Utilizing the Cloud to Store Hijacked Camera Images

Instant messenger, often shortened to IM is one type of communication service that allows person to have a conversation (chat) privately with others via the internet. Within the calls & messaging on Android and iOS operating systems, they both have an equal value in the terms of innovation and performance. In this work, we focus on evaluate the performance of the IM application on Android and iOS operating systems used several criteria; delay, packet size, and throughput. We use two methods of data collection; primary and secondary data, and compare the IM application using simulation methods. Input parameters for this simulation is in the stanza form of a text message, and the output consists of delay, packet size, and throughput. The results of this work indicate that the IM on iOS operating system with criteria of delay, packet size and throughput can send and receive messages faster than IM on android operating system.

With more than three million applications already in the Android marketplace, various malware detection systems based on machine learning have been proposed to prevent attacks from cybercriminals; most of these systems use static analyses to extract application features. However, many features generated by static analyses can be easily thwarted by obfuscation techniques. Therefore, several researchers have addressed this obfuscation problem with obfuscation-invariant features. However, to the best of our knowledge, no researcher has utilized deobfuscation techniques. To this end, we adopt a code deobfuscation technique with an Android malware detection system and investigate its effects. Experimental results indicate that code deobfuscation can successfully retrieve useful information concealed by obfuscation. Further, we propose interaction terms based on identified feature interactions. The proposed interaction terms aim to eliminate the interference caused by the size of the application and other features because many feature values are correlated to the size of the application. In addition, the experimental results indicate that these interaction terms have a high ranking in terms of feature importance values. Our proposed Android malware detection model achieves 99.55% accuracy and a 94.61% F1-score with the well-known Drebin dataset, which is better than the performance of previous works.

The increasing number of mobile devices using the Android operating system in the market makes these devices the first target for malicious applications. In recent years, several Android malware applications were developed to perform certain illegitimate activities and harmful actions on mobile devices. In response, specific tools and anti-virus programs used conventional signature-based methods in order to detect such Android malware applications. However, the most recent Android malware apps, such as zero-day, cannot be detected through conventional methods that are still based on fixed signatures or identifiers. Therefore, the most recently published research studies have suggested machine learning techniques as an alternative method to detect Android malware due to their ability to learn and use the existing information to detect the new Android malware apps. This paper presents the basic concepts of Android architecture, Android malware, and permission features utilized as effective malware predictors. Furthermore, a comprehensive review of the existing static, dynamic, and hybrid Android malware detection approaches is presented in this study. More significantly, this paper empirically discusses and compares the performances of six supervised machine learning algorithms, known as K-Nearest Neighbors (K-NN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR), which are commonly used in the literature for detecting malware apps.

The Android operating system holds the vast majority of the market share in smart device usage worldwide. The Android operating system, which is of interest to users, is increasing its usage rate day by day due to its open source nature and free applications. Applications can be installed on the Android operating system from official application markets and unofficial third-party environments, which poses a great risk to users' privacy and security. In this study, an attention-layered mini-ResNet model is proposed, which can detect QR code-like images created using the 100 most effective defined permission information of Android applications. In the proposed method, permission information is obtained from four different datasets with different number of applications. QR code-like images of size 10x10x1 are created by selecting effective permissions using the chi-square technique. In the proposed classification architecture, residual layers are used to avoid ignoring the residual features of the images, and attention layers are used to focus on specific regions after each residual layer. The proposed architecture has a low number of parameters and memory consumption despite adding the residual layer and the weighting operations in the attention layer. Using the proposed method, accuracy values of 96.95%, 98.34%, 98.33% and 100% were achieved, respectively, on four datasets containing applications obtained from different sources such as Androzoo, Drebin, Genome and Google Play Store. On the Mix dataset, which is a combination of four datasets, an accuracy value of 96.7% was produced with the proposed method. When 10-fold cross validation was applied to reduce the suggested bias, accuracy values of 97.50%, 98.62%, 98%, 94% and 97.61% were obtained, respectively. The success and durability of the proposed method in different environments have been tested through experiments conducted on different datasets. The results show that the proposed method exhibits better classification performance compared to classical machine learning algorithms, deep learning-based studies using permission information, and similar image-based studies.

