and efficiency across a wide range of scenarios, with emphasis on geophysical applications characterized by multiscale problems, aiming to establish under which circumstances one approach should be preferred over the other.

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders. We introduce KnockoffHybrid, a statistical method to identify risk genetic variants for the population and trio designs built upon a recently developed knockoff framework. KnockoffHybrid controls the false discovery rate, protects against confounding due to population structure, and is more powerful than conventional methods that control the family-wise error rate. [ABSTRACT FROM AUTHOR]

Objective: This is a comprehensive overview of long-term cancer survival in Zhejiang Province, China. Hybrid analysis, a combination of cohort and period analysis, has been proposed to derive up-to-date cancer survival estimates. Using this approach, we aimed to timely and accurately analyze the 5-year relative survival (RS) and net survival (NS) in cancer registries of Zhejiang Province, China. Methods: A total of 255,725 new cancer cases diagnosed during 2013–2017 were included in 14 cancer registries in Zhejiang Province, China, with a follow-up on vital status until the end of 2019. The hybrid analysis was used to calculate the 5-year RS and 5-year NS during 2018–2019 for overall and stratifications by sex, cancer type, region, and age at diagnosis. Results: During 2018–2019, the age-standardized 5-year RS and NS for overall cancer in Zhejiang was 47.5% and 48.6%, respectively. The age-standardized 5-year RS for cancers of women (55.4%) was higher than that of men (40.0%), and the rate of urban areas (49.7%) was higher than that of rural areas (43.1%). The 5-year RS declined along with age, from 84.4% for ages 74 years. Our results of the RS and NS showed the similar trend and no significant difference. The top five cancers with top age-standardized 5-year RS were thyroid cancer (96.0%), breast cancer (84.3%), testicular cancer (79.9%), prostate cancer (77.2%), and bladder cancer (70.6%), and the five cancers with the lowest age-standardized 5-year RS were pancreatic cancer (6.0%), liver cancer (15.6%), gallbladder cancer (17.1%), esophageal cancer (22.7%), and leukemia (31.0%). Conclusions: We reported the most up-to-date 5-year cancer RS and NS in Zhejiang Province, China for the first time, and found that the 5-year survival for cancer patients in Zhejiang during 2018–2019 was relatively high. The population-based cancer registries are recognized as key policy tools that can be used to evaluate both the impact of cancer prevention strategies and the effectiveness of health systems.

The prevalence of smartphones is deeply embedded in modern society, impacting various aspects of our lives. Their versatility and functionalities have fundamentally changed how we communicate, work, seek entertainment, and access information. Among the many smartphones available, those operating on the Android platform dominate, being the most widely used type. This widespread adoption of the Android OS has significantly contributed to increased malware attacks targeting the Android ecosystem in recent years. Therefore, there is an urgent need to develop new methods for detecting Android malware. The literature contains numerous works related to Android malware detection. As far as our understanding extends, we are the first ones to identify dangerous combinations of permissions and system calls to uncover malicious behavior in Android applications. We introduce a novel methodology that pairs permissions and system calls to distinguish between benign and malicious samples. This approach combines the advantages of static and dynamic analysis, offering a more comprehensive understanding of an application's behavior. We establish covalent bonds between permissions and system calls to assess their combined impact. We introduce a novel technique to determine these pairs' Covalent Bond Strength Score. Each pair is assigned two scores, one for malicious behavior and another for benign behavior. These scores serve as the basis for classifying applications as benign or malicious. By correlating permissions with system calls, the study enables a detailed examination of how an app utilizes its requested permissions, aiding in differentiating legitimate and potentially harmful actions. This comprehensive analysis provides a robust framework for Android malware detection, marking a significant contribution to the field. The results of our experiments demonstrate a remarkable overall accuracy of 97.5%, surpassing various state-of-the-art detection techniques proposed in the current literature. [ABSTRACT FROM AUTHOR]

