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2021-03-22
Sai, C. C., Prakash, C. S., Jose, J., Mana, S. C., Samhitha, B. K..  2020.  Analysing Android App Privacy Using Classification Algorithm. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184). :551–555.
The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.
2021-03-09
Yerima, S. Y., Alzaylaee, M. K..  2020.  Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1—8.

Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between botnet apps and normal apps. The trained botnet detection model was evaluated on a set of 6,802 real applications containing 1,929 botnets from the publicly available ISCX botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android botnet detection.

2021-02-23
Gamba, J., Rashed, M., Razaghpanah, A., Tapiador, J., Vallina-Rodriguez, N..  2020.  An Analysis of Pre-installed Android Software. 2020 IEEE Symposium on Security and Privacy (SP). :1039—1055.

The open-source nature of the Android OS makes it possible for manufacturers to ship custom versions of the OS along with a set of pre-installed apps, often for product differentiation. Some device vendors have recently come under scrutiny for potentially invasive private data collection practices and other potentially harmful or unwanted behavior of the preinstalled apps on their devices. Yet, the landscape of preinstalled software in Android has largely remained unexplored, particularly in terms of the security and privacy implications of such customizations. In this paper, we present the first large- scale study of pre-installed software on Android devices from more than 200 vendors. Our work relies on a large dataset of real-world Android firmware acquired worldwide using crowd-sourcing methods. This allows us to answer questions related to the stakeholders involved in the supply chain, from device manufacturers and mobile network operators to third- party organizations like advertising and tracking services, and social network platforms. Our study allows us to also uncover relationships between these actors, which seem to revolve primarily around advertising and data-driven services. Overall, the supply chain around Android's open source model lacks transparency and has facilitated potentially harmful behaviors and backdoored access to sensitive data and services without user consent or awareness. We conclude the paper with recommendations to improve transparency, attribution, and accountability in the Android ecosystem.

2021-01-28
Kalaiyarasi, G., Balaji, K., Narmadha, T., Naveen, V..  2020.  E-Voting System In Smart Phone Using Mobile Application. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). :1466—1469.

The development in the web technologies given growth to the new application that will make the voting process very easy and proficient. The E-voting helps in providing convenient, capture and count the votes in an election. This project provides the description about e-voting using an Android platform. The proposed e-voting system helps the user to cast the vote without visiting the polling booth. The application provides authentication measures in order to avoid fraud voters using the OTP. Once the voting process is finished the results will be available within a fraction of seconds. All the casted vote count is encrypted using AES256 algorithm and stored in the database in order to avoid any outbreaks and revelation of results by third person other than the administrator.

Siddiquie, K., Shafqat, N., Masood, A., Abbas, H., Shahid, W. b.  2020.  Profiling Vulnerabilities Threatening Dual Persona in Android Framework. 2019 International Conference on Advances in the Emerging Computing Technologies (AECT). :1—6.

Enterprises round the globe have been searching for a way to securely empower AndroidTM devices for work but have spurned away from the Android platform due to ongoing fragmentation and security concerns. Discrepant vulnerabilities have been reported in Android smartphones since Android Lollipop release. Smartphones can be easily hacked by installing a malicious application, visiting an infectious browser, receiving a crafted MMS, interplaying with plug-ins, certificate forging, checksum collisions, inter-process communication (IPC) abuse and much more. To highlight this issue a manual analysis of Android vulnerabilities is performed, by using data available in National Vulnerability Database NVD and Android Vulnerability website. This paper includes the vulnerabilities that risked the dual persona support in Android 5 and above, till Dec 2017. In our security threat analysis, we have identified a comprehensive list of Android vulnerabilities, vulnerable Android versions, manufacturers, and information regarding complete and partial patches released. So far, there is no published research work that systematically presents all the vulnerabilities and vulnerability assessment for dual persona feature of Android's smartphone. The data provided in this paper will open ways to future research and present a better Android security model for dual persona.

2020-12-17
Sun, P., Garcia, L., Salles-Loustau, G., Zonouz, S..  2020.  Hybrid Firmware Analysis for Known Mobile and IoT Security Vulnerabilities. 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :373—384.

