Popular Instant Messaging Applications Research Articles

Comparative Study and Analysis of Forensic Artifacts of WhatsApp and Telegram on Android Devices

Numerous instant messaging are available for mobile devices as a cheaper alternative over operator-based text messaging via SMS. Furthermore, instant messaging allow the user to exchange textual messages, images, audio, and videos. However, the ease offered by instant messaging also had a negative impact including making instant messaging as a criminal land. This paper focuses on conducting forensic data analysis of two popular instant messaging applications on Android smartphones; WhatsApp and Telegram. In this analysis, we use open-source tools and software applied on non-rooted Android devices. By using the result, an analyst will be able to read, and reconstruct the chronology of the messages and the list of contact and also know the difference in data structures obtained from these two instant messaging applications.

Signature identification and user activity analysis on WhatsApp Web through network data

WhatsApp messenger is a popular instant messaging application that employs end-to-end encryption for communication. WhatsApp Web is the browser-based implementation of WhatsApp messenger. Users of WhatsApp communicate securely using SSL protocol. Encryption and use of common port for communication by multiple applications poses challenge in traffic classification for application identification. It is highly needed to analyze the network traffic for the purpose of QoS, Intrusion Detection and application specific traffic classification. In this paper, we have done traffic analysis on the network packets captured through data transfer in whatsApp web. In the result, we have explored the user activities such as message texting, contact sharing, voice message, location sharing, media transfer and status viewing. Packet level traffic analysis of user activities reveal patterns in the encrypted SSL communication. This pattern is identified across SSL packet lengths for WhatsApp media transfer and voice message communication. Other important features WhatsApp is the ability to view the status of the message being sent. We have identified the read and unread message status in these data packets by exposing signatures in the network layer. These signatures are identified with the help of the SSL lengths in the TLS header information of WhatsApp Web network traffic traces. Various other information on WhatsApp traffic presented in our study is relevant to the version of WhatsApp Web v0.3.2386. Also in this work machine learning based classifier is trained to classify the encrypted malicious, normal network traffic. From the results it is clear that SVM classifier gives the highest accuracy of 0.9666.

Rancang Bangun Kamus Digital Berbasis Chatbot Menggunakan Pendekatan Pattern Matching

The rapid development of technology today has had an impact on various fields of life, one of which is in the world of education. The application of information technology in the learning process at school or on campus has become commonplace. Smart devices such as smartphones connected to the internet can help the learning process and help encourage the communication process between educational stakeholders. In online learning, the process of communication and interaction between students and teachers switches through instant messaging providers. Taken from the 2019 digital report data issued by We Are Social Hotsuit, it is stated that the most popular instant messaging application in Indonesia is WhatsApp, where 83% of internet users in Indonesia are WhatsApp users, with this data making Indonesia one of the countries with the largest number of users in the world. Based on this, to support the performance of teachers in delivering learning materials, as well as provide faster access in searching for the required terms, this study built a digital chatbot-based dictionary that was embedded in the instant messaging application as the most popular message delivery medium in Indonesia. The process of building a chatbot-based digital dictionary in this study was carried out using the pattern matching method, which is a method included in the study of artificial intelligence that recognizes input from users to be matched with data stored in the database, and the appropriate response will be returned to the user. The subject content that is sampled in making this digital dictionary is physics. The result of this study is the existence of a chatbot-based digital dictionary that can assist students in finding definitions of various terms and formulas related to physics material, given by the teacher which students can access at any time as long as they are connected to the internet.

Retrieving deleted records from Telegram

One of the most popular instant messaging applications and platforms is Telegram. In this paper, an investigation into digital forensics on Telegram is provided, deployed on contemporary android mobile phones. Due to its structure complexity, it is very challenging for forensic tools and software to properly decode its data during a forensic examination, and, especially, when it comes to deleted records retrieval. For the analysis, a realistic scenario was implemented by exchanging thousands of messages and media files among four active Telegram users, while trying to recover content from deleted text messages and exchanged picture files. Abundance of modern and up to date forensic software and tools were utilized to verify and crosscheck the results.

