In software programs, most of the time, there is a chance for occurrence of faults in general, and exception faults in particular. Localizing those pieces of code that are responsible for a particular fault is one of the most complicated tasks, and it can produce incorrect results if done manually. Semi-automated and fully-automated techniques have been introduced to overcome this issue. However, despite recent advances in fault localization techniques, they are not necessarily applicable to Android applications because of their special characteristics such as context-awareness, use of sensors, being executable on various mobile devices, limited hardware resources, etc. To this aim, in this paper, a semi-automated hybrid method is introduced that combines static and dynamic analyses to localize exception faults in Android applications. Our evaluations of nine open source Android applications of di erent sizes with various exceptions show that the technique proposed in this paper can correctly identify root causes of the occurred exceptions. These results indicate that our proposed approach is e ective in practice in localizing exception faults in Android applications.

With the increase in the Internet's penetration rate in life and the use of this technology in all aspects, the use of mobile phones has increased as well. This, in addition to creating many benefits, has expanded and accelerated the release of some malicious programs called malware. In this study, it is attempted to use a multilayer neural network and learning machine diagnosis of zero daytime malware on smartphones. For this purpose, the standard database has been labeled with more than 15,000 samples of malware and goodware. In the pre -processing phase, the data is first performed using normalization and alignment of the data and by analyzing the main components of the feature of the selection of the feature and selected from 1183 features 215 features that have higher variances, followed by the model. A suggestion is introduced from the multilayer neural network class and the optimization algorithm based on the training and learning that apply it to the databases and compare its classification results with vector algorithms, genetic algorithm, nearest neighbor. And ... it can be seen that the neural network training increases accuracy and accuracy. The results of the use of multilayer neural network based on education and learning indicate 99% accuracy and 98% accuracy.

Aims: Breast cancer is among the most common cancer types. This study aimed to develop an Android-based detection application for assessing breast cancer risk. Materials & Methods: This quasi-experimental study utilized a research and development approach, employing a pre- and post-test design with one group. The development and field-testing phase took place between July and August 2023, involving 59 women of childbearing age purposively selected within the operational vicinity of Puskesmas Simpang IV Sipin, Jambi City, Indonesia. The application successfully underwent testing, including evaluation by media and material validators. Subsequently, a comprehension test was conducted with three respondents individually, ten individuals in a small group, and 59 participants in a large group. In the field test, data are presented descriptively, including frequencies. The Wilcoxon test was utilized to determine a causal relationship between the product’s usage and the observed impact. Findings: The development of the breast cancer risk assessment application involved several key stages, including the identification stage (comprising problem analysis, context, and literature), the application model design stage, and the material and media validation stage. The media validation process was conducted twice, with the findings yielding a score of 67, averaging 3.35 (meeting valid criteria), while material validation received an average score of 3.0 (also meeting valid criteria). A Wilcoxon test conducted on the knowledge variable revealed a significant increase, with the mean value before the intervention at 8.44 and post-intervention rising to 12.29. Conclusion: Women of childbearing age readily accept Android-based breast cancer risk detection applications, and their usage has a positive impact on increasing their knowledge.

With the widespread use of Android smartphones, the Android platform has become an attractive target for cybersecurity attackers and malware authors. Meanwhile, the growing emergence of zero-day malware has long been a major concern for cybersecurity researchers. This is because malware that has not been seen before often exhibits new or unknown behaviors, and there is no documented defense against it. In recent years, deep learning has become the dominant machine learning technique for malware detection and could achieve outstanding achievements. Currently, most deep malware detection techniques are supervised in nature and require training on large datasets of benign and malicious samples. However, supervised techniques usually do not perform well against zero-day malware. Semi-supervised and unsupervised deep malware detection techniques have more potential to detect previously unseen malware. In this paper, we present MalGAE, a novel end-to-end deep malware detection technique that leverages one-class graph neural networks to detect Android malware in a semi-supervised manner. MalGAE represents each Android application with an attributed function call graph (AFCG) to benefit the ability of graphs to model complex relationships between data. It builds a deep one-class classifier by training a stacked graph autoencoder with graph convolutional layers on benign AFCGs. Experimental results show that MalGAE can achieve good detection performance in terms of different evaluation measures.

Android has been targeted by malware developers since it has emerged as widest used operating system for smartphones and mobile devices. Android security mainly relies on user decisions regarding to installing applications (apps) by approving their requested permissions. Therefore, a systematic user assistance mechanism for making appropriate decisions can significantly improve the security of Android based devices by preventing malicious apps installation. However, the criticality of permissions and the security risk values of apps are not well determined for users in order to make correct decisions. In this study, a new metric is introduced for effective risk computation of untrusted apps based on their required permissions. The metric leverages both frequency of permission usage in malwares and rarity of them in normal apps. Based on the proposed metric, an algorithm is developed and implemented for identifying critical permissions and effective risk computation. The proposed solution can be directly used by the mobile owners to make better decisions or by Android markets to filter out suspicious apps for further examination. Empirical evaluations on real malicious and normal app samples show that the proposed metric has high malware detection rate and is superior to recently proposed risk score measurements. Moreover, it has good performance on unseen apps in term of security risk computation.

With the rapid growth of developing malwares in Android platform as the widest used mobile operating system, knowing security risk of an application (app) can be helpful for warning users regarding the use of potential malicious applications. The security risk of an Android app can be estimated using its requested permissions. In this paper, the concept of critical permissions is precisely re-defined according to the abuse of permissions by previously known Android malwares. Based on this definition and analysis of requested permissions of the large numbers of malwares and benign apps, a new criterion is proposed to measure the security risk of the apps. In this criterion, informative permissions have higher impact on the resulting measured security risk values of the apps. Experimental evaluations show the superiority of the proposed criterion with respect to previously proposed ones in terms of detection rete and generalization capability.

Android malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc. One of the most growing attacks is on Android systems due to its use in many devices worldwide. De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance. There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Hence, understand the distinct ob-jective of malware after identifying their family and category. This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families. Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families. The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work. The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods. The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.