Mobile and IoT operating systems–and their ensuing software updates–are usually distributed as binary files. Given that these binary files are commonly closed source, users or businesses who want to assess the security of the software need to rely on reverse engineering. Further, verifying the correct application of the latest software patches in a given binary is an open problem. The regular application of software patches is a central pillar for improving mobile and IoT device security. This requires developers, integrators, and vendors to propagate patches to all affected devices in a timely and coordinated fashion. In practice, vendors follow different and sometimes improper security update agendas for both mobile and IoT products. Moreover, previous studies revealed the existence of a hidden patch gap: several vendors falsely reported that they patched vulnerabilities. Therefore, techniques to verify whether vulnerabilities have been patched or not in a given binary are essential. Deep learning approaches have shown to be promising for static binary analyses with respect to inferring binary similarity as well as vulnerability detection. However, these approaches fail to capture the dynamic behavior of these systems, and, as a result, they may inundate the analysis with false positives when performing vulnerability discovery in the wild. In particular, they cannot capture the fine-grained characteristics necessary to distinguish whether a vulnerability has been patched or not. In this paper, we present PATCHECKO, a vulnerability and patch presence detection framework for executable binaries. PATCHECKO relies on a hybrid, cross-platform binary code similarity analysis that combines deep learning-based static binary analysis with dynamic binary analysis. PATCHECKO does not require access to the source code of the target binary nor that of vulnerable functions. We evaluate PATCHECKO on the most recent Google Pixel 2 smartphone and the Android Things IoT firmware images, within which 25 known CVE vulnerabilities have been previously reported and patched. Our deep learning model shows a vulnerability detection accuracy of over 93%. We further prune the candidates found by the deep learning stage–which includes false positives–via dynamic binary analysis. Consequently, PATCHECKO successfully identifies the correct matches among the candidate functions in the top 3 ranked outcomes 100% of the time. Furthermore, PATCHECKO's differential engine distinguishes between functions that are still vulnerable and those that are patched with an accuracy of 96%.

2020-11-23
Wang, X., Li, J..  2018.  Design of Intelligent Home Security Monitoring System Based on Android. 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). :2621–2624.
In view of the problem that the health status and safety monitoring of the traditional intelligent home are mainly dependent on the manual inspection, this paper introduces the intelligent home-based remote monitoring system by introducing the Internet-based Internet of Things technology into the intelligent home condition monitoring and safety assessment. The system's Android remote operation based on the MVP model to develop applications, the use of neural networks to deal with users daily use of operational data to establish the network data model, combined with S3C2440A microcontrollers in the gateway to the embedded Linux to facilitate different intelligent home drivers development. Finally, the power line communication network is used to connect the intelligent electrical appliances to the gateway. By calculating the success rate of the routing nodes, the success rate of the network nodes of 15 intelligent devices is 98.33%. The system can intelligent home many electrical appliances at the same time monitoring, to solve the system data and network congestion caused by the problem can not he security monitoring.
2020-11-17
Jaiswal, M., Malik, Y., Jaafar, F..  2018.  Android gaming malware detection using system call analysis. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). :1—5.
Android operating systems have become a prime target for attackers as most of the market is currently dominated by Android users. The situation gets worse when users unknowingly download or sideload cloning applications, especially gaming applications that look like benign games. In this paper, we present, a dynamic Android gaming malware detection system based on system call analysis to classify malicious and legitimate games. We performed the dynamic system call analysis on normal and malicious gaming applications while applications are in execution state. Our analysis reveals the similarities and differences between benign and malware game system calls and shows how dynamically analyzing the behavior of malicious activity through system calls during runtime makes it easier and is more effective to detect malicious applications. Experimental analysis and results shows the efficiency and effectiveness of our approach.
2020-11-04
Peruma, A., Malachowsky, S., Krutz, D..  2018.  Providing an Experiential Cybersecurity Learning Experience through Mobile Security Labs. 2018 IEEE/ACM 1st International Workshop on Security Awareness from Design to Deployment (SEAD). :51—54.

The reality of today's computing landscape already suffers from a shortage of cybersecurity professionals, and this gap only expected to grow. We need to generate interest in this STEM topic early in our student's careers and provide teachers the resources they need to succeed in addressing this gap. To address this shortfall we present Practical LAbs in Security for Mobile Applications (PLASMA), a public set of educational security labs to enable instruction in creation of secure Android apps. These labs include example vulnerable applications, information about each vulnerability, steps for how to repair the vulnerabilities, and information about how to confirm that the vulnerability has been properly repaired. Our goal is for instructors to use these activities in their mobile, security, and general computing courses ranging from secondary school to university settings. Another goal of this project is to foster interest in security and computing through demonstrating its importance. Initial feedback demonstrates the labs' positive effects in enhancing student interest in cybersecurity and acclaim from instructors. All project activities may be found on the project website: http://www.TeachingMobileSecurity.com

2020-11-02
Ermakov, Anton D., Prokopenko, Svetlana A., Yevtushenko, Nina V..  2018.  Security Checking Experiments with Mobile Services. 2018 19th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM). :139—141.
In this paper, we continue to investigate the problem of software security. The problem is to check if software under test has some vulnerabilities such as exceeding of admissible values of input/output parameters or internal variables or can reach states where the software (service) behavior is not defined. We illustrate by experiments that the well-known verifier Java Path Finder (JPF) can be utilized for this purpose. We apply JPF-mobile to Android applications and results of security checking experiments are presented.
2020-10-29
Xylogiannopoulos, Konstantinos F., Karampelas, Panagiotis, Alhajj, Reda.  2019.  Text Mining for Malware Classification Using Multivariate All Repeated Patterns Detection. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). :887—894.