The Balinese language which is used in daily life on the island of Bali has experienced a shift in usage so it needs to be preserved. In an effort to preserve the Balinese language and script, the Provincial Government issued Bali Governor Regulation Number 20 of 2013 and Regulation number 1 of 2018. I Made Suatjana submitted a proposal that the Balinese script be included in Unicode to make it easier to display Balinese script on digital media. Cokorda Rai Adi Pramartha developed keyboards or keyboards and an application called T@miang to facilitate typing Balinese characters on computer devices. Computer equipment is slowly becoming obsolete, replaced by mobile devices used by 53.03% of users in Indonesia, so it is necessary to build a Balinese script keyboard that can be used on mobile devices. PaTik Bali is built for mobile devices with Android operating system. PaTik Bali layout is adapted to T@miang keyboard layout. PaTik Bali version 1.0 only uses the Balinese script on each keyboard key. In version 2.0 each button on PaTik Bali has been added Latin letters because of the large number of entries on the Google Play page. Based on data recorded on the Google Play Console, there are 41,820 devices that have installed the Balinese Keyboard (PaTik Bali) application, 99.19% are from Indonesia. In general, the PaTik Bali application can be accepted in the community, this can be seen from the average rating given by users on Google Play which is 4.27 on a scale of 5. However, some users complain about the incorrect form of letters. The PaTik Bali application uses fonts that have been embedded in the Android operating system so that it can be used as a keyboard in other applications. This font still has enough problems. To fix this problem, it is necessary to develop the Balinese font then ask Google as an Android developer to enter the new font into the Android operating system.

With the growing popularity of autonomous vehicles, ensuring their safety has become a significant concern. Vehicle manufacturers have incorporated the Android operating system to enhance user convenience. However, the Android operating system's diversity and vulnerability may significantly impact autonomous vehicles because these natures lead to a critical threat in autonomous vehicle security: Android malware. The complex nature of multi‐data sources fusion in autonomous driving systems has resulted in low detection efficiency and accuracy for Android malware. Given the security requirements for autonomous‐driving systems, this article presents an Android malware classification method centered around the multifeature fusion and deep learning architecture designed to detect and classify Android malware within autonomous‐driving systems. The proposed MUDROID framework relies on the Android permission mechanism and behavioral feature to formulate a malware classification model. Comprising four essential components—feature extraction, representation, multifeature fusion, and classifier training, MUDROID leverages the advantages of CNN and GCN combination model and is adept at extracting and interpreting multidimensional features, consequently constructing a multifeature fusion classifier to amplify the efficiency and accuracy of Android malware detection and classification. The experiment results support MUDROID's superior classification performance, with an impressive Android malware detection accuracy rate of 98.69% and family classification (95.42%). The proposed method can detect and classify Android malware effectively, thus elevating the security and robustness of the self‐driving system. Additionally, the approach in this article also addresses feature representation issues in detecting malware variants, facilitating the recognition of Android malware variants across diverse platforms.

With approximately 2 billion active devices, the Android operating system tops all other operating systems in terms of the number of devices using it. Android has gained wide popularity not only as a smartphone operating system, but also as an operating system for vehicles, tablets, smart appliances, and Internet of Things devices. Consequently, security challenges have arisen with the rapid adoption of the Android operating system. Thousands of malicious applications have been created and are being downloaded by unsuspecting users. This paper presents a lightweight Android malware detection system based on explainable machine learning. The proposed system uses the features extracted from applications to identify malicious and benign malware. The proposed system is tested, showing an accuracy exceeding 98% while maintaining its small footprint on the device. In addition, the classifier model is explained using Shapley Additive Explanation (SHAP) values.

As mobile devices and applications continue to grow in popularity worldwide, so do the rise of cybersecurity attacks and threats to daily users of both Android and iOS devices on a widespread basis. These attacks not only target mobile devices at the application level but also at the Operating System (OS) device level and Application Programming Interface (API) level extending the Internet of Things (IoT). This paper will address the security vulnerabilities in Android Operating System at the intersection of APIs, device OS, and application-level vulnerabilities by identifying an intersection of commonalities to create mitigation strategies and best practices for securing mobile devices for organizations. The study recommends the adoption of processes of authentication on mobile devices and web-based log-ins as well as access control mechanisms that can mitigate the overlapping security challenges of APIs, mobile devices OS and applications using optimal resources to enhance security. This was achieved by analyzing the threats and vulnerabilities of API.KeywordsAPIsSecurity vulnerabilitiesMobile OSAndroidiOS

This paper discuss the main security topic in mobile security area and this topic is protect user against the leakage of data. This work primarily contains the proposal of concept of dynamic permission mechanism for Android operating system. This mechanism deals with assignment or enforcement permissions to the application according to files that the application works with. Application has set of permissions that can use, but if the user opens confident files the application permissions should change its permission set and there should not be possible leakage of this secret data. The permissions set should be stricter according to opened confidential file or more open (without restriction) if the file is not secret file. The concept proposes the solution for protecting this data leakage. Idea covers rule that user should be avoided of change this permissions himself, but this behavior should be dynamic, automatic and independent. This proposal is mainly aimed to Android operating system, but the concept can be applied to other mobile platforms with some implementation changes.