Forensic Authentication of WhatsApp Messenger Using the Information Retrieval Approach

The development of telecommunications has increased very rapidly since the internet-based instant messaging service has spread rapidly to Indonesia. WhatsApp is the most popular instant messaging application compared to other instant messaging services, according to the statista website users of WhatsApp services in 2018 showed significant growth by gathering 1.5 billion monthly active users or monthly active users (MAU). That number increased 14 percent compared to MAU WhatsApp in July 2017 which amounted to 1.3 billion. Daily active users aka DAU are in the range of one billion. WhatsApp handles more than 60 billion message exchanges between users around the world. This growth is predicted to continue to increase, along with the wider internet penetration. Along with WhatsApp updates with various features embedded in this application including Web-based Whatsapp for computers, this feature makes it easier for users to share data and can be synchronized with their smartphone or user's computer. Besides the positive side found in the application, WhatsApp also provides a security gap for user privacy, one of which is tapping conversations involving both smartphone and computer devices. The handling of crimes involving digital devices needs to be emphasized so that they can help the judicial process of the effects they have caused Mobile Forensics Investigation also took part in suppressing the misuse of WhatsApp's instant messaging service features, including investigating the handling of cases of WhatsApp conversations through a series of standard steps according to digital forensics procedures. Exploration of evidence (digital evidence) WhatsApp conversations will be a reference to the crime of telecommunication tapping which will then be carried out forensic investigation report involving evidence of the smartphone and computer of the victim. Keywords: Authentication, Mobile Forensics, Instant Messenger, and WhatsApp Messenger.

Proposal of Machine Learning Approach for Identification of Instant Messaging

Identification of Internet protocol from either raw network traffic or either network flows plays a crucial role at maintaining and improving the security of computer systems. A significant amount of research is carried out while exploiting a variety of identification techniques. Although certain level in success at detection of network protocols for unencrypted traffic has been achieved, accuracy and performance is rather poor for encrypted traffic. Considering technological trends, new and existing applications have been adopted to use encryption mechanism to protect information and privacy. Therefore, classification of encrypted network traffic is mandatory for ensuring security. Moreover, while performing network forensic investigation, labelling of network protocols/applications is a must to accomplish. In this study, we propose a method to automatically identify instant messaging applications from raw network traffic. To this end, we first extract flow based static features from network capture and then apply machine learning algorithms. The proposed method is evaluated with fairly large dataset. The dataset compromise of publicly available NISM dataset and the network traffic of 9 popular instant messaging applications collected in a controlled environment. The dataset overall contains 716607network flows belonging to 20 application categories. The proposed method classifies network flows of instant messaging applications into their corresponding application categories with the accuracy over 0.99 and F1-score of 0.99.

Media messaging in diagnosis of acute CXR pathology: an interobserver study among residents.

The objectives of the study were to determine whether diagnostic accuracy and reliability by on-call teams is affected by communicating chest radiograph (CXR) images via instant messaging on smartphones in comparison to viewing on a workstation. 12 residents viewed 100 CXR images each with a 24% positive rate for significant or acute findings sent to their phones via a popular instant messaging application and reported their findings if any. After an interval of 42days they viewed the original DICOM images on personal computers and again reported their findings. There were no statistically significant differences in accuracy, agreement, sensitivity, specificity, positive predictive value or negative predictive value between desktop workstation viewed images and images sent via the mobile application. Media messaging is a useful adjunct for quick second opinions on radiological images, without significant decay in diagnostic accuracy. If technical, ethical and legal issues are addressed, it could be incorporated into practice as a useful adjunct.

“WhatsApp, Teacher?” - Student Perspectives on Teacher-Student WhatsApp Interactions in Secondary Schools