Mobile phones have become nowadays a commodity to the majority of people. Using them, people are able to access the world of Internet and connect with their friends, their colleagues at work or even unknown people with common interests. This proliferation of the mobile devices has also been seen as an opportunity for the cyber criminals to deceive smartphone users and steel their money directly or indirectly, respectively, by accessing their bank accounts through the smartphones or by blackmailing them or selling their private data such as photos, credit card data, etc. to third parties. This is usually achieved by installing malware to smartphones masking their malevolent payload as a legitimate application and advertise it to the users with the hope that mobile users will install it in their devices. Thus, any existing application can easily be modified by integrating a malware and then presented it as a legitimate one. In response to this, scientists have proposed a number of malware detection and classification methods using a variety of techniques. Even though, several of them achieve relatively high precision in malware classification, there is still space for improvement. In this paper, we propose a text mining all repeated pattern detection method which uses the decompiled files of an application in order to classify a suspicious application into one of the known malware families. Based on the experimental results using a real malware dataset, the methodology tries to correctly classify (without any misclassification) all randomly selected malware applications of 3 categories with 3 different families each.

2020-09-21
Takahashi, Hironao, Lakhani, Uzair.  2019.  Multiple Layered Security Analyses Method for Cryptocurrency Exchange Servicers. 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). :71–73.
Internet is a common method of trading business today. The usage of cryptocurrencies has increased these days and it has become a trend to utilize them. Cryptocurrency exchange servicers provide different smartphone apps that unfortunately may become the target of malicious attacks. This paper focuses on how it achieves highest security and proposes the multiple layered security analyses method for cryptocurrency exchange servicers.
2020-08-10
Zhang, Xinman, He, Tingting, Xu, Xuebin.  2019.  Android-Based Smartphone Authentication System Using Biometric Techniques: A Review. 2019 4th International Conference on Control, Robotics and Cybernetics (CRC). :104–108.
As the technological progress of mobile Internet, smartphone based on Android OS accounts for the vast majority of market share. The traditional encryption technology cannot resolve the dilemma in smartphone information leakage, and the Android-based authentication system in view of biometric recognition emerge to offer more reliable information assurance. In this paper, we summarize several biometrics providing their attributes. Furthermore, we also review the algorithmic framework and performance index acting on authentication techniques. Thus, typical identity authentication systems including their experimental results are concluded and analyzed in the survey. The article is written with an intention to provide an in-depth overview of Android-based biometric verification systems to the readers.
Rodinko, Mariia, Oliynykov, Roman.  2019.  Comparing Performances of Cypress Block Cipher and Modern Lighweight Block Ciphers on Different Platforms. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S T). :113–116.

The paper is devoted to the comparison of performance of prospective lightweight block cipher Cypress with performances of the known modern lightweight block ciphers such as AES, SPECK, SPARX etc. The measurement was done on different platforms: Windows, Linux and Android. On all platforms selected, the block cipher Cypress showed the best results. The block cipher Cypress-256 showed the highest performance on Windows x32 (almost 3.5 Gbps), 64-bit Linux (over 8 Gbps) and Android (1.3 Gbps). On Windows x64 the best result was obtained by Cypress- 512 (almost 5 Gbps).