For malware detection, the commonly known technique used by many commercial antivirus engines is signature-based methods. However, researchers have started focusing on utilizing machine learning (ML)-based methods for this task over the past few years. This is because signature-based have their limitations of not recognizing new variants of malware. On the other hand, ML-based methods have proven their worth across different domains such as images, text, and audio with their generalization capabilities. Even though these ML-based methods are robust enough compared to signature-based methods, these can be easily fooled by executing an adversarial attack by simply crafting an adversarial sample that looks similar to the original sample with some added perturbation. Such attacks must be carried out to know the potential threat under different scenarios and leverage the knowledge to tighten the defenses of the ML models. We propose a reinforcement learning (RL)-based approach to generate adversarial examples that utilize action-masking with the agent learning process. In our work, we attack two pre-trained static models – EMBER trained on 1 M+ samples and GBDT trained on 100K+ samples. We also attack a surrogate model trained on 25K+ samples, a substitute for 11 different self-trained hybrid models. We also demonstrate the transferability attack on all the hybrid models with the help of adversarial examples generated after attacking the surrogate and two pre-trained static models. We also perform adversarial defense training on these 11 hybrid models and evaluate their robustness against such attacks. We achieve the maximum evasion rates of 47.64%, 33.2%, and 94.67% for GBDT, EMBER, and surrogate models using action masks, respectively. We also present binary modification evidence and a few corner cases. Since modifying binary files is tricky, even a 1-byte change at the wrong place can corrupt the binary. We use the modification routine that covers some corner cases and restricts us from taking action that can potentially corrupt the binary. [ABSTRACT FROM AUTHOR]

As the development of high-performance computing (HPC) is growing, exascale computing is on the horizon. Therefore, it is imperative to develop parallel systems, such as graphics processing units (GPUs) and programming models, that can effectively utilise the powerful processing resources of exascale computing. A tri-level programming model comprising message passing interface (MPI), compute unified device architecture (CUDA), and open multi-processing (OpenMP) models may significantly enhance the parallelism, performance, productivity, and programmability of the heterogeneous architecture. However, the use of multiple programming models often leads to unexpected errors and behaviours during run-time. It is also difficult to detect such errors in high-level parallel programming languages. Therefore, this present study proposes a parallel hybrid testing tool that employs both static and dynamic testing techniques to address this issue. The proposed tool was designed to identify the run-time errors of C++ and MPI + OpenMP + CUDA systems by analysing the source code during run-time, thereby optimising the testing process and ensuring comprehensive error detection. The proposed tool was able to identify and categorise the run-time errors of tri-level programming models. This highlights the need for a parallel testing tool that is specifically designed for tri-level MPI + OpenMP + CUDA programming models. As contemporary parallel testing tools cannot, at present, be used to test software applications produced using tri-level MPI + OpenMP + CUDA programming models, this present study proposes the architecture of a parallel testing tool to detect run-time errors in tri-level MPI + OpenMP + CUDA programming models. [ABSTRACT FROM AUTHOR]

Steel structures are frequently exposed to high‐cycle loading in which fatigue cracks initiate and propagate in the service life. The development of crack(s) will affect the structural integrity. In extreme cases, e.g., earthquakes and impacts, structures may be overloaded in several cycles or even a single loading cycle with the development of significant plastic deformation and macro cracks. Service loads, e.g., traffic and waves, will afterwards still be experienced in these damaged structures. The aforementioned two types of deformation are currently analyzed using damage and fracture mechanics methods, respectively. The cycle‐by‐cycle calculation and jump strategy relying on the Paris' equation, are employed for the low (including monotonic) and high‐cycle problems, using elastoplastic and linear‐elastic materials, respectively. The two calculation methods were developed independently, which presents a challenge in analyzing overloaded high‐cycle fatigue problems. This paper presents and executes a hybrid analysis using damage and fracture mechanics models for the single‐edge notch bend specimen. Either linear‐elastic or elasto‐plastic materials is employed in finite element simulations. The hybrid analysis with the combination of damage and linear‐elastic fracture mechanics shows the potential of a promising solution for assessing the fatigue behaviour of steel structures experiencing extreme loading and associated stepwise material damage. Some challenges are addressed in the combination of the ductile fracture followed by the high cycle fatigue calculation. [ABSTRACT FROM AUTHOR]

