Signatures of Viber Security Traffic

Viber is one of the widely used mobile chat application which has over 606 million users on its platform. Since the recent release of Viber 6.0 in March/April 2016 and its further updates, Viber provides end-to-end encryption based on Open Whisper Signal security architecture. With proprietary communication protocol scattered on distributed cluster of servers in different countries and secure cryptographic primitives, Viber offers a difficult paradigm of traffic analysis. In this paper, we present a novel methodology of identification of Viber traffic over the network and established a model which can classify its services of audio and audio/video calls, message chats including text and voice chats, group messages and file/media sharing. Absolute detection of both parties of Viber voice and video calls is also demonstrated in our work. Our findings on Viber traffic signatures are applicable to most recent version of Viber 6.2.2 for android and iOS devices

Forensics study of IMO call and chat app.

Smart phones often leave behind a wealth of information that can be used as an evidence during an investigation. There are thus many smart phone applications that employ encryption to store and/or transmit data, and this can add a layer of complexity for an investigator. IMO is a popular application which employs encryption for both call and chat activities. This paper explores important artifacts from both the device and from the network traffic. This was generated for both Android and iOS platforms. The novel aspect of the work is the extensive analysis of encrypted network traffic generated by IMO. Along with this the paper defines a new method of using a firewall to explore the obscured options of connectivity, and in a way which is independent of the protocol used by the IMO client and server. Our results outline that we can correctly detect IMO traffic flows and classify different events of its chat and call related activities. We have also compared IMO network traffic of Android and iOS platforms to report the subtle differences. The results are valid for IMO 9.8.00 on Android and 7.0.55 on iOS

Sensing multimedia contexts on mobile devices

We use various multimedia applications on smart devices to consume multimedia content, to communicate with our peers, and to broadcast our events live. This paper investigates the utilization of different media input/output devices, e.g., camera, microphone, and speaker, by different types of multimedia applications, and introduces the notion of multimedia context. Our measurements lead to a sensing algorithm called MediaSense, which senses the states of multiple I/O devices and identifies eleven multimedia contexts of a mobile device in real time. The algorithm distinguishes stored content playback from streaming, live broadcasting from local recording, and conversational multimedia sessions from GSM/VoLTE calls on mobile devices.Peer reviewe