Aim/Purpose: In this paper, we analyze the phenomenon of “classroom WhatsApp groups”, in which a teacher and students from a particular classroom interact with one another, while specifically focusing on the student perspective of these interactions. Background: The instant messaging application WhatsApp enables quick, interactive multimedia communication in closed groups, as well as one-on-one interactions between selected group members. Yet, very little is known about the extent, nature, and purposes of these practices, the limitations and affordances, the type of discourse and conflicts that develop in these spaces, and the extent to which it affects teacher-student interactions outside of WhatsApp (e.g., the social climate in class, the teacher’s status, teacher-student and student-student relations), especially from the students’ perspective. Methodology: Our methodology combines questionnaires, personal interviews, and focus groups with Israeli secondary school students (N = 88). Contribution: The present study adds to the expanding body of empirical research on social media use in educational settings by specifically focusing on a heretofore underexposed aspect, namely, secondary school student-teacher communication in the popular instant messaging application WhatsApp. We report on findings from the student perspective and discuss the advantages and limitations of this form of communication sphere, and on the social functions of the different classroom WhatsApp groups in secondary school students’ everyday life. Findings: The combined findings reveal that classroom WhatsApp groups have become a central channel of communication for school-related topics. It is used primarily for organizational purposes (sending and receiving updates and managing learning activities), as well as a means for teachers to enforce discipline. Students mentioned many advantages of WhatsApp communication, such as easy access, the ability to create communities, the ability to safeguard personal privacy, and the communication format (written, mediated, personal, or group). However, they also recognized limitations (i.e., communication overload) and challenged teacher ability to monitor and affect student interactions in social media, even when they are present in these WhatsApp classroom groups. Finally, we report on the role of parallel, sans-teacher WhatsApp groups, which are characterized as back stage discourse arenas that accompany the front stage offline classroom activities and the “official” classroom WhatsApp group. Recommendations for Practitioners: The combined findings of this study indicate how WhatsApp-based, joint teacher-student groups can serve a variety of educational purposes, namely, organizational, instructional, and educational-disciplinary. In addition, and in spite of teachers concerns, students are aware of the challenges inherent to the use of WhatsApp for communication with their teachers. Some of the main characteristics that prevent teachers from using other ubiquitous digital communication media, such as Facebook or Twitter, are not relevant when it comes to WhatsApp. Both teachers and students view WhatsApp as a favored channel of communication because of the low exposure to personal information and minimal invasion of privacy. Future Research: The qualitative methodology of this paper limits the ability to generalize the current findings to other contexts and population groups. Future research should preferably explore the generalizability of our findings to larger sections of teenage populations. It should also explore similarities and differences with other age groups. Finally, the present study was set in a particular country (Israel). Local norms of cellphone use and of appropriate teacher-student interaction, as well as locally developed media domestication patterns, may differ from country to country and/or from one cultural group to another. Future research should then include and compare the current findings with data from different countries and cultures in order to complete the picture.

Socialized policy administration

With the rapid development of mobile applications and online social networks, users often encounter a frustrating challenge to set privacy and security policies (i.e., permission requests) of various applications correctly. For instance, in an Android system, it is hard for users, even programmers, to identify malicious permission requests (policies) when they install a third-party application. To simplify the task of policy management, in this paper, we propose a novel policy administration method where the policy settings from users' friends will be used as a key recommendation to guide policy administration, and the security of friends' privacy will be protected. We propose to let a user invite his or her friends to help with policy setting in applications, and we call such a method socialized policy administration (SPA for short). We designed two types of SPA: basic SPA and composite SPA. Both types of SPA are equipped with a privacy preserving mechanism that enables users' friends to help users without leaking the friends' preferences. In our prototype based on Telegram, i.e., one of the most popular instant messaging applications, we utilize partially homomorphic encryption cryptosystems to implement our framework. Based on the performance evaluation, SPA is able to configure almost all types of policies of current popular Android applications with a modest performance overhead.

Mobile-assisted Language Learning Using WeChat Instant Messaging

China has a long-standing problem in second-language education, that is, the lack of communicative learning opportunities. This study attempts to solve the problem by introducing mobile-assisted language learning with WeChat instant messaging. WeChat is one of the most popular instant messaging applications in China. It offers five advantages for education: multi-functionality, individuality, accessibility, interactivity, and affordability. 50 students participated in a one-semester program. They were divided into two groups. One group learned English with the assistance of mobile applications (WeChat group), and the other learned English without assistance (Control group). A pre-test and a post-test were given, and the scores were analyzed. The results showed that students in the WeChat group significantly improved in English proficiency. The results suggest that mobile-assisted language learning helps to create language immersion, which effectively motivates the learners further. Therefore, mobile-assisted language learning is promising in English learning for college students.