Android smartphones, integral to everyday life, offer a multifunctional platform for storing and managing sensitive personal data. However, the ubiquity of Android applications intensifies their vulnerability to malicious applications. This study presents the Static Dynamic Hybrid Feature Extraction (SDHFE) tool, a lightweight automation tool designed for the efficient analysis of Android applications by extracting features from a variety of sources. The research generated multiple datasets, each representing different feature categories and their combinations. A novel approach to improve Android malware detection on smartphones is introduced, leveraging the random forest algorithm. Multiple models were created and evaluated using metrics such as accuracy, precision, recall, and F1 score. The model trained on a dataset comprising permissions and intents achieved the highest average scores, 99.2%, thus outperforming other models. A comparative analysis was conducted to evaluate the efficiency of the SDHFE tool against two widely used tools, APKtool and Androguard, in static feature extraction. The results demonstrated that the SDHFE tool significantly reduced disassembly and analysis time, outperforming APKtool and Androguard by factors of 2.2 and 4.6, respectively. While this research provides valuable insights into Android malware detection, it is important to acknowledge potential limitations. The dynamic nature of malware behavior could affect the generalizability of our approach. Despite these potential limitations, the results underscore the effectiveness of our proposed method for enhancing malware detection in Android smartphones. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources can be supported by the digitalization of energy systems, which increase dependability and lower costs of energy production and consumption. However, the energy digitalization support energy infrastructures and technologies currently in place are insufficient. This research presented the study results by using the generalized least square estimates (GLS) model and the international sample of China regions from 2003 to 2017. Main results of the dynamic fixed effect (DFE) estimator for the autoregressive distributed lag (ARDL) method, establishing ES goals for lowering energy consumption and pollution emission fosters a country's renewable energy business sector's digital transformation in the short term, while encouraging the use of renewable energy sources fosters a country's long-term digitalization efforts. Based on this, the direct effects and dynamic effects of digitalization and financial development on environmental are explored, respectively, using the panel data regression model and panel vector autoregression (PVAR) model. The threshold regression model is then used to examine the two parameters' threshold effects on eco-efficiency. An accurate estimate of the resource consumption in smart factories is made possible by the digital twin that is created using the product's and its attributes as well as manufacturing data. The results suggests the future directions for the associated stakeholders. [ABSTRACT FROM AUTHOR]

As one of the most important indicators of coal, calorific value (CV) not only determines the value of coal product, but also has a significant impact on the further processing and utilization of coal. Traditional methods of obtaining prediction data suffer from a range of problems such as too many input variables, low prediction accuracy of a single analysis method and lack of sensitivity analysis of input variables. In this paper, a novel hybrid analysis was presented to predict the CV of coal. Pearson correlation coefficient (PCC) was used to remove correlations between variables in order to provide a suitable combination of input variables for ML models. The results showed that based on the optimal combination of input variables (ash, Fe, Mg and Na), RF model provided a better regression, better fit and better robustness on the testing set than the other three models when the performance indicators and the number of input variables were considered. In addition, sensitivity analyses of input variables showed the relative importance of individual variables and the way in which each variable affects the output variables. The present work provided novel insights and ideas for understanding the prediction of the CV of coal. [ABSTRACT FROM AUTHOR]

Android devices are popularly available in the commercial market at different price levels for various levels of customers. The Android stack is more vulnerable compared to other platforms because of its open-source nature. There are many android malware detection techniques available to exploit the source code and find associated components during execution time. To obtain a better result we create a hybrid technique merging static and dynamic processes. In this paper, in the first part, we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Multicollinearity problem is one of the drawbacks in the existing system. In the proposed work, a novel PCA (Principal Component Analysis) based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach. The Android Sensitive Permission is one major key point to be considered while detecting malware. We select vulnerable columns based on features like sensitive permissions, application program interface calls, services requested through the kernel, and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign. The final goal of this paper is to check benchmarking datasets collected from various repositories like virus share, Github, and the Canadian Institute of cyber security, compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate. [ABSTRACT FROM AUTHOR]

