Should Australian courts give more witnesses the right to Skype?

Millions of people use Skype, a common form of social media that permitspeople to talk to each other over the internet. Courts in Australia havepermitted witnesses in at least a few instances to testify by Skype to date. Thisarticle examines whether Australian courts should permit witnesses to testifyby Skype more often. The article considers using videoconferencing, asopposed to Skype, and security issues associated with Skype. It alsoconsiders the impact that Skype may have upon considering witness credibility.Ultimately, it argues that Australian judicial officers may want to considerpermitting witnesses to testify by Skype if testifying by videoconference is notpossible, on a case by case basis. The authors believe that this is the firstscholarly article in Australia to focus on the issue of witnesses testifying bySkype

Challenges in using cryptography - End-user and developer perspectives

"Encryption is hard for everyone" is a prominent result of the security and privacy research to date. Email users struggle to encrypt their email, and institutions fail to roll out secure communication via email. Messaging users fail to understand through which most secure channel to send their most sensitive messages, and developers struggle with implementing cryptography securely. To better understand how to support actors along the pipeline of developing, implementing, deploying, and using cryptography effectively, I leverage the human factor to understand their challenges and needs, as well as opportunities for support. To support research in better understanding developers, I created a tool to remotely conduct developer studies, specifically with the goal of better understanding the implementation of cryptography. The tool was successfully used for several published developers studies. To understand the institutional rollout of cryptography, I analyzed the email history of the past 27 years at Leibniz University Hannover and measured the usage of email encryption, finding that email encryption and signing is hardly used even in an institution with its own certificate authority. Furthermore, the usage of multiple email clients posed a significant challenge for users when using S/MIME and PGP. To better understand and support end users, I conducted several studies with different text disclosures, icons, and animations to find out if users can be convinced to communicate via their secure messengers instead of switching to insecure alternatives. I found that users notice texts and animations, but their security perception did not change much between texts and visuals, as long as any information about encryption is shown. In this dissertation, I investigated how to support researchers in conducting research with developers; I established that usability is one of the major factors in allowing developers to implement the functions of cryptographic libraries securely; I conducted the first large scale analysis of encrypted email, finding that, again, usability challenges can hamper adoption; finally, I established that the encryption of a channel can be effectively communicated to end users. In order to roll out secure use of cryptography to the masses, adoption needs to be usable on many levels. Developers need to be able to securely implement cryptography, and user communication needs to be either encrypted by default, and users need to be able to easily understand which communication' encryption protects them from whom. I hope that, with this dissertation, I show that, with supporting humans along the pipeline of cryptography, better security can be achieved for all

Are You Really Muted?: A Privacy Analysis of Mute Buttons in Video Conferencing Apps

In the post-pandemic era, video conferencing apps (VCAs) have converted previously private spaces — bedrooms, living rooms, and kitchens — into semi-public extensions of the office. And for the most part, users have accepted these apps in their personal space, without much thought about the permission models that govern the use of their personal data during meetings. While access to a device’s video camera is carefully controlled, little has been done to ensure the same level of privacy for accessing the microphone. In this work, we ask the question: what happens to the microphone data when a user clicks the mute button in a VCA? We first conduct a user study to analyze users\u27 understanding of the permission model of the mute button. Then, using runtime binary analysis tools, we trace raw audio in many popular VCAs as it traverses the app from the audio driver to the network. We find fragmented policies for dealing with microphone data among VCAs — some continuously monitor the microphone input during mute, and others do so periodically. One app transmits statistics of the audio to its telemetry servers while the app is muted. Using network traffic that we intercept en route to the telemetry server, we implement a proof-of-concept background activity classifier and demonstrate the feasibility of inferring the ongoing background activity during a meeting — cooking, cleaning, typing, etc. We achieved 81.9% macro accuracy on identifying six common background activities using intercepted outgoing telemetry packets when a user is muted

Side-Channel VoIP Profiling Attack against Customer Service Automated Phone System

In many VoIP systems, Voice Activity Detection (VAD) is often used on VoIP traffic to suppress packets of silence in order to reduce the bandwidth consumption of phone calls. Unfortunately, although VoIP traffic is fully encrypted and secured, traffic analysis of this suppression can reveal identifying information about calls made to customer service automated phone systems. Because different customer service phone systems have distinct, but fixed (pre-recorded) automated voice messages sent to customers, VAD silence suppression used in VoIP will enable an eavesdropper to profile and identify these automated voice messages. In this paper, we will use a popular enterprise VoIP system (Cisco CallManager), running the default Session Initiation Protocol (SIP) protocol, to demonstrate that an attacker can reliably use the silence suppression to profile calls to such VoIP systems. Our real-world experiments demonstrate that this side-channel profiling attack can be used to accurately identify not only what customer service phone number a customer calls, but also what following options are subsequently chosen by the caller in the phone conversation.Comment: 6 pages, 12 figures. Published in IEEE Global Communications Conference (GLOBECOM), 202