Mobile computational offloading offers a solution to extend the computational power of a mobile device allowing it to run heavyweight activities/tasks. This turns low-end mobile devices into a capable machine of running heavyweight applications that require special processing power. Code offloading is achieved through migration. Migrating code from a local machine (i.e. mobile device) to the cloud where the code is actually executed is challenging. Questions such as when and how migration takes place are critical. Quite a few offloading frameworks are proposed in the literature. They are, mostly, mostly not efficient due to the lack of a standard offloading architecture, some did not propose an architecture at all. The abundance of Quality of Service (QoS) component is another concern. This paper introduces a layered approach to mobile computation offloading called LAMCO. LAMCO is implemented in the Android operating system and the run-time performance of the framework is evaluated in this paper. One advantage of LAMCO is that it does not require any modifications in the underlying operating system as the framework is dynamically installed and updated while the operating system is intact.

One of the main directions of information systems development is to increase the efficiency of collecting, processing and exchanging information through the introduction of modern data transfer technologies, automated remote monitoring and control. The cornerstone of this concept is mobile devices that solve the issue of operational data exchange and processing. Modern mobile services used including the exchange and processing of personalized, banking and critical data are the result of the steady increase in the number of crimes in the field of information security in relation to and using mobile devices. The widespread use of these devices for access to protected information contained in information systems has given special importance to the issue of information security.The subject of this research is to assess the current state of information security mechanisms for mobile operating systems that form the basis of the structure of episodic distributed mobile networks. The Android OS (operating system) was used as an example. The article discusses the problems of the development of a mobile ecosystem and methods aimed at solving them, the main vectors of malicious impact, ways of countering the means of static and dynamic analysis and modern protection mechanisms.

The access and the use of the global navigation satellite system (GNSS) pseudo-range and carrier-phase measurements mobile devices as smartphones and tablets with an Android operating system has transformed the concept of accurate positioning with mobile devices. In this work, the comparison of positioning performances obtained with a smartphone and an external mass-market GNSS receiver both in real-time and post-processing is made. Particular attention is also paid to accuracy and precision of positioning results, also analyzing the possibility of estimating the phase ambiguities as integer values (fixed positioning) that it is still challenging for mass-market devices. The precisions and accuracies obtained with the mass-market receiver were about 5 cm and 1 cm both for real-time and post-processing solutions, respectively, while those obtained with a smartphone were slightly worse (few meters in some cases) due to the noise of its measurements.

Android malware detection has been a popular research topic due to non-negligible amount of malware targeting the Android operating system. In particular, the naive Bayes generative classifier is a common technique widely adopted in many papers. However, we found that the naive Bayes classifier performs badly in Contagio Malware Dump dataset, which could result from the assumption that no feature dependency exists. In this paper, we propose a lightweight method for Android malware detection, which improves the performance of Bayesian classification on the Contagio Malware Dump dataset. It performs static analysis to gather malicious features from an application, and applies principal component analysis to reduce the dependencies among them. With the hidden naive Bayes model, we can infer the identity of the application. In an evaluation with 15,573 normal applications and 3,150 malicious samples, our work detects 94.5% of the malware with a false positive rate of 1.0%. The experiment also shows that our approach is feasible on smartphones.

Cybersecurity threats on mobile devices are also growing substantially with the subsequent rise in the usage of smartphones and mobile applications. Cybercriminals inevitably have expanded their malicious operations to Google’s Android mobile operating system. Due to the limitations of traditional signature-based approaches and the constant evolution of new malware, current malware detection systems are turned to be empowered by intelligent machine learning models. In this aspect, malware defense techniques strive to integrate data science and cybersecurity. This paper presents an Android malware detection system that incorporates the Leave One Feature Out (LOFO) approach and uses tree-based learning models to classify malware applications based on the top selected features. The experimental evaluation is conducted on the DREBIN dataset to demonstrate the efficacy of the proposed method. The prediction accuracy of the XGBoost classifier is observed to outperform other tree-based models, but with higher computational costs.

Mobile devices have evolved from simple devices, which are used for a phone call and SMS messages to smartphone devices that can run third party applications. Nowadays, malicious software, which is also known as malware, imposes a larger threat to these mobile devices. Recently, many news items were posted about the increase of the Android malware. There were a lot of Android applications pulled from the Android Market because they contained malware. The vulnerabilities of those Applications or Android operating systems are being exploited by the attackers who got the capability of penetrating into the mobile systems without user authorization causing compromise the confidentiality, integrity and availability of the applications and the user. This paper, it gave an update to the work done in the project. Moreover, this paper focuses on the Android Operating System and aim to detect existing Android malware. It has a dataset that contained 104 malware samples. This Paper chooses several malware from the dataset and attempting to analyze them to understand their installation methods and activation. In addition, it evaluates the most popular existing anti-virus software to see if these 104 malware could be detected.