As the development of high-performance computing (HPC) is growing, exascale computing is on the horizon. Therefore, it is imperative to develop parallel systems, such as graphics processing units (GPUs) and programming models, that can effectively utilise the powerful processing resources of exascale computing. A tri-level programming model comprising message passing interface (MPI), compute unified device architecture (CUDA), and open multi-processing (OpenMP) models may significantly enhance the parallelism, performance, productivity, and programmability of the heterogeneous architecture. However, the use of multiple programming models often leads to unexpected errors and behaviours during run-time. It is also difficult to detect such errors in high-level parallel programming languages. Therefore, this present study proposes a parallel hybrid testing tool that employs both static and dynamic testing techniques to address this issue. The proposed tool was designed to identify the run-time errors of C++ and MPI + OpenMP + CUDA systems by analysing the source code during run-time, thereby optimising the testing process and ensuring comprehensive error detection. The proposed tool was able to identify and categorise the run-time errors of tri-level programming models. This highlights the need for a parallel testing tool that is specifically designed for tri-level MPI + OpenMP + CUDA programming models. As contemporary parallel testing tools cannot, at present, be used to test software applications produced using tri-level MPI + OpenMP + CUDA programming models, this present study proposes the architecture of a parallel testing tool to detect run-time errors in tri-level MPI + OpenMP + CUDA programming models.

Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination of static and dynamic methods that relies on machine learning to detect android botnet applications is proposed. Furthermore, results are evaluated using machine learning classifiers. The Random Forest (RF) classifier outperform as compared to other ML techniques i.e., Naïve Bayes (NB), Support Vector Machine (SVM), and Simple Logistic (SL). Our proposed framework achieved 97.48% accuracy in the detection of botnet applications. Finally, some future research directions are highlighted regarding botnet attacks detection for the entire community. [ABSTRACT FROM AUTHOR]

Java deserialization vulnerabilities have become a common threat to Java application security nowadays.Finding out the gadget chain determines whether this type of vulnerability can be exploited.Due to the large code space of Java applications and dependent libraries and the polymorphism of Java itself, manual analysis of Java deserialization gadget chains consumes a lot of time and effort and it is highly dependent on the experienced knowledge.Therefore, it is crucial to study how to efficiently and accurately automate the discovery of gadget chains.Java deserialization gadget chain discovery method based on hybrid analysis was proposed.Call graph based on the variable declaration type was constructed, and then the deserialization entry functions that may reach the dangerous functions were screened using the call graph analysis.The screened entry functions were used as the entry point of the hybrid information flow analysis.The hybrid information flow analysis was carried out for both pointer and tainted variables.The tainted objects created implicitly were marked.The tainted information and the pointer information were propagated simultaneously to construct the hybrid information flow graph.The reachability of external taint data propagation to the dangerous function was judged based on the hybrid information flow graph.The corresponding deserialization gadget chain was constructed according to the taint propagation path.The hybrid analysis took into account the efficiency of call graph analysis and the accuracy of hybrid information flow analysis.The corresponding static analysis tool, namely GadgetSearch, was implemented based on the proposed hybrid analysis method.In the experimental evaluation, GadgetSearch had lower false positive and lower false negative than the existing tool GadgetInspector on four datasets of Ysoserial, Marshalsec, Jackson historical CVE, and XStream historical CVE.Additionally, GadgetSearch also found multiple undisclosed gadget chains.The experimental results show that the proposed method can efficiently and accurately discover the Java deserialization gadget chain in multiple practical Java applications.