OnionBots: Subverting Privacy Infrastructure for Cyber Attacks

Over the last decade botnets survived by adopting a sequence of increasingly sophisticated strategies to evade detection and take overs, and to monetize their infrastructure. At the same time, the success of privacy infrastructures such as Tor opened the door to illegal activities, including botnets, ransomware, and a marketplace for drugs and contraband. We contend that the next waves of botnets will extensively subvert privacy infrastructure and cryptographic mechanisms. In this work we propose to preemptively investigate the design and mitigation of such botnets. We first, introduce OnionBots, what we believe will be the next generation of resilient, stealthy botnets. OnionBots use privacy infrastructures for cyber attacks by completely decoupling their operation from the infected host IP address and by carrying traffic that does not leak information about its source, destination, and nature. Such bots live symbiotically within the privacy infrastructures to evade detection, measurement, scale estimation, observation, and in general all IP-based current mitigation techniques. Furthermore, we show that with an adequate self-healing network maintenance scheme, that is simple to implement, OnionBots achieve a low diameter and a low degree and are robust to partitioning under node deletions. We developed a mitigation technique, called SOAP, that neutralizes the nodes of the basic OnionBots. We also outline and discuss a set of techniques that can enable subsequent waves of Super OnionBots. In light of the potential of such botnets, we believe that the research community should proactively develop detection and mitigation methods to thwart OnionBots, potentially making adjustments to privacy infrastructure.Comment: 12 pages, 8 figure

Your WiFi Is Leaking: Inferring Private User Information Despite Encryption

This thesis describes how wireless networks can inadvertently leak and broadcast users' personal information despite the correct use of encryption. Users would likely assume that their activities (for example, the program or app they are using) and personal information (including age, religion, sexuality and gender) would remain confidential when using an encrypted network. However, we demonstrate how the analysis of encrypted traffic patterns can allow an observer to infer potentially sensitive data remotely, passively, undetectably, and without any network credentials. Without the ability to read encrypted WiFi traffic directly, the limited side-channel data available is processed. Following an investigation to determine what information is available and how it can be represented, it was determined that the comparison of various permutations of timing and frame size information is sufficient to distinguish specific user activities. The construction of classifiers via machine learning (Random Forests) utilising this side-channel information represented as histograms allows for the detection of user activity despite WiFi encryption. Studies showed that Skype voice traffic could be identified despite being interleaved with other activities. A subsequent study then demonstrated that mobile apps could be individually detected and, concerningly, used to infer potentially sensitive information about users due to their personalised nature. Furthermore, a full prototype system is developed and used to demonstrate that this analysis can be performed in real-time using low-cost commodity hardware in real-world scenarios. Avenues for improvement and the limitations of this approach are identified, and potential applications for this work are considered. Strategies to prevent these leaks are discussed and the effort required for an observer to present a practical privacy threat to the everyday WiFi user is examined

Help, my Signal has bad Device! Breaking the Signal Messenger’s Post-CompromiseSecurity through a Malicious Device

In response to ongoing discussions about data usage by companies and governments, and its implications for privacy, there is a growing demand for secure communication techniques. While during their advent, most messenger apps focused on features rather than security, this has changed in the recent years: Since then, many have adapted end-to-end encryption as a standard feature. One of the most popular solutions is the Signal messenger, which aims to guarantee forward secrecy (i.e. security of previous communications in case of leakage of long-term secrets) and future secrecy (i.e. security of future communications in case of leakage of short-term secrets). If every user uses exactly one device, it is known that Signal achieves forward secrecy and even post-compromise security (i.e. security of future communications in case of leakage of long-term secrets). But the Signal protocol also allows for the use of multiple devices via the Sesame protocol. This multi-device setting is typically ignored in the security analysis of Signal. In this work, we discuss the security of the Signal messenger in this multi-device setting. We show that the current implementation of the device registration allows an attacker to register an own, malicious device, which gives them unrestricted access to all future communication of their victim, and even allows full impersonation. This directly shows that the current Signal implementation does not guarantee post-compromise security. We discuss several countermeasures, both simple ones aiming to increase detectability of our attack, as well as a broader approach that seeks to solve the root issue, namely the weak device registration flow