2020-07-30
Bays, Jason, Karabiyik, Umit.  2019.  Forensic Analysis of Third Party Location Applications in Android and iOS. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :1—6.
Location sharing applications are becoming increasingly common. These applications allow users to share their own locations and view contacts’ current locations on a map. Location applications are commonly used by friends and family members to view Global Positioning System (GPS) location of an individual, but valuable forensic evidence may exist in this data when stored locally on smartphones. This paper aims to discover forensic artifacts from two popular third-party location sharing applications on iOS and Android devices. Industry standard mobile forensic suites are utilized to discover if any locally stored data could be used to assist investigations reliant on knowing the past location of a suspect. Security issues raised regarding the artifacts found during our analysis is also discussed.
2020-07-27
Pandey, Ashutosh, Khan, Rijwan, Srivastava, Akhilesh Kumar.  2018.  Challenges in Automation of Test Cases for Mobile Payment Apps. 2018 4th International Conference on Computational Intelligence Communication Technology (CICT). :1–4.
Software Engineering is a field of new challenges every day. With every passing day, new technologies emerge. There was an era of web Applications, but the time has changed and most of the web Applications are available as Mobile Applications as well. The Mobile Applications are either android based or iOS based. To deliver error free, secure and reliable Application, it is necessary to test the Applications properly. Software testing is a phase of software development life cycle, where we test an Application in all aspects. Nowadays different type of tools are available for testing an Application automatically but still we have too many challenges for applying test cases on a given Application. In this paper the authors will discuss the challenges of automation of test cases for a Mobile based payment Application.
Dar, Muneer Ahmad, Nisar Bukhari, Syed, Khan, Ummer Iqbal.  2018.  Evaluation of Security and Privacy of Smartphone Users. 2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). :1–4.

The growing use of smart phones has also given opportunity to the intruders to create malicious apps thereby the security and privacy concerns of a novice user has also grown. This research focuses on the privacy concerns of a user who unknowingly installs a malicious apps created by the programmer. In this paper we created an attack scenario and created an app capable of compromising the privacy of the users. After accepting all the permissions by the user while installing the app, the app allows us to track the live location of the Android device and continuously sends the GPS coordinates to the server. This spying app is also capable of sending the call log details of the user. This paper evaluates two leading smart phone operating systems- Android and IOS to find out the flexibility provided by the two operating systems to their programmers to create the malicious apps.

2020-05-18
Liu, Xueqing.  2018.  Assisting the Development of Secure Mobile Apps with Natural Language Processing. 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). :279–280.
With the rapid growth of mobile devices and mobile apps, mobile has surpassed desktop and now has the largest worldwide market share [1]. While such growth brings in more opportunities, it also poses new challenges in security. Among the challenges, user privacy protection has drawn tremendous attention in recent years, especially after the Facebook-Cambridge Analytica data scandal in April 2018 [2].
2020-03-23
Alzahrani, Abdulrahman, Alshahrani, Hani, Alshehri, Ali, Fu, Huirong.  2019.  An Intelligent Behavior-Based Ransomware Detection System For Android Platform. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :28–35.

Malware variants exhibit polymorphic attacks due to the tremendous growth of the present technologies. For instance, ransomware, an astonishingly growing set of monetary-gain threats in the recent years, is peculiarized as one of the most treacherous cyberthreats against innocent individuals and businesses by locking their devices and/or encrypting their files. Many proposed attempts have been introduced by cybersecurity researchers aiming at mitigating the epidemic of the ransomware attacks. However, this type of malware is kept refined by utilizing new evasion techniques, such as sophisticated codes, dynamic payloads, and anti-emulation techniques, in order to survive against detection systems. This paper introduces RanDetector, a new automated and lightweight system for detecting ransomware applications in Android platform based on their behavior. In particular, this detection system investigates the appearance of some information that is related to ransomware operations in an inspected application before integrating some supervised machine learning models to classify the application. RanDetector is evaluated and tested on a dataset of more 450 applications, including benign and ransomware. Hence, RanDetector has successfully achieved more that 97.62% detection rate with nearly zero false positive.

2019-11-11
Wang, Xiaoyin, Qin, Xue, Bokaei Hosseini, Mitra, Slavin, Rocky, Breaux, Travis D., Niu, Jianwei.  2018.  GUILeak: Tracing Privacy Policy Claims on User Input Data for Android Applications. 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE). :37–47.
The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information. In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app's privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.
2019-03-28
He, F., Zhang, Y., Liu, H., Zhou, W..  2018.  SCPN-Based Game Model for Security Situational Awareness in the Intenet of Things. 2018 IEEE Conference on Communications and Network Security (CNS). :1-5.
Internet of Things (IoT) is characterized by various of heterogeneous devices that facing numerous threats, which makes modeling security situation of IoT still a certain challenge. This paper defines a Stochastic Colored Petri Net (SCPN) for IoT-based smart environment and then proposes a Game model for security situational awareness. All possible attack paths are computed by the SCPN, and antagonistic behavior of both attackers and defenders are taken into consideration dynamically according to Game Theory (GT). Experiments on two typical attack scenarios in smart home environment demonstrate the effectiveness of the proposed model. The proposed model can form a macroscopic trend curve of the security situation. Analysis of the results shows the capabilities of the proposed model in finding vulnerable devices and potential attack paths, and even facilitating the choice of defense strategy. To the best of our knowledge, this is the first attempt to use Game Theory in the IoT-based SCPN to establish a security situational awareness model for a complex smart environment.
2019-02-14
Kelkar, S., Kraus, T., Morgan, D., Zhang, J., Dai, R..  2018.  Analyzing HTTP-Based Information Exfiltration of Malicious Android Applications. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1642-1645.