Artificial intelligence capabilities (AIC) can influence supply chain management (SCM) in multiple ways. This study explores how generative artificial intelligence capabilities (GAIC) could affect digital supply chain performance (DSCP) through ambidexterity innovation (AMI), which includes both elements, exploratory and exploitative innovations in the manufacturing firms (MFs) in Jordan as a developing and emerging economy. This study adopted a quantitative methodology for the data collection process applying a cross-sectional approach through testing deductive-hypotheses techniques. 263 valid surveys were used for analysis using hybrid analysis measurements (i.e., PLS-SEM, and CB-SEM). Further, it was applied data reliability, convergent validity, and discriminant validity tests. Additionally, examined the mediating effect of exploratory innovation (EXPI), and exploitative innovation (EXTI) on DSCP. The study findings assured that the proposed direct and indirect causal associations illustrated in the study model were accepted due to that all associations between the dimensions s were statistically significant. The findings of the GAIC supported a positive relationship between GAIC and the DSCP, GAIC on EXPI and EXTI, and EXPI and EXTI on DSCP respectively. Furthermore, the mediating effect of EXPI and EXTI is statistically significant, which was confirmed. This study developed a conceptual model to merge GAIC, AMI, and DSCP. This study provides new outcomes that bridge the existing research gap in the literature by testing the mediation model with a focus on the MF benefits of GAIC to improve levels of EXPI, EXTI, and DSCP in Jordan as a developing and emerging economy. Furthermore, this study is considered unique, as it was the first study in Jordan, and through applying hybrid analysis measurements using both PLS-SEM and CB-SEM methods. • Purpose– Artificial intelligence capabilities (AIC) can influence supply chain management (SCP) in multiple ways. This study explores how generative artificial intelligence capabilities (GAIC) could affect digital supply chain performance (DSCP) through ambidexterity innovation (AMI), which includes both elements, exploratory and exploitative innovations in the manufacturing firms (MFs) in Jordan as a developing and emerging economy. • Design/methodology/approach – This study adopted a quantitative methodology and used online Google Form surveys for the data collection process applying a cross-sectional approach through testing deductive-hypotheses techniques. 263 valid surveys out of 1114 were used for analysis using hybrid analysis measurements (i.e., PLS-SEM, and CB-SEM), representing a 23.6 % response rate. Further, it was applied data reliability, convergent validity, and discriminant validity tests. Additionally, examined the mediating effect of exploratory innovation (EXPI), and exploitative innovation (EXTI) on DSCP. • Findings – The study findings assured that the proposed direct and indirect causal associations illustrated in the study model were accepted due to that all associations between the dimensions s were statistically significant. The findings of the GAIC supported a positive relationship between GAIC and the DSCP, GAIC on EXPI and EXTI, and EXPI and EXTI on DSCP respectively. Furthermore, the mediating effect of EXPI and EXTI is statistically significant, which was confirmed. • Research limitations/implications – This study was conducted in one shot using a cross-sectional approach, one of the main limitations was using this methodology affected generalizing the study findings, since the primary data was gathered from only one country (Jordan). • Originality/value – This study developed a conceptual model to merge GAIC, AMI, and DSCP. This study provides new outcomes that bridge the existing research gap in the literature by testing the mediation model with a focus on the MF benefits of GAIC to improve levels of EXPI, EXTI, and DSCP in Jordan as a developing and emerging economy. Furthermore, this study is considered unique, as it was the first study in Jordan, and through applying hybrid analysis measurements using both PLS-SEM and CB-SEM methodologies. [ABSTRACT FROM AUTHOR]

Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and tools. Research has spawned in various directions to yield effective malware detection mechanisms. Since malware can adopt different ways to attack and hide, accurate analysis is the key to detecting them. Like any usual mobile app, malware requires permission to take action and use device resources. There are 235 total permissions that the Android app can request on a device. Malware takes advantage of this to request unnecessary permissions, which would enable those to take malicious actions. Since permissions are critical, it is important and challenging to identify if an app is exploiting permissions and causing damage. The focus of this article is to analyze the identified studies that have been conducted with a focus on permission analysis for malware detection. With this perspective, a systematic literature review (SLR) has been produced. Several papers have been retrieved and selected for detailed analysis. Current challenges and different analyses were presented using the identified articles. [ABSTRACT FROM AUTHOR]

Copyright of Österreichische Wasser- und Abfallwirtschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobile apps represent essential tools in our daily routines, supporting us in almost every task. However, this assistance might imply a high cost in terms of privacy. Indeed, mobile apps gather a massive amount of data about individuals (e.g., users' profiles and habits) and their devices (e.g., locations), where not all are strictly needed for app execution. According to privacy laws, apps' providers must inform end-users on adopted data usage practices (e.g., which data are collected and for which purpose). Unfortunately, understanding these practices is a complex task for average end-users. The result is that they install apps without understanding their privacy implications. To support users in making more privacy-aware decisions on app usage, we propose a risk estimation approach based on an analysis of the app's code. This analysis adopts a hybrid strategy, exploiting static and dynamic code analyses. Static analysis aims at discovering which personal data an app is collecting to determine whether the target app is asking more than required. This gives the first estimation of the app's risk level. In addition, we also perform a dynamic analysis of the target app's code. This further analysis helps determining whether the collected personal data is consumed locally on the mobile device or sent out to external services. If this happens, the risk level has to be increased, as personal data are more exposed. To prove the proposal's effectiveness, we run several experiments involving different groups of participants. The obtained accuracy results are promising and outperform those obtained with static analysis only. [ABSTRACT FROM AUTHOR]

