More precisely, the approach we propose is based on the analysis of some characteristics and patterns present in the traffic generated during typical WhatsApp sessions. In this paper we focus on the WhatsApp application and propose an approach based on the side-channel analysis to detect some actions performed by WhatsApp users, such as the starting or rejecting of a call, the joining or leaving from/to a chat group, etc. On the other hand, due to their security properties, those applications are widely used also by cybercriminals. Therefore, it is practically impossible to break the protection of messages exchanged by such applications and find the content of such messages. Indeed, in the WhatsApp application as well as in the other IM applications, the communication between the various entities takes place in a protected manner. Given the multiple operational scenarios where they are involved in, to prevent the violation of communication by malicious users, IM applications typically ensure security in terms of confidentiality, integrity and availability. In the state of the art, the most popular instant messaging application in the world is definitely WhatsApp. In this way, users can be available everywhere and at any time. These applications have a positive impact in social relations, allowing a real-time communication that is simple and immediate. Nowadays, billions of people use Instant Messaging (IM) applications (called apps for short) to communicate, e.g., WhatsApp, Telegram, etc. A prototype has been built to evaluate side-channel effects on four popular scenarios.
These streams are used for training and evaluating a machine learning classifier to determine its performance of private system activity prediction. The framework operates by monitoring two data streams the first being the stream of side-channel cues, and the second being the stream of private system activity. This paper introduces a framework for adversary modeling and feedback generation on what the adversary may learn from the various side-channel information sources.
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Furthermore, no portable method exists for distributing side-channel test cases. Research has revealed side-channels formed by everything from CPU acoustic noise, to encrypted network traffic patterns, to computer monitor ambient light. What is missing, however, is the consideration of implicit information flows, or side-channels. Machine-learning based intrusion detection systems are also available and are commonly deployed in production environments. The state of research on direct attacks, such as exploiting memory safety or shell input errors is well established and a rich set of testing tools are available for these types of attacks. Based on these three vulnerable design patterns we present a set of tools for detecting these types of side-channel information leaks given a training set of captured encrypted network traffic sessions.Ĭomputer and network security is an ever important field of study as information processed by these systems is of ever increasing value. In this paper, we discuss three design patterns that often result in side-channel information leaks along with three real-world websites which posses these vulnerabilities. Such type of information leak is unique in the sense that while certain web security vulnerabilities such as SQL injections have been well researched and thus there are known design patterns to avoid and penetration testing tools based on detecting known-to-be insecure design patterns, the state of research for the types of information leaks described in this paper still lags behind. The design of SSL/TLS, however, merely provides data stream encryption and authentication properties which often leads to the incorrect conclusion that by simply wrapping an unencrypted HTTP connection to a server with SSL/TLS, user privacy and web application behaviour integrity are guaranteed. Since its creation, SSL/TLS has been the go-to solution for securing unencrypted web protocols-most commonly HTTP.
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