Exfiltrating sensitive information from smartphones has become one of the most significant security threats. We have built a system to identify HTTP-based information exfiltration of malicious Android applications. In this paper, we discuss the method to track the propagation of sensitive information in Android applications using static taint analysis. We have studied the leaked information, destinations to which information is exfiltrated, and their correlations with types of sensitive information. The analysis results based on 578 malicious Android applications have revealed that a significant portion of these applications are interested in identity-related sensitive information. The vast majority of malicious applications leak multiple types of sensitive information. We have also identified servers associated with three country codes including CN, US, and SG are most active in collecting sensitive information. The analysis results have also demonstrated that a wide range of non-default ports are used by suspicious URLs.

Xu, Z., Shi, C., Cheng, C. C., Gong, N. Z., Guan, Y..  2018.  A Dynamic Taint Analysis Tool for Android App Forensics. 2018 IEEE Security and Privacy Workshops (SPW). :160-169.

The plethora of mobile apps introduce critical challenges to digital forensics practitioners, due to the diversity and the large number (millions) of mobile apps available to download from Google play, Apple store, as well as hundreds of other online app stores. Law enforcement investigators often find themselves in a situation that on the seized mobile phone devices, there are many popular and less-popular apps with interface of different languages and functionalities. Investigators would not be able to have sufficient expert-knowledge about every single app, sometimes nor even a very basic understanding about what possible evidentiary data could be discoverable from these mobile devices being investigated. Existing literature in digital forensic field showed that most such investigations still rely on the investigator's manual analysis using mobile forensic toolkits like Cellebrite and Encase. The problem with such manual approaches is that there is no guarantee on the completeness of such evidence discovery. Our goal is to develop an automated mobile app analysis tool to analyze an app and discover what types of and where forensic evidentiary data that app generate and store locally on the mobile device or remotely on external 3rd-party server(s). With the app analysis tool, we will build a database of mobile apps, and for each app, we will create a list of app-generated evidence in terms of data types, locations (and/or sequence of locations) and data format/syntax. The outcome from this research will help digital forensic practitioners to reduce the complexity of their case investigations and provide a better completeness guarantee of evidence discovery, thereby deliver timely and more complete investigative results, and eventually reduce backlogs at crime labs. In this paper, we will present the main technical approaches for us to implement a dynamic Taint analysis tool for Android apps forensics. With the tool, we have analyzed 2,100 real-world Android apps. For each app, our tool produces the list of evidentiary data (e.g., GPS locations, device ID, contacts, browsing history, and some user inputs) that the app could have collected and stored on the devices' local storage in the forms of file or SQLite database. We have evaluated our tool using both benchmark apps and real-world apps. Our results demonstrated that the initial success of our tool in accurately discovering the evidentiary data.

2019-01-31
Zhang, H., Chen, L., Liu, Q..  2018.  Digital Forensic Analysis of Instant Messaging Applications on Android Smartphones. 2018 International Conference on Computing, Networking and Communications (ICNC). :647–651.

In this paper, we discuss the digital forensic procedure and techniques for analyzing the local artifacts from four popular Instant Messaging applications in Android. As part of our findings, the user chat messages details and contacts were investigated for each application. By using two smartphones with different brands and the latest Android operating systems as experimental objects, we conducted digital investigations in a forensically sound manner. We summarize our findings regarding the different Instant Messaging chat modes and the corresponding encryption status of artifacts for each of the four applications. Our findings can be helpful to many mobile forensic investigations. Additionally, these findings may present values to Android system developers, Android mobile app developers, mobile security researchers as well as mobile users.

Kumbhar, S. S., Lee, Y., Yang, J..  2018.  Hybrid Encryption for Securing SharedPreferences of Android Applications. 2018 1st International Conference on Data Intelligence and Security (ICDIS). :246–249.

Most mobile applications generate local data on internal memory with SharedPreference interface of an Android operating system. Therefore, many possible loopholes can access the confidential information such as passwords. We propose a hybrid encryption approach for SharedPreferences to protect the leaking confidential information through the source code. We develop an Android application and store some data using SharedPreference. We produce different experiments with which this data could be accessed. We apply Hybrid encryption approach combining encryption approach with Android Keystore system, for providing better encryption algorithm to hide sensitive data.