A hybrid seismic analysis computing the full nonlinear response of building structures is proposed and validated in this paper. Recurrent neural networks are trained to predict the nonlinear hysteretic response of isolation devices with deformation- and velocity-dependent behavior. Then, they are implemented in an explicit time integration method for time history analysis. A comprehensive framework is proposed to develop and test deep learning models considering the data framing, the network architecture, and the learning behavior. Hybrid seismic analyses of three base-isolated building models subjected to four ground motions with different properties were performed in order to check their efficiency. The small relative errors of computed results to those of the conventional analysis successfully validate the accuracy of the proposed analysis. Its computation time depends mainly on the ground motion duration and is considered negligible. The development of the machine learning model is more time-consuming but nonrepetitive since it can be saved and reused to analyze any new structure containing the same target components. The proposed hybrid seismic analysis overcomes the shortcomings of usual applications of machine learning in structural response prediction problems being limited to specific response quantity(s) of the same structure(s) used at the training process. By taking advantage of both mechanics-based and data-driven methods, results reveal that hybrid analysis is an efficient tool for building-response simulation. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper provides a new solution to identify the internal forces of segmental tunnel linings by combining laser scanning and hybrid structural analysis. First, a hybrid structural analysis method for quantifying the internal forces based on displacement monitoring is established, which requires comprehensive displacement monitoring with high precision and a complete trace history. Motivated by the development of laser scanning, two remedial solutions are proposed for typically insufficient engineering conditions, i.e., lack of displacement developing process and poor accuracy of measurements, which is highlighted in this paper. Therefore, with the help of remedial solutions, the structural analysis is able to be adopted with the application of laser scanning. The tool for developing remedial solutions is the first-order theory of slender circular arches. Virtual tests, based on a calibrated finite element model, were performed to verify the feasibility of the presented hybrid analysis and remedial solutions. In addition, parametric analyses were conducted to study the error propagation from laser scanning to the results of hybrid analysis. The resolution and measurement noise of laser scanning were investigated and discussed. On this basis, advice on combining laser scanning and hybrid structural analysis is proposed. Finally, on-site application of the hybrid analysis on an actual tunnel is presented and discussed. [ABSTRACT FROM AUTHOR]

Android has become the target of attackers because of its popularity. The detection of Android mobile malware has become increasingly important due to its significant threat. Supervised machine learning, which has been used to detect Android malware is far from perfect because it requires a significant amount of labeled data. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique, namely pseudo-label stacked auto-encoder (PLSAE), which involves training using a set of labeled and unlabeled instances. We use a hybrid approach of dynamic analysis and static analysis to craft feature vectors. We evaluate our proposed model on CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories. After that, we compare the results with state-of-the-art techniques in terms of accuracy and efficiency. Experimental results show that our proposed framework outperforms other semi-supervised approaches and common machine learning algorithms. [ABSTRACT FROM AUTHOR]

In today's world that called technology age, smartphones have become indispensable for users in many areas such as internet usage, social media usage, bank transactions, e-mail, as well as communication. The Android operating system is the most popular operating system that used with a rate of 85.4% in smartphones and tablets. Such a popular and widely used platform has become the target of malware. Malicious software can cause both material and moral damages to users. In this study, malwares that targeting smart phones were detected by using static, dynamic and hybrid analysis methods. In the static analysis, feature extraction was made in 9 different categories. These attributes are categorized under the titles of requested permissions, intents, Android components, Android application calls, used permissions, unused permissions, suspicious Android application calls, system commands, internet addresses. The obtained features were subjected to dimension reduction with principal component analysis and used as input to the deep neural network model. With the established model, 99.38% accuracy rate, 99.36% F1 score, 99.32% precision and 99.39% sensitivity values were obtained in the test data set. In the dynamic analysis part of the study, applications were run on a virtual smartphone, and Android application calls with strategic importance were obtained by hooking. The method called hybrid analysis was applied by combining the dynamically obtained features with the static features belonging to the same applications. With the established model, 96.94% accuracy rate, 96.78% F1 score, 96.99% precision and 96.59% sensitivity values were obtained in the test data set. [ABSTRACT FROM AUTHOR]