Analysis of the key influencing factors of haze information dissemination behavior and motivation in WeChat

PurposeWith the development of mobile internet, instant messaging (IM) applications have gradually changed the way to receive information and communicate with friends. Considered as one of the important social media platforms, WeChat is the most popular IM application in China. This paper aims to examine the influence factors of haze information’s dissemination in WeChat.Design/methodology/approachBased on the theory of information dissemination and the theory of motivation, three information dissemination elements including information source, information and information receiver were proposed in the structural equation model. The model was tested using data collected from both online and offline questionnaire survey results. The data analysis from 170 valid responses confirmed the path effects that emotional motivation, social motivation of information receiver and the credibility of information source positively influenced WeChat users’ re-transmission intention and information emotional tendency negatively influenced their re-transmission intention.FindingsThe research model revealed these key factors that affected dissemination of haze information in WeChat. More importantly, it provided a decision-making framework to release the public emergency information in WeChat. Hence, the paper made an important contribution to practical guidance to relevant decision makers.Originality/valueThis study contributes to the literature on examining factors affecting the haze information transmission in WeChat. A framework was proposed to describe the information dissemination process which comprises information source, information and information receiver.

The social networking investigation of metadata of forensic artifacts of a typical WeChat session under Windows

AbstractDigital forensics‐related investigations have been a relative competitive research area by the computer specialists or law enforcement agency officers to confirm or refuse the assumptions with respect to the cybercrime scenes. WeChat is one of the most popular instant messaging application programs in contemporary age. While applying WeChat, there is precious information deposited in the computing devices that could be supportive evidences in a court of law. Hence, this research is focusing on the application of WeChat on the desktop PC under Windows 7 by means of the acquisition of volatile digital evidences in order to provide supporting information for some cybercrime cases. This paper provides generic investigation methods conducting forensic sound analysis on WeChat from the aspect of data volatility of the computing devices. By means of the proposed methodologies, the digital forensics specialists should be able to gather critical digital traces to confirm the previous actions of a WeChat user or even to reconstruct the crime scene to prove the suspect is guilty or innocent in a court of law. Copyright © 2017 John Wiley & Sons, Ltd.

Assessing network energy consumption of mobile applications

Continuous growth in the energy consumption of mobile networks has become a major concern for mobile carriers. Since current mobile networks are dominated by mobile data traffic generated by over-the-top mobile applications, it is crucial for mobile carriers to understand how much network energy is used to deliver these applications. Here, we use real network and application measurements to comprehensively analyze the energy consumption of 12 common mobile applications by breaking down their total energy consumption into data and signaling energy components. The results provide insights into the different proportions of data and signaling energy (due to LTE signaling) for different mobile applications. They show that the energy consumption of a mobile application can vary at different base station cell sites due to different ratios of throughput to physical resource block utilization. We estimate the total monthly energy consumption of all 4G users of a major mobile carrier using conventional mobile services, such as voice and the short messaging service, and two over-the-top applications, i.e. a popular instant messaging application in China and an online video application. The results show that signaling energy consumption may become a major concern for mobile carriers, and that this issue will be exacerbated as the usage of over-the-top applications continues to grow. Energy assessment of mobile applications provides valuable information to enable mobile carriers to improve the energy efficiency of their networks. An energy assessment tool that enables real-time network and service energy monitoring will also assist in developing energy-efficient network policies for diverse applications with different energy consumption profiles. Furthermore, given our signaling energy consumption findings for over-the-top applications, there may be benefits for mobile operators to introduce a surcharge for signaling traffic to mitigate the high signaling energy consumption of some over-the-top mobile applications.

Network and device forensic analysis of Android social-messaging applications

In this research we forensically acquire and analyze the device-stored data and network traffic of 20 popular instant messaging applications for Android. We were able to reconstruct some or the entire message content from 16 of the 20 applications tested, which reflects poorly on the security and privacy measures employed by these applications but may be construed positively for evidence collection purposes by digital forensic practitioners. This work shows which features of these instant messaging applications leave evidentiary traces allowing for suspect data to be reconstructed or partially reconstructed, and whether network forensics or device forensics permits the reconstruction of that activity. We show that in most cases we were able to reconstruct or intercept data such as: passwords, screenshots taken by applications, pictures, videos, audio sent, messages sent, sketches, profile pictures and more.