Android ransomware is one of the most threatening attacks that is increasing at an alarming rate. Ransomware attacks usually target Android users by either locking their devices or encrypting their data files and then requesting them to pay money to unlock the devices or recover the files back. Existing solutions for detecting ransomware mainly use static analysis. However, limited approaches apply dynamic analysis specifically for ransomware detection. Furthermore, the performance of these approaches is either poor or often fails in the presence of code obfuscation techniques or benign applications that use cryptography methods for their APIs usage. Additionally, most of them are unable to detect ransomware attacks at early stages. Therefore, this paper proposes a hybrid detection system that effectively utilizes both static and dynamic analyses to detect ransomware with high accuracy. For the static analysis, the proposed hybrid system considered more than 70 state-of-the-art antivirus engines. For the dynamic analysis, this research explored the existing dynamic tools and conducted an in-depth comparative study to find the proper tool to integrate it in detecting ransomware whenever needed. To evaluate the performance of the proposed hybrid system, we analyzed statically and dynamically over one hundred ransomware samples. These samples originated from 10 different ransomware families. The experiments' results revealed that static analysis achieved almost half of the detection accuracy—ranging around 40–55%, compared to the dynamic analysis, which reached a 100% accuracy rate. Moreover, this research reports some of the high API classes, methods, and permissions used in these ransomware apps. Finally, some case studies are highlighted, including failed running apps and crypto-ransomware patterns. [ABSTRACT FROM AUTHOR]

The analysis of the target's broadband electromagnetic scattering characteristics plays an important role in radar stealth and recognition areas. The traditional method of moments (MoM) needs to calculate the currents at each frequency point when analyzing the target. With the increase of the target electrical size and complexity, the radar cross section (RCS) of the target changes drastically with frequency, it is necessary to calculate the accurate frequency response with a small frequency interval, which is very time-consuming in calculation and not feasible. This paper proposes to combine the subdomain method of moments (SMoM) with the Chebyshev approximation technique (CAT) to efficiently analyze the electromagnetic scattering of arbitrary shaped objects over a broad frequency band. In the SMoM technique, the whole object can be divided into several regions, each of which can be independently solved by the method of moments (MoM). The CAT technique is employed to expand the unknown current coefficients into a Chebyshev series for achieving fast frequency sweeping. Instead of direct point-by-point simulations, it is only necessary to calculate the currents by SMoM at several Chebyshev-Gauss frequency sample points. Furthermore, the Chebyshev series are matched via the Maehly approximation to a rational function to improve the accuracy. Numerical examples show that the hybrid technique can greatly reduce the computation time without loss of accuracy.

Android platform has been the target of attackers due to its openness and increasing popularity. Android malware has explosively increased in recent years, which poses serious threats to Android security. Thus proposing efficient Android malware detection methods is curial in defeating malware. Various features extracted from static or dynamic analysis using machine learning have played an important role in malware detection recently. However, existing code obfuscation, code encryption, and dynamic code loading techniques can be employed to hinder systems that single based on static analysis, purely dynamic analysis systems cannot detect all potential code execution paths. To address these issues, we propose CoDroid, a sequence‐based hybrid Android malware detection method, which utilizes the sequences of static opcode and dynamic system call. We treat one sequence as a sentence in the natural language processing and construct a CNN–BiLSTM–Attention classifier which consists of Convolutional Neural Networks (CNNs), the Bidirectional Long Short‐Term Memory (BiLSTM) with an attention language model. We extensively evaluate CoDroid under a real‐world data set and perform comprehensive analysis against other existing related detection methods. The evaluations show the effectiveness and flexibility of CoDroid across a variety of experimental settings. [ABSTRACT FROM AUTHOR]

Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and tools. Research has spawned in various directions to yield effective malware detection mechanisms. Since malware can adopt different ways to attack and hide, accurate analysis is the key to detecting them. Like any usual mobile app, malware requires permission to take action and use device resources. There are 235 total permissions that the Android app can request on a device. Malware takes advantage of this to request unnecessary permissions, which would enable those to take malicious actions. Since permissions are critical, it is important and challenging to identify if an app is exploiting permissions and causing damage. The focus of this article is to analyze the identified studies that have been conducted with a focus on permission analysis for malware detection. With this perspective, a systematic literature review (SLR) has been produced. Several papers have been retrieved and selected for detailed analysis. Current challenges and different analyses were presented using the identified articles.